University of Southern California Dissertations and Theses

Intelligent building skins: Parametric-based algorithm for kinetic facades design and daylighting performance integration

(USC Thesis Other)

Intelligent building skins: Parametric-based algorithm for kinetic facades design and daylighting performance integration

PDF

Download a page range

Download transcript

Copy asset link

Request this asset

Transcript (if available)

Content

INTELLIGENT BUILDING SKINS:
PARAMETRIC-BASED ALGORITHM FOR KINETIC FACADES DESIGN AND DAYLIGHTING
PERFORMANCE INTEGRATION

by

Mohamed Mansour El Sheikh

A Thesis Presented to the
FACULTY OF THE USC SCHOOL OF ARCHITECTURE
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF BUILDING SCIENCE

May 2011

Copyright 2011 Mohamed Mansour El Sheikh
ii

ACKNOWLEDGMENTS
I would like to express my deep and sincere gratitude to Karen Kensek. Her wide knowledge and
her logical way of thinking have been of great value to me from the very early stage. Her
understanding, encouraging and personal guidance has provided a good basis for this thesis. She
has always provided me with the materials required for the success of my studies, as well as
introducing me to new ideas and technology. Karen, you have been a great and generous
mentor, whom I hope to get another chance to work with in the future.

I gratefully acknowledge Dr. David Gerber for his advice, supervision, and crucial contributions,
which have made him a backbone of this thesis. His involvement and originality have triggered
and nourished my intellectual maturity, which I will benefit from for a long time to come. With
his enthusiasm and inspiration, and his ability to explain things clearly and simply, he
contributed to the success of this thesis. David, I am grateful in every possible way and hope to
keep up our collaboration in the future.

My special acknowledgements go to the person who inspired me to explore this field, Nathan
Miller. He introduced me to parametric design in one of his classes, in which I had the chance to
discover my passion about it. Nathan’s great work in parametric design has influenced my
approach to problem solving, especially in my thesis work. I have also benefited from his
continuous advice, guidance, and critical comments. I am indebted to him more than he knows.

iii

I warmly thank Professor Marc Schiler, a role model and gracious person, for his valuable advice
and friendly help. His interesting explorations and extensive discussions around my work have
been very helpful for this study. I am proud to record that I had the opportunity to work with
such an exceptionally experienced person. Though he is not an official thesis advisor, he served
on my thesis with true passion, sincerity, and kindness. Marc, words fail to express my
appreciation to you.

I owe a special acknowledgment to Greg Otto, who was a gracious mentor at the early stages of
this thesis. I am thankful that in the midst of all his activities, he provided me with the time and
guidance to properly structure my study. His critical feedback in the beginning was crucial to the
success of this study. Greg, you have been and will always be an exceptional mentor to me.

I would like to thank Professor Doug Noble for his support and guidance. He supervised my
previous research work and has always been a good person for advice. He has always supported
me in pursuing my research on building skins, and has provided me with all of the resources
required for my success. Doug, thanks for providing me with the good foundation necessary for
the success of this study; it has been pleasure working with you.

My parents, brother, and sisters deserve special mention for their inseparable support and
prayers. My father, Mansour El Sheikh, is the person who set the foundation for my learning
character, showing me the joy of intellectual pursuit ever since I was a child. My mother is the
one who sincerely raised me with her caring and gentle love, and has always been a strong
iv

supporter of my life path. Rana, Ahmed, and Salma, thanks for being supportive, loving, and
caring siblings.

Finally, I would like to express my gratitude to everyone who supported me in bringing this
thesis to light.

v

TABLE OF CONTENTS
ACKNOWLEDGMENTS ............................................................................................................ ii
LIST OF TABLES ................................................................................................................... viii
LIST OF FIGURES .................................................................................................................... ix
ABSTRACT ........................................................................................................................... xvi
CHAPTER 1 INTRODUCTION ....................................................................................................1
1.1. BACKGROUND ........................................................................................................................ 2
1.2. RESEARCH STATEMENT............................................................................................................ 12
1.3. GOALS AND OBJECTIVE ............................................................................................................ 14
1.3.1 ILLUMINANCE ................................................................................................................ 15
1.3.2 LUMINOUS DISTRIBUTION ................................................................................................ 15
1.3.3 LIGHT PENETRATION ....................................................................................................... 16
1.4. STRUCTURE AND METHOD ....................................................................................................... 16
1.4.1 INVESTIGATION .............................................................................................................. 17
1.4.2 PROCESSING LOGIC......................................................................................................... 18
1.4.3 DOCUMENTATION .......................................................................................................... 20
1.4.4 SIMULATION METHOD .................................................................................................... 21
1.5. CONCLUSION ........................................................................................................................ 24
CHAPTER 2 INTELLIGENT SKINS ............................................................................................. 26
2.1. OVERVIEW ........................................................................................................................... 27
2.2. INTELLIGENCE IN ARCHITECTURE ................................................................................................ 27
2.3. SKIN AS ENVIRONMENTAL FILTER ............................................................................................... 34
2.4. INTELLIGENT KINETICS AND DAYLIGHTING PERFORMANCE ................................................................ 39
2.5. TYPOLOGY OF KINETICS IN ARCHITECTURE .................................................................................... 42
2.6. HUMAN INTERACTION AND CONTROLS ....................................................................................... 47
2.7. MODELING INTELLIGENT KINETICS ............................................................................................. 48
CHAPTER 3 DYNAMIC DAYLIGHTING ..................................................................................... 52
3.1. INTRODUCTION ..................................................................................................................... 53
3.2. DAYLIGHT REWARDS .............................................................................................................. 55
3.2.1 HUMAN HEALTH AND PRODUCTIVITY ................................................................................... 55
3.2.2 INDIVIDUAL OUTCOMES ................................................................................................... 57
3.2.3 ENERGY USAGE ............................................................................................................. 59
3.3. DAYLIGHT FUNDAMENTALS ..................................................................................................... 61
3.3.1 SOURCE OF LIGHT ........................................................................................................... 61
3.3.2 PROPERTIES OF DAYLIGHT ................................................................................................ 63
3.3.3 FORM AND ORIENTATION ................................................................................................. 65
3.3.4 INTERNAL FACTORS IMPACT .............................................................................................. 67
vi

3.4. PERFORMANCE INDICATORS ..................................................................................................... 68
3.4.1 PREVIOUS WORK ........................................................................................................... 68
3.4.2 ILLUMINANCE ................................................................................................................ 70
3.4.3 LUMINOUS DISTRIBUTION ................................................................................................ 71
3.4.4 DEPTH OF LIGHT PENETRATION ......................................................................................... 73
3.4.5 EVALUATION CRITERIA ..................................................................................................... 74
3.5. DAYLIGHT-DEFLECTION TECHNIQUE ........................................................................................... 76
3.5.1 EVOLUTION OF LIGHT DEFLECTION ..................................................................................... 76
3.5.2 CONCEPT OF DAYLIGHT DEFLECTION ................................................................................... 77
3.5.3 DAYLIGHT HARVESTING USING LIGHT-DEFLECTORS ................................................................ 77
CHAPTER 4 INTELLIGENT SKIN DESIGN TOOL ......................................................................... 82
4.1. INTRODUCTION ..................................................................................................................... 83
4.2. CONCEPTUAL IDEA ................................................................................................................. 85
4.3. DESIGN TOOL DOCUMENTATION ............................................................................................... 89
4.3.1 SKIN SYSTEM ................................................................................................................ 89
4.3.2 DIVA COMPONENT ........................................................................................................ 91
4.3.3 ILLUMINANCE VALUES ..................................................................................................... 91
4.3.4 EVALUATION AND PERFORMANCE CRITERIA.......................................................................... 92
CHAPTER 5 DAYLIGHTING SIMULATION METHOD ............................................................... 104
5.1. OVERVIEW ......................................................................................................................... 105
5.2. SIMULATION CONDITIONS ..................................................................................................... 106
5.2.1 LOCATION, DATE & TIME ............................................................................................... 106
5.2.2 SPACE DIMENSIONS & MATERIALS ................................................................................... 107
5.2.3 RADIANCE SETTINGS ..................................................................................................... 109
5.3. SIMULATION METHOD .......................................................................................................... 111
5.4. UNDERSTANDING SIMULATION RESULTS ................................................................................... 119
5.5. VISUALIZATION OF SIMULATED SCHEMES .................................................................................. 122
CHAPTER 6 SIMULATION ANALYSIS..................................................................................... 124
6.1. DATA ANALYSIS METHOD ...................................................................................................... 126
6.1.1 DIAGRAMS AND CHARTS ................................................................................................ 126
6.1.2 KINETIC SCENARIO COMPILATION METHOD ......................................................................... 128
6.2. SELECT CASE ANALYSIS UNDER CLEAR SKY ................................................................................... 129
6.2.1 LIGHTSHELF AND HORIZONTAL LOUVERS............................................................................. 130
6.2.2 SHADING CONFIGURATION 35: AND 55: ........................................................................... 134
6.2.3 COMBINED CONFIGURATION 24: AND 156: ...................................................................... 135
6.2.4 COMBINED CONFIGURATION 26: AND 163: ...................................................................... 138
6.3. OVERCAST SKY CONDITION DISCUSSION ................................................................................... 140
6.4. KINETIC SCENARIO FOR ACTUATION PATTERN ............................................................................ 144
6.5. ANALYSIS CONCLUSION ......................................................................................................... 146
CHAPTER 7 CONCLUSION AND FUTURE WORK .................................................................... 149
7.1. CONCLUSION ...................................................................................................................... 150
7.1.1 SCOPE AND LIMITATIONS ........................................................................................ 155
vii

7.2. FUTURE WORK ................................................................................................................ 158
7.2.1 OCCUPANTS’ BEHAVIOR .......................................................................................... 158
7.2.2 GLARE REMEDIATION .............................................................................................. 160
7.2.3 HEAT GAIN AND THERMAL COMFORT ...................................................................... 160
7.2.4 SKIN GEOMETRY ...................................................................................................... 160
7.2.5 SURROUNDING URBAN CONTEXT ............................................................................ 161
7.3. SUMMARY ......................................................................................................................... 162
BIBLIOGRAPHY ................................................................................................................... 165
APPENDIX A: ILLUMINANCE NODE POINT RESULTS ............................................................. 172
APPENDIX B: RESULTS FOR CHART PLOTTING ...................................................................... 250

viii

LIST OF TABLES
TABLE 6-1: PERFORMANCE PERCENTAGE TABLE .................................................................................................. 147
TABLE 10-1: GLAZING ONLY CHART VALUES ....................................................................................................... 250
TABLE 10-2: LIGHTSHELF CHART VALUES........................................................................................................... 250
TABLE 10-3: HORIZONTAL LOUVERS CHART VALUES............................................................................................. 250
TABLE 10-4: SHADING 0° AND 45° CHART VALUES.............................................................................................. 251
TABLE 10-5: SHADING 10° AND 45° CHART VALUES ............................................................................................ 251
TABLE 10-6: SHADING 35° AND 55° CHART VALUES ............................................................................................ 251
TABLE 10-1: HARVESTING 145° AND 160° CHART VALUES ................................................................................... 251
TABLE 10-7: HARVESTING 153° AND 153° CHART VALUES ................................................................................... 252
TABLE 10-8: COMBINED 24° AND 156° CHART VALUES ....................................................................................... 252
TABLE 10-9: COMBINED 26° AND 163° CHART VALUES ....................................................................................... 252
TABLE 10-10: COMBINED 55° AND 168° CHART VALUES ..................................................................................... 252
TABLE 10-11: COMBINED 10° AND 170° CHART VALUES ..................................................................................... 253
TABLE 10-12: TOP-LOWER SPLIT 162° AND 28° CHART VALUES............................................................................. 253

ix

LIST OF FIGURES
FIGURE 1-1: CURTAIN WALLS ............................................................................................................................ 2
FIGURE 1-2: ELECTRICITY USE IN USA.................................................................................................................. 4
FIGURE 1-3: DAYLIT ZONE ................................................................................................................................. 6
FIGURE 1-4: FLARE FAÇAD ................................................................................................................................ 8
FIGURE 1-5: INDEPENDENT TILT ANGLES ............................................................................................................. 14
FIGURE 1-6: SYSTEM LOGIC ............................................................................................................................. 19
FIGURE 1-7: INDEPENDENT ACTUATION .............................................................................................................. 23
FIGURE 2-1: INTELLIGENT BUILDING FEATURES AND HUMAN INTELLIGENCE ................................................................ 28
FIGURE 2-2: INTELLIGENT PROCESS ................................................................................................................... 32
FIGURE 2-3: QUALITIES OF BUILDING INTELLIGENCE ............................................................................................... 32
FIGURE 2-4: EGYPTIAN MASHRABIYYA ............................................................................................................... 35
FIGURE 2-5: BRISE SOLEIL ............................................................................................................................... 37
FIGURE 2-6: KINETIC TYPOLOGY ....................................................................................................................... 43
FIGURE 2-6: CONTINUED ................................................................................................................................ 44
FIGURE 2-6: CONTINUED ................................................................................................................................ 45
FIGURE 2-6: CONTINUED ................................................................................................................................ 46
FIGURE 3 -1: ELECTROMAGNETIC SPECTRUM ....................................................................................................... 54
FIGURE 3-2: QUALITIES OF LIGHTING ................................................................................................................. 58
FIGURE 3-3: INDIVIDUAL OUTCOME .................................................................................................................. 59
FIGURE 3-4: ENERGY USAGE IN OFFICES ............................................................................................................. 61
FIGURE 3-5: ATMOSPHERE LAYERS .................................................................................................................... 62
FIGURE 3-6: DAYLIGHTING CHARACTERISTICS ...................................................................................................... 64
FIGURE 3-7: BUILDING FORM AND ORIENTATION .................................................................................................. 66
x

FIGURE 3-8: SUN PATH DIAGRAM ..................................................................................................................... 67
FIGURE 3-9: IES LIGHTING HANDBOOK ILLUMINATION .......................................................................................... 71
FIGURE 3-10: ILLUMINANCE DISTRIBUTION ......................................................................................................... 72
FIGURE 3-11: ILLUMINANCE-DEPTH RELATIONSHIP ............................................................................................... 73
FIGURE 3-12: INTERPRETATION OF INDICATORS ................................................................................................... 75
FIGURE 3-13: SIMPLE LIGHT DEFLECTION ........................................................................................................... 76
FIGURE 3-14: COMBINED CONFIGURATION ......................................................................................................... 79
FIGURE 3-15: ANGLES CALCULATION ................................................................................................................. 80
FIGURE 4-1: SYSTEM LOGIC ............................................................................................................................. 86
FIGURE 4-2: SPACE DIMENSION ........................................................................................................................ 87
FIGURE 4-3: LOUVERS DEFINITION..................................................................................................................... 88
FIGURE 4-4: INDEPENDENT SPLIT SYSTEM ............................................................................................................ 88
FIGURE 4-5: INDEPENDENT LOUVER ACTUATION ................................................................................................... 90
FIGURE 4-6: ILLUMINANCE SCALE ..................................................................................................................... 90
FIGURE 4-7: DIVA COMPONENT SECTION .......................................................................................................... 93
FIGURE 4-8: VALUE ADJUSTMENT ..................................................................................................................... 94
FIGURE 4-9: ILLUMINANCE VALUES ADJUSTMENT .................................................................................................. 97
FIGURE 4-10: CALCULATION POINTS EXTRACTION ................................................................................................. 98
FIGURE 4-11: ILLUMINANCE EVALUATION RESULT ................................................................................................. 99
FIGURE 4-12: LUMINOUS DISTRIBUTION EVALUATION CRITERIA .............................................................................. 100
FIGURE 4-13: GENETIC ALGORITHM FITNESS VALUE ............................................................................................. 101
FIGURE 5-1: SPACE DIMENSIONS .................................................................................................................... 107
FIGURE 5-2: MODELED SPACE ....................................................................................................................... 108
FIGURE 5-3: MATERIALS SELECTION................................................................................................................. 109
xi

FIGURE 5-4: SIMULATION PARAMETERS ............................................................................................................ 110
FIGURE 5-5: RADIANCE PARAMETERS ............................................................................................................... 111
FIGURE 5-6: REDIRECTED ANGLE FOR SINGLE CONFIGURATION ............................................................................... 112
FIGURE 5-7: REDIRECTED ANGLE FOR COMBINED CONFIGURATION .......................................................................... 113
FIGURE 5-8: REVISED ANGLES DIAGRAM ........................................................................................................... 114
FIGURE 5-9: LOUVER CONFIGURATION ............................................................................................................. 114
FIGURE 5-10: SEMI-CLOSED CONDITION ........................................................................................................... 115
FIGURE 5-11: OPTIMAL TILT ANGLES ............................................................................................................... 116
FIGURE 5-12: SIMULATION CASES................................................................................................................... 118
FIGURE 5-13: VISUAL PRESENTATION OF TILT ANGLES .......................................................................................... 119
FIGURE 5-14: ILLUMINANCE NODES RESULTS SAMPLE .......................................................................................... 120
FIGURE 5-15: FRONT VIEW OF SIMULATED SCHEMES ........................................................................................... 122
FIGURE 5-16: PERSPECTIVE VIEW OF SIMULATED SCHEMES ................................................................................... 123
FIGURE 6-1: INDEPENDENT TILT-ANGLE SYSTEM ................................................................................................. 125
FIGURE 6-2: CHART VALUES SELECTION SAMPLE ................................................................................................. 127
FIGURE 6-3: CHART SAMPLE .......................................................................................................................... 128
FIGURE 6-4: LIGHTSHELF ILLUMINANCE RESULTS ................................................................................................. 131
FIGURE 6-5: ZOOMED LIGHTSHELF ILLUMINANCE RESULTS..................................................................................... 131
FIGURE 6-6: ZOOMED HORIZONTAL LOUVERS ILLUMINANCE RESULTS ....................................................................... 133
FIGURE 6-7: HORIZONTAL LOUVERS ILLUMINANCE RESULTS ................................................................................... 133
FIGURE 6-8: SHADING CONFIGURATION ILLUMINANCE RESULTS .............................................................................. 135
FIGURE 6-9: COMBINED CONFIGURATION ILLUMINANCE RESULTS ........................................................................... 136
FIGURE 6-10: ZOOMED COMBINED CONFIGURATION ILLUMINANCE RESULTS ............................................................. 137
FIGURE 6-11: COMBINED CONFIGURATION ILLUMINANCE RESULTS ......................................................................... 139
xii

FIGURE 6-12: ZOOMED COMBINED CONFIGURATION ILLUMINANCE RESULTS ............................................................. 140
FIGURE 6-13: HARVESTING CONFIGURATION ILLUMINANCE RESULTS ....................................................................... 141
FIGURE 6-14: COMBINED CONFIGURATION ILLUMINANCE RESULTS ......................................................................... 142
FIGURE 6-15: COMBINED-SPLIT CONFIGURATION ILLUMINANCE RESULTS .................................................................. 144
FIGURE 6-16: COMPILED SCENARIO FOR CLEAR SKY ............................................................................................. 145
FIGURE 6-17: COMPILED SCENARIO FOR OVERCAST SKY ........................................................................................ 146
FIGURE 7-1: STATIC VERSUS KINETIC ................................................................................................................ 155
FIGURE 7-2: ARCHITECTURE FOR HUMAN BEHAVIOR DETECTION SYSTEM .................................................................. 159
FIGURE 7-3: ALGORITHM LOGIC FOR FUTURE WORK ............................................................................................ 162
FIGURE 9-1: GLAZING ONLY RESULTS ............................................................................................................... 172
FIGURE 9-2: GLAZING ONLY RESULTS ............................................................................................................... 173
FIGURE 9-3: GLAZING ONLY RESULTS ............................................................................................................... 174
FIGURE 9-4: GLAZING ONLY RESULTS ............................................................................................................... 175
FIGURE 9-5: GLAZING ONLY RESULTS ............................................................................................................... 176
FIGURE 9-6: GLAZING ONLY RESULTS ............................................................................................................... 177
FIGURE 9-7: LIGHTSHELF RESULTS ................................................................................................................... 178
FIGURE 9-8: LIGHTSHELF RESULTS ................................................................................................................... 179
FIGURE 9-9: LIGHTSHELF RESULTS ................................................................................................................... 180
FIGURE 9-10: LIGHTSHELF RESULTS ................................................................................................................. 181
FIGURE 9-11: LIGHTSHELF RESULTS ................................................................................................................. 182
FIGURE 9-12: LIGHTSHELF RESULTS ................................................................................................................. 183
FIGURE 9-13: HORIZONTAL LOUVERS RESULTS ................................................................................................... 184
FIGURE 9-14: HORIZONTAL LOUVERS RESULTS ................................................................................................... 185
FIGURE 9-15: HORIZONTAL LOUVERS RESULTS ................................................................................................... 186
xiii

FIGURE 9-16: HORIZONTAL LOUVERS RESULTS ................................................................................................... 187
FIGURE 9-17: HORIZONTAL LOUVERS RESULTS ................................................................................................... 188
FIGURE 9-18: HORIZONTAL LOUVERS RESULTS ................................................................................................... 189
FIGURE 9-19: SHADING 0° AND 45° RESULTS .................................................................................................... 190
FIGURE 9-20: SHADING 0° AND 45° RESULTS .................................................................................................... 191
FIGURE 9-21: SHADING 0° AND 45° RESULTS .................................................................................................... 192
FIGURE 9-22: SHADING 0° AND 45° RESULTS .................................................................................................... 193
FIGURE 9-23: SHADING 0° AND 45° RESULTS .................................................................................................... 194
FIGURE 9-24: SHADING 0° AND 45° RESULTS .................................................................................................... 195
FIGURE 9-25: SHADING 10° AND 45° RESULTS .................................................................................................. 196
FIGURE 9-26: SHADING 10° AND 45° RESULTS .................................................................................................. 197
FIGURE 9-27: SHADING 10° AND 45° RESULTS .................................................................................................. 198
FIGURE 9-28: SHADING 10° AND 45° RESULTS .................................................................................................. 199
FIGURE 9-29: SHADING 10° AND 45° RESULTS .................................................................................................. 200
FIGURE 9-30: SHADING 10° AND 45° RESULTS .................................................................................................. 201
FIGURE 9-31: SHADING 35° AND 55° RESULTS .................................................................................................. 202
FIGURE 9-32: SHADING 35° AND 55° RESULTS .................................................................................................. 203
FIGURE 9-33: SHADING 35° AND 55° RESULTS .................................................................................................. 204
FIGURE 9-34: SHADING 35° AND 55° RESULTS .................................................................................................. 205
FIGURE 9-35: SHADING 35° AND 55° RESULTS .................................................................................................. 206
FIGURE 9-36: SHADING 35° AND 55° RESULTS .................................................................................................. 207
FIGURE 9-37: HARVESTING 145° AND 160° RESULTS .......................................................................................... 208
FIGURE 9-38: HARVESTING 145° AND 160° RESULTS .......................................................................................... 209
FIGURE 9-39: HARVESTING 145° AND 160° RESULTS .......................................................................................... 210
xiv

FIGURE 9-40: HARVESTING 145° AND 160° RESULTS .......................................................................................... 211
FIGURE 9-41: HARVESTING 145° AND 160° RESULTS .......................................................................................... 212
FIGURE 9-42: HARVESTING 145° AND 160° RESULTS .......................................................................................... 213
FIGURE 9-43: HARVESTING 153° AND 153° RESULTS .......................................................................................... 214
FIGURE 9-44: HARVESTING 153° AND 153° RESULTS .......................................................................................... 215
FIGURE 9-45: HARVESTING 153° AND 153° RESULTS .......................................................................................... 216
FIGURE 9-46: HARVESTING 153° AND 153° RESULTS .......................................................................................... 217
FIGURE 9-47: HARVESTING 153° AND 153° RESULTS .......................................................................................... 218
FIGURE 9-48: HARVESTING 153° AND 153° RESULTS .......................................................................................... 219
FIGURE 9-48: COMBINED 10° AND 170° RESULTS .............................................................................................. 220
FIGURE 9-49: COMBINED 10° AND 170° RESULTS .............................................................................................. 221
FIGURE 9-50: COMBINED 10° AND 170° RESULTS .............................................................................................. 222
FIGURE 9-51: COMBINED 10° AND 170° RESULTS .............................................................................................. 223
FIGURE 9-52: COMBINED 10° AND 170° RESULTS .............................................................................................. 224
FIGURE 9-53: COMBINED 10° AND 170° RESULTS .............................................................................................. 225
FIGURE 9-54: COMBINED 24° AND 156° RESULTS .............................................................................................. 226
FIGURE 9-55: COMBINED 24° AND 156° RESULTS .............................................................................................. 227
FIGURE 9-56: COMBINED 24° AND 156° RESULTS .............................................................................................. 228
FIGURE 9-57: COMBINED 24° AND 156° RESULTS .............................................................................................. 229
FIGURE 9-58: COMBINED 24° AND 156° RESULTS .............................................................................................. 230
FIGURE 9-59: COMBINED 24° AND 156° RESULTS .............................................................................................. 231
FIGURE 9-60: COMBINED 26° AND 163° RESULTS .............................................................................................. 232
FIGURE 9-61: COMBINED 26° AND 163° RESULTS .............................................................................................. 233
FIGURE 9-62: COMBINED 26° AND 163° RESULTS .............................................................................................. 234
xv

FIGURE 9-63: COMBINED 26° AND 163° RESULTS .............................................................................................. 235
FIGURE 9-64: COMBINED 26° AND 163° RESULTS .............................................................................................. 236
FIGURE 9-65: COMBINED 26° AND 163° RESULTS .............................................................................................. 237
FIGURE 9-66: COMBINED 55° AND 168° RESULTS .............................................................................................. 238
FIGURE 9-67: COMBINED 55° AND 168° RESULTS .............................................................................................. 239
FIGURE 9-68: COMBINED 55° AND 168° RESULTS .............................................................................................. 240
FIGURE 9-69: COMBINED 55° AND 168° RESULTS .............................................................................................. 241
FIGURE 9-70: COMBINED 55° AND 168° RESULTS .............................................................................................. 242
FIGURE 9-71: COMBINED 55° AND 168° RESULTS .............................................................................................. 243
FIGURE 9-72: TOP-LOWER SPLIT 162° AND 28° RESULTS ..................................................................................... 244
FIGURE 9-73: TOP-LOWER SPLIT 162° AND 28° RESULTS ..................................................................................... 245
FIGURE 9-74: TOP-LOWER SPLIT 162° AND 28° RESULTS ..................................................................................... 246
FIGURE 9-75: TOP-LOWER SPLIT 162° AND 28° RESULTS ..................................................................................... 247
FIGURE 9-76: TOP-LOWER SPLIT 162° AND 28° RESULTS ..................................................................................... 248
FIGURE 9-77: TOP-LOWER SPLIT 162° AND 28° RESULTS ..................................................................................... 249

xvi

ABSTRACT
A high integration of design and research between architects, computational designers, and
consultants is important to achieve innovation and efficiency. Communicating to the designer
the importance of integrating performance-based approaches in the early design stage and their
impact on the design, may shift the logic of executing an architectural project. The integration of
daylighting into the design phase, through design tools and computation, results in the
improved performance of daylight harvesting and therefore tackles issues of human comfort
and energy efficiency. One example of performance-based integration is the design, simulation,
and validation of intelligent features in building skin design and its impact on daylighting
performance.

This thesis presents the design of an algorithm and parametric process developed in
Grasshopper, a plugin for Rhino 3D, using DIVA for daylighting simulation. The main objective of
the process and algorithm is to evaluate the performance of an intelligent façade, composed of
a series of kinetic louvers that actuate in response to dynamic daylighting, and the incorporation
of occupants’ preferences. Within the framework of this study, Grasshopper as a parametric
computational tool allows the integration of Rhino, the design space, and DIVA, the dynamic
daylighting tool, into a single process. The parametric tool extracts the designed geometry from
the modeling space and inputs it into the DIVA component to be tested for illumination
performance, luminous distribution, and daylight penetration depth inside an office space.

xvii

The thesis presents the initial experiment, in which the external skin actuates to optimize
daylight-deflection, maintaining a desirable luminous indoor environment. In the experiment,
the louvers rotate using the concept of independent tilt-angle, where every other louver has the
same tilt angle; they could be in a harvesting, shading, or a combined configuration. When skin
configuration changes, due to louver actuation, the algorithm detects the alteration and
instantly reflects it onto a calculation grid inside the space. This allows the designer to run
numerous iterations during the design stage and select the best possible one based on pre-
defined criteria.

A genetic algorithm has been incorporated into the definition to enable a search for the best
skin configuration at specific dates and times or under different sky conditions. The genetic
algorithm works on finding an optimal – although not necessarily the best – solution under
certain parameters and conditions. These parameters could range from users’ desired
illumination levels, to externally-reflected daylighting components. Changes in any of these
parameters trigger the system to run and find an optimal configuration for the skin to maintain
the desired luminous environment.

In this study, one actuation parameter and three performance indicators for daylighting are
defined. However, the proposed design tool is extensible; it is open to accepting additional
parameters and performance indicators, which makes it more complex for better performance
assessment. As a future development, the geometry of the skin panels will be considered as a
varying parameter.
1

1. CHAPTER 1 INTRODUCTION
This research presents an investigation into using intelligent light-deflection techniques to
optimize daylighting in office buildings. In this chapter, the background of the proposed topic,
the research argument, and the objectives of the study are presented, along with insight into
the methodology and intended deliverable
2

1.1. BACKGROUND

Figure 1-1: Curtain Walls – The left image shows one of the world’s first curtain walls in England, Oriel Chambers. The
right image shows a building by the same architect, which also has a curtain wall system, built two years later. At this
time, the amount of glazing used was large compared to adjacent buildings in the area. The architect’s goal was to
allow more daylight penetration into the space, as a means of energy-saving.
1

In 1864, Peter Ellis designed Oriel Chambers in Liverpool
2
, England, one of the world’s earliest
buildings with metal-framed glass curtain walls. Peter was considered a substantial user of glass
facades. His design intent was not aesthetic, but had more of an emphasis on energy efficiency.

1
“Curtain wall - Wikipedia, the free encyclopedia.”
2
“Liverpool Architecture.”
3

The penetration of daylighting deeply into the space, reducing lighting costs, was the main
objective for using curtain wall systems. As time passed, architects started incorporating more
glass into their facades, resulting in high levels of illumination inside the spaces. The use of
larger amounts of glazing became more common, which made architects worried about their
façade performance, and they started looking at the façade as an important filtering layer to the
indoor environment.

In the United States, lighting accounts for almost 20-25%
3
of total electrical energy use, and in
the commercial sector accounts for 37%
4
(Figure 1-2). Electric lighting also has an indirect effect
on cooling loads in spaces; as a rule of thumb, each unit of electric light requires an additional
one-half unit of electricity for space conditioning.
5
Improvements in the lighting design
profession increased the efficiency of lighting, by utilizing less electrical lighting and exploiting
the available natural light. The impact of using natural light is significant not only in terms of
energy usage, but also on employees’ productivity and health
6
.

Use of daylight is important not only in offices, but also in most architectural projects, including
retail spaces
7
. In a comparative study by Southern California Edison of a large multinational

3
Ander, Daylighting Performance and Design, 2.
4
Ibid.
5
Ibid.
6
Dasgupta, The Impact of Windows on Mood and Performance of Judgmental Tasks.
7
Ander, Daylighting Performance and Design, 31.
4

retail organization, an approximate 26%
8
increase in sales has been noticed in a store with
skylights, relative to similar stores with no skylights.

Figure 1-2: Electricity Use in USA – breakdown of electricity consumption in the commercial building sector.
9

The use of glass in office buildings has become important in the profession for transparency,
visual, and daylighting purposes. Although useful for allowing light into buildings, untreated
windows allow more daylight into a space than required, resulting in visual problems, such as
over-abundance of light in some areas, not enough in others, and glare. Typically, the daylight
depth in a room with untreated openings is about one-and-a-half times the distance from the
window head to the floor (Figure 1-3). A typical window head is at 2.20m, which results in a
3.30m room depth of daylight area. Using light deflection techniques, such as light shelves, can

8
Ibid.
9
Ibid., 2.
5

extend the ratio up to twice that height, resulting in a daylit zone of 4.40m depth.
10
This ratio
shows the limitation of daylight penetration, which entails the use of electric lighting in many
indoor spaces, leading to greater electrical energy consumption, and greater heat from light
fixtures, in addition to that from sunlight. This is still a major balancing act in window design:
daylight harvesting can help save energy through reduced use of electricity to run lights, but the
heat gain might be undesired. As mentioned previously, light shelves are a good solution for
deep light penetration up to twice the window header height
11
. The principle behind this idea is
light-deflection, in which light is bounced off the upper surface of a shelf and deflected deeply
into the back of the space. This technology opened new venues for the advancement of
daylighting performance inside spaces, especially in office buildings. Amongst the technologies
that allow better daylighting efficiency, redirecting light either into or out of a space is
commonly referred to as light-deflection technology
12
.

10
O’Connor, Tips for daylighting with windows: the integrated approach, 3.
11
Ibid.
12
“Thiele AG - Transparente Innovation.”
6

Figure 1-3: Daylit Zone – the figure on the left shows the depth of daylight in the case of an untreated window
opening, while the right figure shows the depth extended up to 2.0x using light shelves.

The introduction of light redirection technology had a significant impact on the performance of
facades in optimizing daylighting
13
. Light deflection devices have been proven to efficiently
increase the performance of daylighting in interior spaces, by redirecting light deep into the
spaces, minimizing the undesirable effects of direct sunlight and the use of electric lighting. Such
systems include light shelves, light tubes, venetian blinds, and anidolic systems. While these
techniques have the same objective - increasing the amount of daylight in interior spaces - they
are not suitable for every building. For example, using light tubes in a high-rise office building is
not an efficient approach, given the typical height of a high-rise tower.

Daylight problems are mostly treated as individual cases, in which system customization is
sometimes required. Such customization does not have to involve the major alteration of an
existing technology, but can be a minor addition that makes the system fit within the design

13
Koster, Dynamic Daylighting Architecture.
7

context. The system can be a passive daylight system that aims for better lighting inside spaces,
or a static or active system that possesses some dynamic capabilities. While passive systems
enhance performance, they lack the flexibility of adapting to changing outdoor conditions. For
example, fixed light shelves are optimized for specific ranges of dates and times, and are less
effective for the rest of the year or under different sky conditions. The ineffectiveness of light
shelves is due to the changing angle of the sun. Given the limitations of passive systems,
designers started adopting active control systems, which led to the introduction of kinetics
techniques in façade design.

Over the past few decades, architects have adopted kinetic systems in many glass façade
systems, for their interactive abilities - and not particularly for environmental purposes
14
. Having
movable devices on a building façade was a turning point in the profession. However, a façade
can be referred to as “interactive” without possessing kinetic capabilities. Media facades are
one type of interactive architecture, considered socially interactive due to their use of lights.
They are even sometimes considered to be sustainable, when they use solar energy as a
renewable energy source. Other types of interactive designs include wall prototypes designed
by some architects for social interaction purposes, in which a wall is able to respond to an
occupant’s motion. But these are also irrelevant to our current environmental needs, since they
only address artistic and aesthetic issues.

14
“FLARE-facade.”
8

Figure 1-4: Flare Façade – The modular façade is composed of metal flake elements that are controlled by a series of
pneumatic cylinders. These elements reflect sunlight in a way that casts a shadow on some of the faces of each
element, giving them darker colors. The façade does not optimize the energy performance of the indoor spaces, but
socially interacts with people outside the building and operates as an interactive piece of art.15

Architecture is currently experiencing a demand for smart, responsive-based designs, where the
occupants’ comfort level is achieved through means of perception, processing, and response.
Buildings are becoming more like high performance working robots/machines. Designers are
investigating the potential of making façade elements move in response to other stimuli,
whether human or natural. If we are to develop the field of architecture and performance-based
design, the profession needs to restructure traditional kinetic approaches to make use of

15
Ibid.
9

today’s technology, beyond conventional mechanisms and single-function design. Venetian
blind systems that are programmed to retract and close at certain times of the days are an
example of conventional kinetic techniques.
It is worth noting that a number of studies have demonstrated that the ability of an
employee to control to a degree the daylight and electric light directly around his or
her work area has led to even better production and morale. This is because, while
overall good lighting is important, individuals often have their own particular
preferences for brightness and the angle from which light hits their work area.
16

The statement above proclaims the need for individual control of daylight and electric light
around an employee’s working area. Given today’s technological advancement, individual
control can be provided in the form of an automated-smart system: a system which stores the
preferences of the occupants and acts accordingly to adjust the quality of the luminous
environment inside the space, without the need for manual human control.

Integrating intelligent features into the architecture of a building is a discourse taking place in
the architecture profession. Façades that possess intelligent capabilities are always referred to
as “Intelligent Skins”. In Chapter 2, the research presents a detailed explanation of intelligent
skins. Intelligent skins touch upon not only energy performance, but also the aesthetics of the
design. This combination is the focus of many interactive architecture designers, such as Michael
A. Fox and Chuck Hoberman, who try to use intelligence to transform façade panels whenever
needed in response to natural forces. The design of façades, an important element of
architecture, is influenced by changes in design trends, in terms of geometry and systems.

16
Ander, Daylighting Performance and Design, 28.
10

Many designers, some of whom are famous, are incorporating greater geometric complexity
into their designs. This approach to design has become more widely used for landmark and
iconic architecture. While complexity is also found in systems and structure, this aspect
addresses complexity in terms of form and geometry only. Designing a building with complex
geometry does not obviate the need to perform better in terms of energy-efficiency. An optimal
approach in this case is to combine arts and science to support whatever design path the
designer is undertaking, by providing advanced technological solutions. Though complex
geometry is not in the scope of this thesis, it could be approached using the same technique as a
regular façade: one way of integrating advanced technology is through the use of dynamic
techniques and capabilities.

Dynamic kinetics is a façade typology that integrates movable panels into one large system, the
envelope; it is an integral part of the whole building, acting independently. Within the scope of
this study, dynamic capabilities are applied to a secondary skin, where the building has two
external facades: one has the glazing, and the other is offset from the glazing by 1.00m and
consists of a series of horizontal louvers. The offset distance allows for accessibility and
maintenance activities to take place. While in this study the secondary skin is a series of kinetic
horizontal louvers, it can be found in different forms and configurations based on the project’s
objectives, whether performance-based or aesthetic.

11

In this case, the horizontal louvers function as light-deflection devices capable of adapting to
environmental changes. The use of light deflectors as a secondary skin on a building requires
proper rationalization of the system to match the desired geometry of the architecture. This
sometimes requires system customization using commonly-used technologies, developing a new
approach using established techniques. If we are to make a significant contribution to the field,
we have to commit to a new paradigm based on innovative development of the old.
Signal your intention. Commit to a new paradigm, rather than to an incremental
improvement of the old…. In this case, the intention is not to be slightly more efficient,
to improve on the old model, but to change the framework itself.
17

This statement by McDonogh and Braungart is inspiring for designers to look forward and
support today’s technology in the design profession. It reflects the need for an innovative
framework that adapts to current performance needs, rather than altering old techniques and
making them fit within a new context. Good practice should experience a fluidity of design and
scientific creativity flowing between architects and consultants, in order to achieve innovative
buildings. Rethinking the design program may be better than refining an old approach to fit the
target problem - the designer should make use of existing techniques and approaches to
develop a new, perhaps customized, technique for solving design and performance problems. It
is crucial for the development of our profession that designers learn from the past, explore the
current, and innovate for our changing environment and demands.

17
McDonough and Braungart, Cradle to Cradle, 182.
12

1.2. RESEARCH STATEMENT
Energy efficiency in buildings is influenced by the behavior of architectural spaces, part of which
can be attributed to daylighting. Daylighting performance is envelope-dominated. For example,
designing fully-glazed façades minimizes the efficiency of the envelope, despite allowing huge
amounts of daylight into a space. This does not necessarily result in good performance, since
illumination levels are not optimized to fall within the acceptable range. The problem is notably
experienced in south facing façades, which get the most solar exposure over the course of the
day.

Daylighting is a crucial asset for office design, but it is variable. Indoor spaces suffering
inadequate daylighting levels during daytime experience illumination levels (quantity) out of the
recommended range, and uneven distribution of daylighting (quality). They also suffer the need
for electric lighting to compensate for the limitation of daylighting depth into the space. The
design profession is currently undergoing technological advancement which will allow for better
daylight performance, targeting greater energy savings and reduced electricity consumption.
This will happen by bringing daylight deeper into the space, maintaining desired illumination
levels (quantity), and achieving even luminous distribution (quality).

The integration of light deflection techniques into an intelligent dynamic panel system allows
the enhancement of daylight harvesting, quantity and quality, inside south-facing spaces
enclosed by fully-glazed façades – Hypothesis

13

This hypothesis combines two different aspects of architecture that correlate: “intelligent skins”
and “daylight-deflection technology.” Within the context of this thesis, 1) intelligent skin is the
means through which 2) daylight-deflection is enhanced.

The statement focuses on the intelligent dynamic panel system and its impact on the quality and
quantity of daylighting in office spaces. Investigating this topic requires defining particular
specifications, among which are daylighting benchmarks and the qualities that distinguish an
intelligent envelope performance. In Chapter 3, an explanation of the qualities and
specifications of daylighting in office spaces is presented. These qualities vary, and include such
factors as sensors and controls, patterns of actuation, panel materials, panel geometry, targeted
number of inputs and outputs to and from the system, illumination levels (quantity), and
luminous distribution (quality). This study specifically investigates independent tilt angles and
panel geometry, and their impact on optimizing the quantity and quality of daylighting inside
office spaces. Independent tilt angles are an approach proposed in which every other louver has
the same tilt angle, either in shading or harvesting position.

14

Figure 1-5: Independent tilt angles – the shading and harvesting configurations of different panels actuate
independently in a secondary skin system.
1.3. GOALS AND OBJECTIVE
An optimal visual environment in office spaces, achieved through the use of daylight, is crucial
for employees’ comfort, productivity, and morale
18
. Visual comfort is addressed through many
factors, among which are light level (illuminance), luminous distribution, glare, light penetration
depth, and direct sunlight
19
. The research goal of this thesis is to design an intelligent dynamic
light-deflection system that provides daylight levels within a recommended range, even
distribution of daylight inside the space, and penetration of daylight deep into the space,
beyond today’s normal achievable depth (Figure 1-3). For a better understanding of the goals,
the studied parameters are further explained below.

18
Dasgupta, The Impact of Windows on Mood and Performance of Judgmental Tasks.
19
Schiler, Simplified Design of Building Lighting.
15

1.3.1 ILLUMINANCE
Different organizations, like the Illuminating Engineering Society (IES) and the National Research
Council of Canada (NCR), recommend different light levels for office spaces. The recommended
illumination level for an office space according to the Illuminating Engineering Society of North
America (IESNA), is 200 – 1500 lux, based on task.
20
The NRC Institute for Research in
Construction recommends a level of 400 – 500 lux for general office work.
21
In terms of daylight
factor, the recommended percentage is 2-5%. Maintaining a range of 200-1500 lux is the
objective of this study, taking into account that values less than 200 lux, and higher than the
recommended range, may be acceptable in some areas of the space, under certain conditions,
where no activity is assumed to take place.
1.3.2 LUMINOUS DISTRIBUTION
For a better visual environment, the IESNA recommends that, within the occupant’s field of
view, the ratio between the maximum and minimum illuminance should not exceed 1:10.
22

However, the NRC Institute for Research in Construction recommendation exceeds that of IES,
and goes up to 1:20
23
, providing an acceptable argument for this high contrast, like highlighting
certain objects on the working plane. Sometimes due to high contrast, the occupant perceives

20
IES North America, IESNA Lighting Handbook.
21
National Research Council Canada, “NRC Canada.”
22
IES North America, IESNA Lighting Handbook.
23
National Research Council Canada, “NRC Canada.”
16

parts of the space as dark which in reality have sufficient light levels. Maintaining a ratio of 1:10
prevents the false perception of light level inside spaces.
1.3.3 LIGHT PENETRATION
Untreated window openings allow light penetration one-and-a-half times the distance from the
floor to the window head. Incorporating light shelves extends the ratio up to twice the distance
(Figure 1-3). For example, a 2.20m window head height allows the penetration of daylight into
the space up to 3.30m, if using an untreated opening, and 4.40m if using a light shelf. Within the
context of this study, the goal is to exceed the 2x ratio – aiming for, at least, two-and-a-half
times the vertical distance.
1.4. STRUCTURE AND METHOD
This thesis develops a parametric tool for integrating daylighting performance into the process
of kinetic façade design. The tool is intended to be used by anyone - but especially designers -
involved in the design process and early-stage decisions. This idea, where innovative,
contemporary, exciting developments in façade design are tested for daylight optimization, is a
primary concern; it allows designers to find the potential of complex design forms to enhance
the indoor luminous environment.

The study focuses on investigating the effectiveness of light deflection by an intelligent
secondary skin layer in south-facing indoor spaces. In this study, parametric tools are used to
search façade configurations, by pre-defining some parameters denoting intelligence and
environmental changes (refer to Chapter 2 for intelligence parameters). The investigation of this
17

topic prior to executing computer simulations is crucial. To address the problem in an efficient
manner, the research has been divided into three main parts: investigation (literature study),
simulation and documentation, and analysis. Each of the three parts is explained in the section
below.
1.4.1 INVESTIGATION
This section provides a complete literature review of the investigated topic and its attributes.
Given the investigation of two related topics, 1) intelligent kinetic skins and 2) daylighting
performance, the investigation phase is split into two chapters. The literature review of both
topics addresses the definition of intelligence within the framework of the study, the use of
intelligent skins as light-deflectors, and the performance of daylight-deflection in enhancing the
quality and quantity of light in office spaces.

If we are to express the correlation between intelligent skins and daylight enhancement, some
points need to be covered in the literature study of the topic. These points directly relate to the
intelligence of a building skin and its ability to respond to environmental changes, specifically
sunlight. Providing a solid background to the following points builds a concrete foundation for
explaining daylight enhancement through the use of intelligent systems:
 What is intelligence?;
 The concept of intelligent responsive systems;
 Intelligence and environmental controls;
 Sensors, data exchange and feedback loops of intelligent systems.
18

While more points are covered further in Chapter 1, for the interest of the study, the first
portion of Chapter 2 covers the intersection of intelligent skins and daylight enhancement; it
explains possible techniques for using smart-kinetic systems to adjust daylight quality in indoor
spaces. Examples of similar systems and previous work are used to illustrate the positive
potential of using deflection techniques in the context of the current technological
advancements. The main points covered in Chapter 2 are:
 Benefits of daylighting;
 Daylight fundamentals;
 Performance indicators;
 Deflection techniques.
1.4.2 PROCESSING LOGIC
Within the framework, data processing depends not only on the simulation tool and its
calculation capabilities, but also on the pre-defined parameters, based on the desired
daylighting performance. The algorithmic components incorporated into the set of parameters
force data to flow in certain directions for evaluation purposes. Processing stops when an
optimal skin configuration is found by the genetic algorithm component (Figure 1-6).
19

Figure 1-6: System logic – the inputs to the system and the processing of data.

Grasshopper is responsible for setting the input parameters according to the desired luminous
environment conditions, and passing these to the daylighting simulation tool, DIVA/Radiance,
which then processes the data and sends the results back to Grasshopper for evaluation.
Grasshopper compares the results against the pre-defined performance criteria. If the results
are acceptable, Grasshopper provides the generated solution as the best possible under the
defined simulation conditions. If the results do not match the criteria, Grasshopper sets another
scenario for the skin and triggers the simulation tool to re-run a new scenario and generate a
new outcome - and so on, until an acceptable result is obtained (Figure 1-6).

20

The proposed data workflow depends on a solution-based factor, where processing is about
finding the optimal solution for a design problem. The system will run “all” possible solutions
and then pick the one resulting in the best daylighting performance inside the space. Panel tilt
angles are endless; thus, increments of 3.60 degrees will be set to restrict the possible set of
solutions to 100.

Different sun conditions are expected to affect the performance of daylighting in the space, and
simulating all times of the day is difficult due to time limitations. Consequently, data will be
recorded for the following times only: June 21
st
(summer solstice), December 21
st
(winter
solstice), and March 21
st
(equinox), each at 9:00am and 12:00pm, for clear sky and overcast sky.
At each of the mentioned times, the sun has different solar angle and/or a different direction.
1.4.3 DOCUMENTATION
The integration of four tools was necessary for the objective of this study: Rhino as a modeling
tool, Grasshopper as a parametric interface, DIVA 1.1 for daylight simulation, and the Galapagos
component as a genetic algorithm problem-solver. A simple example is developed to assess the
performance of the system in searching for an optimal solution for skin configurations.
Grasshopper is used to identify the input parameters and the evaluation criteria for daylighting
assessment, the DIVA component is used to simulate the process of daylighting, and Galapagos,
the genetic algorithm component, uses a single-numerical value as a fitness number in search of
optimal configuration.

21

The proposed tool is intended to be used by designers at the early stages of design, to integrate
daylighting performance into the process. Such integration could be into the design of interior
spaces, building envelopes, and/or orientations. In Chapter 4, the proposed design algorithm
used to determine the optimal configuration for a building skin for better daylighting
performance is documented. The algorithm is applied to a secondary skin configuration
composed of horizontal louvers split into two independent layers; every alternate louver has the
same tilt angle. The definition utilizes Radiance as the calculation engine for daylighting, and has
been divided into screenshots, which are shown in Chapter 4.
1.4.4 SIMULATION METHOD
As a parametric design tool, Grasshopper allows the creation of a kinetic system that can
respond to multiple inputs and outputs through the use of a genetic algorithm. In this tool,
intelligent features are expressed as parameters, mathematical functions and benchmarks
which make the intelligence of the system limited, but flexible enough for the system to
implement certain desired tasks for better daylighting performance. Given that DIVA is a
Rhino/Grasshopper plugin, it can easily be integrated into the intelligent part of the algorithmic
definition.

Technical difficulties were experienced using DIVA 1.1, the daylighting simulation tool, to extract
illuminance values and import them into Grasshopper. The difficulties experienced were due to
scripting issues in the component itself. This problem prevented node values from being passed
to the evaluation criteria for assessment.

22

Simulation is vital for this study, to illustrate the objective of the tool. Daylight simulation is a
commonly-used technique for predicting the quality of luminous environments, as well as
accounting for daylight variability during early design stages. This part provides a set of figures
and results generated through executing numerous simulation runs for different skin
configurations. Intelligence is defined in the parametric software in the form of preset
parameters/constraints, such as the occupants’ desired illuminance range. As previously
mentioned, Rhino, Grasshopper and the DIVA-plugin are the tools used for simulation in this
study. These three tools were designed to work together, and are capable of efficiently
exchanging data without the need for more interfaces, making the simulation clean, quick, and
reliable.

Modeling building intelligence in computer programs is new technology. Accordingly – within
the content of this thesis – intelligence is defined in terms of parameters that can be controlled
and referred to as “intelligent enough” to execute required tasks, like setting a parameter for
the desired illuminance range. These parameters work in a loop of multiple inputs and outputs
which can only be handled by parametric software.

In all simulation runs – as shown in Chapter 5 – a generic office space of 6.00m x 7.50m x 3.00m
is used. These dimensions remain fixed throughout the entire study. Though the tilt angle
changes, the space dimensions remain the same. The façade is assumed to be fully glazed, using
a curtain wall system. However, the elements of the curtain wall system are not taken into
account during simulation, in order to minimize calculation time. Initially, the simulation is run
23

for clear and overcast skies for three base test cases: glazing only, light shelf, and horizontal
louver system. This part shows the enhancement of daylight performance in relation to
changing façade/skin system. It also illustrates the ability of daylight deflection to affect the
indoor luminous environment quality.

Afterwards, an intelligent-dynamic system composed of a series of louvers arranged
horizontally, each actuating independently (Figure 1-7), is modeled using Rhino/Grasshopper.
This is considered to be the main case study of the thesis, on which design alterations will be
tested. The panels actuate independently, with the aid of some parametric algorithms. The
performance of the skin is shown inside the interior space in terms of quality and quantity of
daylighting - which requires establishing some measuring points in the space.

Figure 1-7: Independent actuation of secondary skin panels for better indoor luminous environment quality.

The modeled office space is divided into 36 calculation nodes spread over a grid set at 0.70m,
covering the entire surface area of the interior space. Assuming workers do not occupy the
entire floor area of an office space, the success of the system is based on achieving the desired
performance indicators – previously mentioned – for at least 75% of the office floor area.
24

Theoretically, the calculation grid is composed of points that read the lighting levels in different
positions inside the space. It is expected that readings will vary from one point to another. These
points act as the sensors in the simulation. Readings from each point are individually extracted
and passed on to set performance criteria for assessment.

A successful run should be able to achieve each of the three indicators for as close as possible to
75% of the entire surface area. Two types of indicators are experienced in this study: individual
node indicators, and group indicators. An individual indicator is the illuminance of each point.
This does not require evaluation of all points together to see if they fit within the range or not;
each node is evaluated independently. Group indicators represent the luminous distribution and
the depth of penetration. To evaluate both items, the system is required to look at the results as
a collection of nodes, and assess their performance.
1.5. CONCLUSION
Daylighting is a crucial asset in office spaces; it increases the productivity of workers, enhances
their morale, and maintains their health
24
. Despite its importance, sometimes designers do not
adequately account for daylight during the design phase, which subsequently requires the use of
more electric lighting. On the other hand, some architects, like Aalvar Aalto, Louis Kahn, and Le
Corbusier, addressed daylight through architecture, emphasizing its importance
25
. This thesis

24
Boubekri, Daylighting, Architecture and Health.
25
Schiler, Simplified Design of Building Lighting, xi.
25

explores one example, where innovative-contemporary developments in façade design are
considered for better daylighting performance in south-facing office spaces. The whole study is
dependent on daylight-deflection techniques to bring daylight levels to desired ranges inside a
generic office space. The deflection method is supported by some intelligent features to
enhance the operation of the façade system.

The methodology implemented in this study enables designers to account for the performance
of daylighting during the early design stage. It also allows them to explore numerous façade
designs and their impact on the quality of the indoor luminous environment. Moreover, the
technique integrates lighting analysis into the design/modeling tool, which eliminates the need
for more interfaces or model export/import procedures. The proposed algorithmic definition is
intended to provide flexibility by allowing the possibility of setting different performance
indicators – if necessary – based on different design problems.

In the following chapters, an elaboration of the investigated topic is presented, detailing the
argument, as well as the intersection of intelligence and daylight-deflection.

26

2. CHAPTER 2 INTELLIGENT SKINS
Applying intelligence to buildings in the form of intelligent façades, sensors, materials, or even
building systems, is a discourse taking place in the profession. In this chapter, background
research into intelligence in architecture, intelligent applications to buildings, and the human
interaction with building systems, is presented.
27

2.1. OVERVIEW
The word “intelligent” was first used at the beginning of the 1980s to describe buildings,
together with the American word “smart”
26
. Since then, building façades incorporating
intelligent features have come to be known as “intelligent building skin,” where the skin forms
the greater part of the intelligent system in the building. Describing a façade as an intelligent
element requires the presence of dynamic living capabilities, which enable interaction with
diurnal and seasonal changes, and human beings in the surrounding environmental context, in
order to achieve a reduction in the energy consumption inside indoor spaces. Intelligence is not
an equation of fixed variables; it is a process that is inspired by human intelligence and cognitive
capabilities. That being said, the definition of intelligence can be manipulated in different ways
according to the designers’ intentions and approaches. However, all definitions acknowledge
the influence of living organisms in terms of behavior and reasoning.
2.2. INTELLIGENCE IN ARCHITECTURE
Applying intelligence to buildings in the form of intelligent façades, sensors, materials, and even
building systems, is a discourse taking place in the profession. People are the establishing point
for intelligence in buildings. They are not passive organisms, but adapt to their surroundings
psychologically, physically and behaviorally, and often change their environments to be more
suitable for them. This is a complex interaction, informed by their senses and mediated by the
brain. The brain allows people to reason, perceive, and react to their environment, whether

26
Wigginton and Harris, Intelligent Skins.
28

tangible or non-tangible. Humans have always been the inspirational model for considering the
application of intelligence in buildings (Figure 2-1).

Figure 2-1: Intelligent Building Features and Human Intelligence – the aspects of human intelligence that profoundly
impacted the implementation of intelligent features in buildings
27
.

The concept of intelligence in architecture should be distinguished from integration or
automation in buildings. Intelligence is often referred to by people when objects or elements
function using automated features and control systems
28
. They sometimes use the term
“intelligent” to describe automated domestic functions at home through a computerized
system, like turning on/off the lights, or opening and closing doors. Since these actions are
manually triggered by the occupant, they should not be considered intelligent, due to the
absence of a system reasoning process (Figure 2-2). In this research, intelligence is based on a

27
Clements-Croome, Intelligent Buildings, 44.
28
Fox and Kemp, Interactive Architecture.
29

different archetype, which relates to human characteristics and the energy-performance of
indoor spaces in buildings, exploring the interaction between the building skin and occupants to
provide a better luminous indoor environment. Clements-Croome provides a relevant definition
of the term ‘Intelligence’:
Intelligence is not an attribute, but a complex hierarchy of information processing
skills, underlying an adaptive equilibrium between individuals and their environment.
29

Moreover, the Intelligent Building Institute (IBI) presented one of the first definitions of
intelligence in buildings:
An intelligent building is one which provides a productive and cost effective
environment through the optimization of its four basic elements – systems, structure,
services, management and the inter-relationship between them. Intelligent buildings
help building owners, property managers, and occupants realize their goals in the
areas of cost, comfort, convenience, safety, long-term flexibility, and marketability.
There is no intelligence threshold past which a building "passes" or "fails". Optimal
building intelligence is the matching of solutions to occupant needs. The only
characteristic that all intelligent buildings have in common is a structured design to
accommodate change in a convenient, cost-effective manner.
30

Given the scope of this thesis, the above statement presents a valid argument. Assessing the
performance of intelligent systems in buildings does not have an absolute benchmark; it is a
relative evaluation process that depends on the occupants’ needs and preferences, and the
function of the space. Accordingly, the presence of an occupants-model during simulation is
necessary for the success of the study. However, simulating such a model in the study would
add another layer of investigation, and this has not been included, due to time limitations.
Therefore, a compromise has to be made for the simple modeling of intelligent features. The

29
Clements-Croome, Intelligent Buildings, 5.
30
“iBuilding Website.”
30

occupants-model is represented in the form of a set of parameters that resemble the normal
working employee preferences. These parameters are simplified factors of the desired luminous
environment in general office spaces. Refer to Chapter 3 for a detailed explanation of the desired
quality of luminous environments.

People involved in the design and construction process can have different views on the
performance of an space; the occupant, however, is the one who will truly judge the success of
the luminous environment inside the space. Since it is hard to incorporate occupants in the
simulation model, the designed algorithmic definition allows for multiple variable inputs to
represent changing user needs. This is considered to be an appropriate simplified method for
mimicking changing occupants’ preferences.

Despite their importance, some buildings tend to ignore occupants’ behaviors and preferences,
and incorporate responsive features that work based only on environmental changes and
climatic conditions. An intelligent building can be looked at as a human body, where occupants
represent internal organs, and the envelope resembles the external organ, the skin. Human
intelligence is geared to protecting the internal organs, and providing an optimal living
environment for them to perform well. Likewise are the building occupants. They need to have a
good working environment for better productivity. The occupants’ behaviors and preferences
are driving factors for building operation - though they differ according to the climate and
function of the space. An intelligent building should be able to achieve optimal performance by
implementing the following processes:
31

 Create a relationship between occupants’ behavior and indoor space conditions.
 Automatically adapt and respond to environmental changes and user requirements.
 Expedite cost-effective alteration to occupants’ behavior changes, including changing
tasks.
The term “intelligent” has been applied recently to objects that resemble human beings’
behavior. Sometimes called ‘smart’, they tend to process (perception) data like human beings
and react (action) based on a process of analysis (thinking). Most of these intelligent features
have the same intent, but are different in scale and function. In cars, for example, intelligent
driving systems enable cars to automatically detect when cars ahead stop unexpectedly and
respond by applying brakes. This feature provides security and comfort to the driver - who is the
occupant in this case. The smart driving system evaluates the situation and reacts in a way
similar to the way the driver would have reacted. It mimics the expected driver’s behavior for
better performance and comfort.

Intelligent buildings should operate in a similar way. They are expected to possess some
cognitive features that allow them to perceive data, analyze it, and respond with an appropriate
reaction (Figure 2-2). This process can be applied to different aspects of the building operation,
among which is the environmental performance of spaces, emphasizing this study’s objective.
Embedding the behavior of users, and the changing environmental conditions, into an
intelligent-cognitive system may drastically enhance the energy performance of spaces.
32

Figure 2-2: Intelligent Process – the data flow process that, generically, identifies a façade as an intelligent feature.

In his book “Intelligent Buildings”, Brian Atkin identifies three aspects that should be found in
intelligent buildings:
31

 Buildings should ‘know’ what is happening inside and immediately outside;
 Buildings should ‘decide’ the most efficient way of providing a convenient, comfortable,
productive environment for the occupants;
 Buildings should ‘respond’ quickly based on occupants’ preferences.

Figure 2-3: Qualities of building intelligence – the main qualities of building intelligence, as defined by Mervi
Himanen.
32

31
Atkin, Intelligent Buildings.
32
Clements-Croome, Intelligent Buildings, 45.
Perception
(Sensors)
Analysis and
Reasoning
(BMS)
Response
(Dynamics)
33

Mervi Himanen divided the qualities of building intelligence into five main categories (Figure 2-
3): connectivity, self-recognition, kinaesthetics, logic, and spatiality. Clements-Croome defined
each category as follows:
33

 Connectivity relates to how the occupant connects to the system. This could be through
speech recognition, automatic control, motion detection, etc.
 Self-recognition provides self-consciousness capabilities for the building to detect its
current status and interaction level.
 Spatiality describes the spatial expression of the architecture.
 Kinaesthetics relates to the kinetic capabilities of the system, as well as adaptive
technology.
 Building Logic expresses the embedded sensors layer that monitors occupants’ behavior
and reports back to the building management system.
These five factors are taken into consideration within this study with a different approach than
that of real-life, due to the limitations of simulation tools, time constraints, and the difficulty of
modeling occupants’ behavior inside the space. “Connectivity” and “Building Logic” are
expressed in the form of a pre-defined set of parameters which reflect the occupants’
requirements for better productivity and an enhanced luminous environment. These
parameters can be changed at any time prior to running the simulation, which means that the
system can be tested for different user preferences, if any. “Self-recognition” and “Spatiality”
are expressed in the form of space-sensors/calculation points. Whenever the calculation grids

33
Ibid.
34

go out of the performance criteria range, the system should be able to detect this and work to
bring at least 75% of the points into the desired range. Although the method used in this study
for simulating one factor of human intelligence inside the space - the varying occupants’ desired
range of illumination - is not close to reality, it is the best model that can be achieved within the
time limitation.

As previously mentioned, intelligent-responsive skins should account for environmental changes
and occupants’ comfort for better performance. But does this mean that no building skins
without intelligent features perform well in terms of energy-efficiency? The answer is “no”.
Environmental approaches have been implemented in the profession before the advanced
intelligent technologies discussed here were invented. Architects have long integrated passive
approaches into their designs to enhance the performance of the spaces. We, as architects,
should acknowledge the importance of previously-designed buildings in providing a concrete
foundation for implementing today’s technologies in the intelligent-responsive building field.
The following section provides two examples of passive environmental controls in architecture
which influenced the invention of today’s techniques.
2.3. SKIN AS ENVIRONMENTAL FILTER
Given that a façade is the exterior shell of a building, it should act as an environmental filter
fine-tuned towards energy-reduction, daylighting, ventilation, and excellent quality of indoor
spaces. Throughout history, façade treatments for better performance have been applied to
protect indoor spaces from adverse climatic conditions. Many different examples illustrate the
façade as an important filter layer, among which are: Mashrabiyya, brise-soleil, the trombe
35

wall, high-mass walls, and super-insulated envelopes. Despite the functional differences
between the techniques, they all aim for better indoor environments, as well as protecting
occupants from unfavorable environmental changes. Mashrabiyya and brise-soleil are
especially good examples, which attempt to deal with both thermal and daylighting
considerations.

Figure 2-4: Egyptian Mashrabiyya – the Mashrabiyya is a great example of an environmental façade treatment for
optimizing daylighting, minimizing glare, and driving cool air into the space. The image on the left34 shows the indoor
effect of this element, while the image on the right35 shows the exterior look of this ornamental Egyptian icon.

The Mashrabiyya is a great example of a passive environmental façade application that controls
daylighting and natural ventilation inside a space. Hassan Fathy, the Egyptian leader in

34
“Sennari House In Cairo: Home to Napoleon’s Scholars.”
35
“Mashrabiyya - Wikipedia, the free encyclopedia.”
36

environmental and vernacular architecture, describes the Mashrabiyya as a mean of control of
severe glare conditions in interior spaces (Figure 2-4). In old times, Egyptians were leaders in
inventing environmental performance-based prototypes. They adopted the Mashrabiyya in
most Egyptian Islamic houses for environmental purposes and comfort of human occupants; in
addition, it also reflected local cultural values
36
. While it was a successful approach, widely
adopted in Egyptian houses, people are now moving towards implementing more advanced
technologies in buildings. The Mashrabiyya works inefficiently when windows are open – as
seen in the left image of Figure 2-4 – which is necessary for having an outdoor view. The
question arises: if it performs so well, why don’t we continue using it? Simply put, the answer is:
we cannot use this ornamentation piece on all façades. It is a product of the Egyptian culture
that was suitable for buildings at that time - the middle ages up to the mid-twentieth century. As
seen in Figure 2-4, the design of this element was customized for specific cultures; it is not a
generic-modern element that can be fitted into any design - quite apart from its transparency
limitation, as previously mentioned.

The Western world has had different approaches for environmental filters. Brise soleil, a
pattern of permanent concrete shading elements, is a technique that was popularized by Le
Corbusier in buildings with large surface areas of glazing
37
. This performance-based pattern
allows the penetration of low-angle sun in winter, for passive heating, and the blockage of high-
angle sun during the summer (Figure 2-5). The Gustavo Capanema Palace, designed by Le

36
Kenzari and Elsheshtawy, “The Ambiguous Veil.”
37
Melendo, Lainez, and Verdejo, “Nineteen Thirties Architecture for Tropical Countries.”
37

Corbusier in 1935, is a famous example that shows the efficient use of brise soleil in Rio de
Janeiro. In this example, the brise soleil works well with high-angle sun, however the system is
limited in its ability to deal with low-angle sun that may penetrate the space, resulting in an
uncomfortable luminous environment. Today’s demands for complexity in form and design led
to the emergence of another aesthetical limitation of using traditional brise soleil, however this
system is found in some old buildings by famous architects like Richard Meier, who used brise
soleil in a canopy at the Getty Center in Los Angeles, California.

Figure 2-5: Brise Soleil – a modular pattern of fixed concrete shading elements to protect occupants during summer
months from heat gain and to allow passive heating during winter months.
38

Architects have long been able to achieve successful environmental control techniques for
“better” performing spaces. They used all available resources – from cultural inspirations to

38
“Brise soleil - Wikipedia, the free encyclopedia.”
38

local materials – to achieve better performance. Though these techniques worked well at the
time, they do not fit within the needs and preferences of today’s occupants, and the design
complexity. In other words, though the performance of these systems hasn’t changed, other
variables have changed which demand better energy performance inside spaces. My hypothesis
is that the fast pace of ongoing technological advancement in building systems has made the
occupants demand more energy-efficient environments - especially in office spaces - for better
productivity and lower running costs. To fulfill the users’ needs, designers have explored
advanced techniques on building façades, which represent a significant part of any architectural
project, besides acting as the medium between the indoor and outdoor environments
39
.
However, designers have also developed new systems inspired by historical innovations.

The main case study of this thesis investigates the performance of a brise soleil-like system that
possesses dynamic-responsive capabilities. The system uses the same solar shading technique of
the brise soleil, and adds to it light-deflection techniques for enhanced luminous environment in
office spaces. While the proposed system is inspired by historical innovations, it exploits today’s
advanced technology by integrating intelligent features into the skin system, which allow
independent tilt angle control for each panel.

Building façades are the main manipulators of environmental parameters. If designed efficiently,
they should be capable of maintaining an adequate comfort level, by adjusting the impact of
natural forces on interior spaces. The envelope’s functions include daylighting optimization and

39
Wigginton and Harris, Intelligent Skins.
39

glare protection, acoustical barrier, natural ventilation, humidity adjustment, wind protection,
visual contact, security and safety, and energy performance. For a building to overcome
unexpected and changing adverse climatic conditions, it has to integrate advanced cognitive
technology that allows the skin to think, and respond appropriately. For example, a space can
detect changes in human behavior - typing or reading, for example - and optimize daylighting
levels accordingly.
2.4. INTELLIGENT KINETICS AND DAYLIGHTING PERFORMANCE
The skin is one of the dynamic regulatory organs that allow the body to survive through a wide
range of unfavorable conditions. The intelligent behavior of the skin enables adaptation to the
surrounding environment by means of physical processes, such as perception, reasoning and
action, which allow the human body to overcome adverse environmental conditions.
Analogously, the building envelope acts as the medium through which the interior spaces of a
building interact with the surrounding environment. Buildings are subjected to a wide range of
unfavorable and varying environmental conditions, which requires the envelope to possess
intelligent‐dynamic capabilities, and automatically respond to environmental changes and user
demands.

Smart-kinetic façades are one type of intelligent skin. This is a technological advancement that
addresses today’s dynamic, constantly-changing activities in order to optimize indoor conditions
to meet the user’s needs. Smart-kinetics is the flexible adaptability of building façades to
respond to changing environmental conditions, taking into account human interaction and
40

behavior. They should be capable of handling the rapidly varying patterns of human interaction
in a space.

Smart-kinetics, part of comprehensive intelligent building systems, can be designed as
performance-based elements that relate to interaction and responsiveness. They can be part of
a primary system, or perform independently as a secondary skin layer. Operable windows are
one example of a primary system application, where the windows on the main envelope open
and close according to need, while a series of external louvers is a secondary skin application,
distinct from the main skin and operating independently. Michael A. Fox, an interactive
architecture designer, refers to this type as “dynamic” kinetic typology, where the kinetic
elements are part of a bigger system but can act independently.
40
Besides the potential of
providing better energy-performance, the use of a secondary skin system allows for more
creative designs and skin geometries. That is why a secondary skin system is used in this study.
Despite the advanced technology in most buildings, only a few efficiently exploit the presence of
an intelligent platform in the building management system, because cognitive-smart
applications in buildings haven’t developed enough yet for real-life applications. Though some
buildings are referred to as “intelligent architecture,” they can only execute specific tasks, and
are unable to act the same way human beings do.

Responsive skin systems first emerged in the form of kinetic technology that works according to
a pre-defined set of orders, with no room for processing or reasoning in response to unforeseen

40
Fox and Kemp, Interactive Architecture.
41

variables. Natural forces are the variables in this case. One example is a dynamic louver system
that follows the motion of the sun to prevent direct light inside the space. Such a system is not
considered intelligent, as the sun’s path is already known and can be programmed into the
system, making the louvers move in the same pattern every day. A designer can provide the
equations for calculating the sun’s position, and the panels will actuate accordingly to face the
calculated position. This can be referred to as a single-input system, where only one pre-set
input is dealt with. Other types of input that can directly affect the occupants in terms of
comfort level and productivity include: enhancement of daylight, maximization of daylight,
protection from the sun, insulation, natural ventilation, heat collection, heat rejection, sound
attenuation, generation of electricity, and exploitation of pressure differentials.
41

Making a building respond to and account for human needs in its daily operation is one problem
where user input is critical. This is obvious when it comes to lighting in office spaces. People
usually assume that daylighting will be available when they go to work during daytime. In fact,
employees rarely question the quality of the luminous environment inside the space. With
today’s changing needs, designers have provided occupants with manual control of lighting for
better satisfaction, reflecting changing tasks and activities inside the same space. The question
is: what happens if we design an intelligent daylight harvesting system that automatically
recognizes occupants’ demands and reacts accordingly? Will it enhance daylighting performance
and satisfy users? My answer would be: yes, it will. This does not mean that good performance
is achieved only through intelligent kinetics, however; it is one of many approaches.

41
Wigginton and Harris, Intelligent Skins, 36.
42

Within the scope of this research, the critical questions of “where” and “when” daylight is
needed are more important than “how much”. Adding the time and place as variables to the
equation requires the implementation of an automated-cognitive system that can automatically
detect human behavior, and react to it in an appropriate manner
42
. This is what is referred to as
an “intelligent” system, after adding the “how much” to the equation.
2.5. TYPOLOGY OF KINETICS IN ARCHITECTURE
Kinetic architecture can be expressed in different forms in buildings, one of which is integrating
dynamic features into the façade/skin. Kinetic façade elements are found in various
configurations. Below are some configurations of kinetic façades that have been adopted in
current buildings. All images in this section have been extracted from a Master of Building
Science thesis by former USC student Ryan Hansanuwat.
43

42
Wang, Intelligent Buildings and Building Automation.
43
Hansanuwat, “Kinetic facades as environmental control systems.”
43

Figure 2-6: Kinetic Typology – The figure shows various typologies of kinetic envelope applications on building
facades.

44

Figure 2-6: Continued

45

Figure 2-6: Continued

46

Figure 2-6: Continued

47

2.6. HUMAN INTERACTION AND CONTROLS
Architectural space can take advantage of an audience locally, regionally, and globally by
reconceptualizing the role that the physical environment plays in shaping the viewer’s
experience.
44

The quote above is from Interactive Architecture, a book by Fox and Kemp which presents the
general idea of human interaction in a simple way. Though it sounds simple, the quote carries an
important aspect that highlights the significance of human interaction in the field of intelligent
skins: it is the users’ learning experience. Intelligent architecture should be able to not only
expedite lifestyle and behavior, but also to influencing them.
45

Intelligence in architecture is a two-way process, in which the building learns from the
occupants’ behavior, and vice versa. The architectural environment can learn from our
experiences on a short-term or long-term basis, and can also teach us how to live more
efficiently. For example, when direct sunlight hits the working plane, an intelligent system of
series of louvers can adjust to overcome the problem of glare or over-illumination. The
occupants will learn that, at this time of the day, the sun direction is undesirable for better
working conditions. This feedback from the system is significant in teaching occupants how to
respond to such situations. Though there could be more complex examples, this one explains

44
Fox and Kemp, Interactive Architecture, 138.
45
Ibid., 142.
48

the simple idea behind a back-and-forth interaction. Another quote from the same chapter in
Fox and Kemp's book says that:

To a certain extent, our behaviors are nothing but learned intuitions growing out of our
experiences in the world.
46

As buildings react to changing human behavior, users are challenged with new levels of
involvement, understanding and choice. Since this interaction brings new learning experiences
to their lifestyle, it is crucial for architects to account for these changing patterns in future
designs, and make them geared towards enhancing the working environment in ways other than
facilitating the human experience inside the space.

Intelligent architecture responses could be based on either knowledge-based information,
sensor-based information, or both. There are many possible mechanisms of human interaction,
including sensors, which are highly important elements in intelligent environments. Sensors are
devices capable of gathering data from the real world, like motion, light, temperature, humidity,
sound, and so on.
2.7. MODELING INTELLIGENT KINETICS
Conceptually, the design of kinetic skins is similar to some of the advanced techniques where
the designer adjusts parameters to generate a range of outcomes, except that in the case of

46
Ibid.
49

intelligent kinetics there is no final form; rather, the design outcome is a kinetic process, from
which multiple forms will occur over the life-cycle of a building.
47
Kinetic skins are generally
described as elements integrated into one large system, with adaptable capabilities to create
more efficient spaces. The need for better-performing architecture has led to the emergence of
kinetics technology, extending beyond conventional techniques. The current trend in the
profession is towards incorporating sustainability and technology as integral elements of this
larger system, such as intelligence and kinetic applications. In practice, kinetic façades are found
in different compositions, where each has its own characteristics, objectives, and impact on the
building functionality and aesthetics. They are not necessarily for purely sustainable purposes,
but also have aesthetic or socio-cultural value. That being said, modeling intelligence in
computer tools is crucial for achieving all the objectives mentioned above.

Modeling intelligence is a special, complicated case of simulation. Commercial computer tools
for modeling intelligent architecture are not yet available for designers. Most of the work, time
and effort is still in research labs, and firms specializing in this advanced technology. The
challenging part of simulating intelligence is in how we can insert human-like agents into a
model and make them behave like human beings, and how the computer tool can detect these
agents and recognize their behavior as occupants. In the case of real-life simulations, where
sensors occupy the space, it is still hard – but not impossible – for the sensors to understand and
detect human behavior and emotions, and react accordingly. Many research labs are currently

47
Moloney, “Building Skins as Kinetic Process: Some Precedent from the Fine Arts.”
50

working on cognitive realization of human behavior, and how sensors can detect facial emotions
and human interaction.

Carnegie Mellon Robotics Institute is one of the leading labs in intelligence and human
interaction. The Intelligent Agent Technology Lab is developing a research project about “Agent-
based Composition of Behavioral Models (ABC)” in which cognitive models are being developed
to represent human performance in computer simulations. These models provide a
comprehensive representation of the knowledge, behavior, problem-solving skills, and
procedures used by occupants in task situations; they take into consideration the intellectual
capabilities and limitations of humans. Since there is no “standard” cognitive reference for all
situations, problems still persist in developing the human behavior models. The project team
analyzed labor-intensive tasks to create a detailed mapping of what humans do to accomplish
those tasks. Though this method is time-consuming, effort-intensive, and does not well
represent real human behavior, they were able to program human agents with a set of
production rules and executable procedures.
48

This is where the argument lies: how can we rationalize human behavior in the form of rules,
procedures and parameters in simulation tools? How simple should it be? Should the model be
capable of handling multitasking or single-tasking? In my opinion, a successful comprehensive
simulation would be one that exploits more than one human model, each representing a single-
task activity. In this study, the behavior of occupants is dependent on the quality of the

48
“Robotics Institute: Agent-based Composition of Behavioral Models.”
51

luminous environment. Intelligence has been integrated into the simulation from a single-task
perspective for adjusting the quality and quantity of daylighting inside the space; only the
desired illumination for occupants has been taken into account in the algorithm. A set of
parameters is changed, based on human discomfort assumptions that allow for testing the
intelligent skin system against different scenarios of occupant preferences. This is not the only
method of simulating human behavior during the early design stage, but it is the simplest.

52

3. CHAPTER 3 DYNAMIC DAYLIGHTING
Bringing daylight into the core of the building is an architectural design challenge that aims for a
better visual environment and greater energy efficiency. This can be achieved through the use
of advanced dynamic daylight strategies.

This Chapter addresses the specifications for daylighting requirements, providing essential
fundamental knowledge for the purpose of this research. It presents background research on
daylight and its rewards, daylight qualities, light-deflection strategies, daylight evaluation, and
the target benchmark of the design. It is expected that the reader will, at this point, have the
necessary information on which the simulation in the following chapter is based.
53

3.1. INTRODUCTION
In 1973
49
, the energy crisis triggered designers’ interest in the use of renewable sources of
energy, such as solar power, in an effort to reduce non-renewable energy usage. Today, the
increasing notion of scarce resources has demanded better daylight strategies to minimize
dependency on electric light. Despite the growing notion of energy conservation, daylight is not
often used as a major scheme to exploit renewable sources and reduce energy consumption.
Many offices rely on electric light during daytime, when it is unnecessary and can be replaced by
the integration of daylight strategies. The point is that the notion of energy conservation has not
influenced architectural practices enough to adopt advanced daylight approaches in the design.
They are required by code to provide certain levels of illumination for different space usage,
regardless of the source of light, which gives more room for relying on electric light to
compensate for the lack of interior illumination from natural light. New demands by the building
industry, and occupants, requires taking daylighting into account during the design phase, to
allow more light deep into the core of the building and maximize the percentage of daylit areas.

Daylighting in architecture is a design strategy that exploits natural sunlight in indoor spaces,
reducing dependency on electric light, and maintaining human health and a productive work
environment during daytime. Daylight refers only to the visible part of the energy spectrum
released by the sun (Figure 3-1). It is a source of light that provides full-spectrum light with
flawless color rendering.

49
Boubekri, Daylighting, Architecture and Health, 39.
54

Figure 3 -1: Electromagnetic Spectrum – the range of the visible part of the spectrum.
50

Daylight, as a free design resource available to architects, enhances the quality of indoor spaces.
It invigorates interior spaces, creating a relationship between the occupant and the space. This
relationship could be dramatic or intimate, depending on the function of the space. For office
spaces, intimacy is required for a better work environment. The art of using daylighting in
architecture is not only about allowing light into the space, but providing it without adverse
effects. Despite these effects, life cannot continue without daylight, which is required for human
health and, now more than ever, for energy-saving approaches.

Successful daylighting is not about increasing opening sizes or adding skylights. It encompasses
thoughtful integration of design approaches addressing glare, heat gain, variation in light-

50
Ander, Daylighting Performance and Design, 27.
55

availability, and direct light penetration. However, in the context of this thesis, only variation in
light level, luminous distribution, and penetration depth are tested with different daytime
scenarios.

The benefits of a well-designed daylighting concept in office spaces range from better
productivity, due to enhanced daylighting quality inside the space, to reduced consumption of
artificial lighting. A well-designed space should be able to optimally utilize solar energy through
controlling and harvesting daylight. Taking into account the limitations of passive daylight
strategies, successful daylight design in office buildings requires going beyond conventional
techniques of integrating large openings or light shelves in the architecture. It requires a system
that is capable of accounting for unforeseen changes in natural lighting. These changes can
range from external elements like reflections from surrounding context, to internal factors like
interior surface reflections or changing occupants’ activities that require different illumination
schemes.
3.2. DAYLIGHT REWARDS
3.2.1 HUMAN HEALTH AND PRODUCTIVITY
Despite the excessive use of artificial light in architecture, people still appreciate the natural gift
of daylight, acknowledging its advantages. Natural lighting has always been an important design
feature in the building design field. Besides providing a connection to the outdoor environment,
it is as vital an element for maintaining human physical health as it is to plant life. Natural light
56

can bring happiness to a space and make people escalate the value of their presence. It also
results in an environment with dynamic light conditions, unlike one lit artificially.

Daylight strategies are crucial for sustaining human health, work productivity and a pleasing
work environment. There is a disconnection in office architecture and daylighting design which
has an extensive impact on inhabitants, affecting body health, productivity, and visual comfort.
Such problems arise due to the lack of daylight inside a space, and the extreme reliance on
electric light. Most people perceive daylit environments as pleasant spaces for work. In 2003, a
student at the Rensselaer Polytechnic Institute carried out a survey of measured negative mood
under different daylight conditions. Dasgupta was able to show that, while people working for
20 minutes in an office with a large window during daytime showed a small but significant
reduction in negative mood, workers in the same office space during nighttime had no change in
their negative mood.
51

In his book, Koster briefly explained the influence of daylighting on human physiology and
psychology. The following quote explains how important natural light is to human health:
Light synchronizes the human biological clock with day, night and seasonal rhythms. A
lack of natural daylight can lead to disorders of the automatic nervous system, loss of
energy, fatigue, a tendency towards self-isolation and metabolic disorders. Conversely,
intensive light therapy has been shown to support the healing process.
52

51
Dasgupta, The Impact of Windows on Mood and Performance of Judgmental Tasks.
52
Koster, Dynamic Daylighting Architecture, 57.
57

3.2.2 INDIVIDUAL OUTCOMES
In 1996, Veitch and Newsham proposed that lighting quality can be defined as the degree to
which the luminous environment supports the following requirements of the people who use
the space (Figure 3-2):
 Visual performance;
 Post-visual performance (task performance and behavioral effects other than vision);
 Social interaction and communication;
 Mood state (happiness, alertness, satisfaction, preference);
 Health and safety;
 Aesthetic judgments (assessments of the appearance of the space or the lighting).

58

Figure 3-2: Qualities of lighting – Veitch’s proposal determines the quality of lighting in any given installation through
determining the balance of the (sometimes conflicting) dimensions shown in the diagram.
53

They also added that the qualities mentioned above do not allow direct measurement of
daylighting, but express an emergent state that is created between the occupant in the
environment and the light. According to their report, good lighting quality exists when a lighting
system creates good conditions for seeing, supports task performance or setting-appropriate
behaviors, fosters desirable interaction and communication, contributes to appropriate mood,
provides good conditions for health and avoids ill effects, and contributes to the aesthetic
appreciation of the space.
54

53
Veitch, “Psychological processes influencing lighting quality,” 19.
54
Veitch, J. A, Determinants of Lighting Quality I.
59

Figure 3-3: Individual Outcome – the influence of various factors, including lighting conditions, on the individual
outcome.
55

3.2.3 ENERGY USAGE
Lighting accounts for 30-50% of the energy use in commercial buildings.
56
Significant energy-
savings can be obtained if daylight strategies are incorporated into the architecture, primarily
for use during daytime when sunlight is available and the use of artificial light is unnecessary.

Daylight design strategies can reduce the extensive use of electric light inside office spaces if
well designed, as well as indirectly increasing savings due to changes in thermal loads.
Controlling sunlight penetration into a space is a broad strategy that not only guarantees

55
Veitch, “Psychological processes influencing lighting quality,” 20.
56
Phillips, Daylighting, 38.
60

adequate light levels, but also reduces solar heat gain, which is preferred in hot climates.
However, savings vary by building orientation, season, and façade design.

The Environment Department of Great Britain carried out a comparative study on various façade
types (Figure 3-4). The study shows room for decreasing lighting loads through the design of
better performing façades. Lighting needs for a cellular plan are the least significant, while an
open office space required a greater lighting load for a better work environment. In cellular
plans, more vertical surfaces could be used to bounce light and provide more illumination,
which is not the case in open offices. Adding movable reflective surfaces on façades could be a
good solution for open space offices.

While a passive system can result in considerable energy savings, active approaches can go
beyond these conventional numbers, simply because they are capable of adapting to the major
source of daylight - the sun - which is a dynamic natural force that changes location over the
course of the day.
61

Figure 3-4: Energy Usage in Offices – the breakdown of energy usage costs, including lights, for typical and good
practice offices.
57

3.3. DAYLIGHT FUNDAMENTALS
3.3.1 SOURCE OF LIGHT
Daylight, an important requisite in office buildings for efficient task execution and a satisfying
visual working environment, is usually a combination of direct sunlight, diffuse light from the

57
Koster, Dynamic Daylighting Architecture, 23.
62

sky, and light bounced off surfaces. One can distinguish daylight sources into two main types:
primary sources, and secondary sources. Primary sources are sources that release light like the
sun and sky, while secondary sources are sources that reflect light but do not produce it, like
surfaces and objects.

Sunlight passes through various layers in the atmosphere before hitting the built environment.
The intensity of solar radiation is dependent on various factors, among which are the sun’s
position, clouds, gaseous and particulate pollution, and thickness of the air mass (Figure 3-5).
Daylight delivers full-spectrum light for flawless color rendering, as well as providing the best
visual conditions in a luminous environment for the human eye.

Figure 3-5: Atmosphere Layers – the layers through which light passes before arriving on the work plane.
58

58
Baker and Steemers, Daylight Design of Buildings, 31.
63

Considered a secondary source of daylight, surfaces are the surrounding context, including the
ground and neighboring façades, and interior surfaces in the space. The finish material of each
surface affects the amount of light bounced off of it. The brighter the surface, the more light is
bounced off. Therefore, daylight at a point in a space is composed of the following components:
 Direct sunlight (in the case of a clear sky and no blockage);
 Diffused light from the sky;
 Externally reflected components;
 Internally reflected component.
Considered the only source of natural light, the sun has high luminous efficacy. On a clear day,
direct sunlight can illuminate a horizontal plane up to 10000 foot-candles. On an overcast day,
sky luminance may reach up to 20,000 cd/m
2
, while the luminance of a clear sky reaches 50,000
cd/m
2
.
59

3.3.2 PROPERTIES OF DAYLIGHT
Visual and thermal problems always accompany the penetration of daylight into a space, and
are perceived by occupants as unfavorable luminous and thermal conditions. Insufficient
lighting, illumination beyond recommended ranges, or direct sunlight falling on working planes
are among the major problems caused by daylighting. More problems are addressed in terms of
characteristics or variables (Figure 3-6), thereby controlling the quality of the luminous
environment. Each parameter is looked at as a design problem that results in an adverse
condition, requiring the adoption of design strategies to overcome it.

59
Koster, Dynamic Daylighting Architecture, 54.
64

Figure 3-6: Daylighting Characteristics – the characteristics of daylighting that have impact on occupants’ productivity
and visual comfort in indoor spaces

Within the scope of the study, the two main factors that are tested are the exterior dynamic-
intelligent skin that harvests and controls the penetration and direction of light, and the
complex panel geometry. The conventional horizontal louver linked to an intelligent system is
the first phase of the study, while the second phase tests various panel geometries linked to the
same system of a real-life building.

Within the context of this research, the quality of daylight in a space is addressed through three
main aspects: the illumination levels range, the light penetration depth, and the luminous
distribution in the space. In office buildings, the use of daylighting has a considerable impact on
the energy consumption of the building. However, flooding the space with daylight neither
achieves the recommended illumination range nor provides a good visual environment. It may
INDOOR
DAYLIGHTING
CHARACTERISTICS
Brightness
Luminous
Distribution
(Quality)
Colour
Glare and
Veiling
Reflections
Visual Contact
with Outdoor
Environment
Individual
Control
65

result in over-illuminating the space, leading to many visual, thermal and energy problems.
Daylight harvesting strategies may efficiently control the penetration of light into the space,
controlling the quality of the luminous environment accordingly.
3.3.3 FORM AND ORIENTATION
Orienting a building in relation to the surrounding context and the known path of the sun is a
crucial concern in the design process, allowing the designer to either let more light into the
space, or to prevent it (Figure 3-7). There are different techniques for controlling light amounts
in spaces, among which horizontal and vertical fins are the most famous. Though they have
been very successful in solar shading and light deflection throughout the past decades,
designers are rethinking this technique with an eye to using advanced integrated technology.
While vertical fins are efficient blockers on east and west façades, where the sun angle is low,
horizontal panels are the most effective for south-facing spaces, due to high-angle sun. These
strategies are commonly known, because of the constant path of the sun, which moves in the
same route every day, every month, and every year.

66

Figure 3-7: Building form and orientation – various building forms and orientations, and their impact on daylight
penetration into the space.
60

Building form determines the surface area exposed to the outdoor. The more compact the
building plan, the less daylit space exists (Figure 3-7). The depth of the plan and section
determines how deep the light will penetrate into the space. As shown in Figure 3-7, a north-
south orientation has a deep width, which minimizes the penetration of sunlight into the space.
Conversely, an east-west orientation has a shorter width, allowing sunlight to hit most of the
indoor space. Generally, orienting the longer side of the building towards the sun is a favorable
idea. Also, the use of courtyards produces greater light-deflection on the building exterior
surfaces, and directs light into more space. This strategy also increases the perimeter of the
building that is exposed to sunlight.

60
Ibid., 69.
67

Figure 3-8: Sun path diagram – the annual sun path for Los Angeles. The high-angle sun represents August, while the
low-angle sun reflects the sun location in December, both on the same day, at the same time.

Given that the path of the sun is precisely known, designers are able to calculate the altitude
and azimuth of the sun on any specific date and time in any location worldwide (Figure 3-8).
They can predict the best building orientation, and generate diagrams displaying the sun’s
location and annual shading range. Using numbers from the charts, equations, and tables, the
amount of illumination inside a space can be manually calculated. The fluctuation of the amount
of sunlight hitting a space is dependent on the orientation of space, location, and month, day
and time of the year. During the summer, a larger surface area of a building is exposed to the
sun - represented by the blue line on the horizontal plane in Figure 3-8, while the red line,
representing the sun range during the winter, has a shorter period.
3.3.4 INTERNAL FACTORS IMPACT
In addition to the external factors mentioned above, lighting design is directly related to some
internal parameters which are present inside a space itself. Manipulating these parameters
changes the quality of the interior luminous environment. Required illumination differs
68

according to task; the more complex a task, the more light is likely to be required. Given that
light in general office spaces is task-oriented, all parameters of task execution should be
precisely defined. Some of these parameters are known and unlikely to change, like the working
plan of a computer desk - which is always around 2’ 7” (90 cm). Illumination levels are also
dependent on materials - specifically surface reflectance, dirt accumulation, and room
dimensions. These factors should be defined prior to beginning the design process. Furniture
distribution in relation to glazing location is crucial in daylighting. A favorable practice for
designing is to change one variable at a time, and in this study furniture has been omitted from
the simulation, for quick-easier runs, intending to bring as much surface area as possible into
desired conditions, regardless of the occupancy status.
3.4. PERFORMANCE INDICATORS
3.4.1 PREVIOUS WORK
In section 3.2.1, the importance of daylighting to human health was highlighted as a necessary
requisite for a better, more productive work environment, and greater energy savings. However,
there are no exact measurements that can directly quantify this impact. Several studies on the
topic of daylight quality and performance have been conducted in the past ten years. Several
research labs dedicated sections to lighting research, including daylighting. Various studies
showed different assessment criteria for the quality of daylight inside a space. No disagreement
appeared in any of this literature on the basic evaluation factors, such as illumination levels,
luminous distribution, and glare. Nevertheless, each researcher added their own factors,
depending on the experimental conditions and the objectives of the study.
69

In 1994, the committee of quality of the visual environment of the Illuminating Engineering
Society of North America (IESNA) identified ten aspects that impact lighting quality, and can also
be used to evaluate daylighting quality:
61

 Brightness (comparative luminance) of room surfaces;
 Task contrast;
 Task illuminance;
 Source luminance (glare);
 Color spectrum and color rendering;
 Daylight (view);
 Spatial and visual clarity;
 Visual interest;
 Psychological orientation;
 Occupant control and system flexibility.
In the IESNA assessment criteria, factors like visual interest, psychological orientation, and
occupant control, are hard to evaluate, due to the various personal variables involved in each
factor. The remaining factors can be evaluated through calculations and computer simulations.
However, not all factors can be studied within the context of this research, due to time
limitations. Nonetheless, these criteria provide a sound assessment of indoor daylight
performance in office spaces, if all factors are taken into account.

61
Dubois, “Impact of Solar Shading Devices on Daylight Quality,” 24.
70

In 2001, Marie-Claude Dubois conducted doctoral research on “Impact of Solar Shading Devices
on Daylight Quality,” with a focus on office spaces. She came up with simpler assessment
measures for evaluating the performance of daylight inside a space. Dubois defined five main
indicators affecting the luminous environment in office spaces:
62

 Daylight factor;
 Absolute work plane illuminance;
 Illuminance uniformity on the work plane;
 Absolute luminance values on the vertical plane;
 Luminance ratios between the paper task, the walls, and the video display terminal
(VDT) screen.
Within the framework of this thesis, three conditions were selected, based on the literature
review of the topic and previous work done in the same field: illuminance, luminous
distribution, and light penetration.
3.4.2 ILLUMINANCE
Different organizations recommend different light levels for office spaces. The recommended
illumination level according to the Illuminating Engineering Society of North America (IESNA) for
a typical office space is 200-500 lux.
63
The NRC Institute for Research in Construction

62
Ibid., 13.
63
IES North America, IESNA Lighting Handbook.
71

recommends a level of 400-500 lux for typical office work.
64
In terms of daylight factor, the
recommended percentage is 2-5%. This study targets a level of 300 lux, taking into account that
values less than 200 lux and higher than the recommended range may be acceptable in some
areas of the space, under certain conditions. In general, the IES lighting handbook defines values
ranging from 100 to 1500 lux
65
as acceptable (Figure 3-9). Evaluation of the simulation runs will
be based on this range.

Figure 3-9: IES Lighting Handbook Illumination – the recommended range of illumination in footcandles for office
spaces.
66

3.4.3 LUMINOUS DISTRIBUTION
For a better visual environment, the IESNA recommends that, within the occupant’s field of
view, the ratio between the maximum and minimum illuminance should not exceed 1:10.
67

64
National Research Council Canada, “NRC Canada.”
65
Bradshaw, The Building Environment, 259.
66
Ibid.
72

However, the NRC Institute for Research in Construction recommendation exceeds that of IES
and goes up to 1:20
68
, providing an acceptable argument for this high contrast, like highlighting
certain objects on the working plane. Sometimes, due to high contrast, the occupant perceives
parts of the space to be dark, which in reality have sufficient light levels. Maintaining this ratio
prevents the false perception of light levels inside spaces.

Figure 3-10: Illuminance Distribution – typical fall-off curve-shape of illumination inside a space.

Daylight factor (D) = E
i
/ E
a
x 100%

E
i
: intensity of interior illumination
E
a
: intensity of exterior illumination

67
National Research Council Canada, “NRC Canada.”
68
Ibid.
73

3.4.4 DEPTH OF LIGHT PENETRATION
An untreated window opening allows light penetration one-and-a-half times the distance from
the floor to the window head. Incorporating a light shelf extends the ratio to up to twice the
distance. For example, a 2.20m window head height allows the penetration of daylight into the
space up to 3.30m if using untreated opening, and 4.40m using a light shelf. Within the context
of this study, the goal is to go beyond the 2x ratio, aiming for at least two-and-a-half times this
vertical distance.

Figure 3-11: Illuminance-Depth Relationship – typical fall-off curve of illumination inside a space, and its relationship
to room depth. The deeper the room the less illumination at the back
69
.

69
Ander, Daylighting Performance and Design, 13.
74

3.4.5 EVALUATION CRITERIA
Dubois also included in her dissertation work evaluation criteria for the five indicators that she
was testing (Figure 2-7). She provided numerical values for each category, and their
corresponding interpretation. Based on the objective of this research, only the first three factors
are considered for evaluation parameters - in addition to light penetration depth, which was not
taken into account.
75

Figure 3-12: Interpretation of Indicators – evaluation criteria for each of the indicators affecting indoor daylight
performance. Only factors one thru three are considered within the scope of this work.
70

70
Dubois, “Impact of Solar Shading Devices on Daylight Quality,” 14.
76

3.5. DAYLIGHT-DEFLECTION TECHNIQUE
3.5.1 EVOLUTION OF LIGHT DEFLECTION
The concept of light deflection goes back to 1801
71
, when Johann Soldner, German physicist,
mathematician and astronomer, concluded that light would be diverted by heavenly bodies. He
was able to calculate the amount of sunlight ray deflection caused by a star. It was not until
1919 that British astronomers were able to attempt the first measurement of the relativistic
phenomenon of light diversion. Their conclusion was dependent on the fact that light-deflection
changes the perception of astronomical observations.

Figure 3-13: Simple Light Deflection – the basic concept of light-deflection in 1919.
72

The "location of a star in the night sky" is simply short-hand for "the direction from
which that star's light reaches us". Starlight that passes close to the sun before
reaching us gets deflected, as sketched in the figure above (but by a much smaller
amount than is shown there). This starlight will thus reach us from a slightly different
direction than when the sun is in some different region of the sky. Accordingly, the
star's position in the night sky is shifted slightly.
73

This inspiring concept of exploiting sunlight had a great impact on architecture. Designers
extensively adopted this technology in different forms of daylight controlling systems, such as
light-pipes, prismatic louvers, and anidolic lighting systems. This technology plays a great role in

71
Soldner, J. G. v., “On the Deflection of a Light Ray from its Rectilinear Motion.”
72
“The light side of gravity — Einstein Online.”
73
Ibid.
77

redefining the importance of daylighting strategies in architecture, and the possibility of
maximizing their usage.
3.5.2 CONCEPT OF DAYLIGHT DEFLECTION
Daylight deflection is the technique of re-directing light into a space in a controlled fashion. This
technique has been around since the early use of venetian blinds and light-shelves, which
depend on bouncing light back into the outdoor environment. These were not referred to as
light-deflectors, though, because their main purpose was blocking light. Solar shading and
conventional venetian blinds efficiently block direct light, but they do not harvest and re-direct it
into the space, thereby losing it to the outdoor environment: a waste of energy. Light deflectors
block light by re-directing it outside of the occupants’ line of vision, and into the space,
protecting inhabitants from glare and direct sunlight.
Daylight deflection is re-direction of incoming natural light through glass systems
containing reflecting and light-guiding or light-diffusing surfaces or gratings. Such
systems distribute and diffuse the incoming light in a room, ensuring even illumination
with no glares or heavy shadows.
74

3.5.3 DAYLIGHT HARVESTING USING LIGHT-DEFLECTORS
Daylight deflection is a process achieved using reflective shading devices. It can be static or
dynamic. As a technological advancement in daylighting design, the purpose of daylight
deflection, besides protection against glare, is to control the intensity and direction of light, and
its distribution. This can be achieved by controlling the amount of light penetrating through the
building envelope, and reflecting unnecessary light back into the outdoor environment. In his

74
“Thiele AG - Transparente Innovation.”
78

book, Koster mentions that the efficiency of a daylight deflection system is directly related to
the following parameters:
75

 Type of the deflector;
 Physical properties of the deflector;
 Location of the system in the building;
 Mounting position relative to the space.
Koster also mentions that the purpose of light-deflection techniques is to provide protection
against solar heat and glare, and to provide a controlled supply, thereby improving indoor
illumination. The advantage of light-deflection techniques over solar shading is their ability to
work as a control layer, and to strengthen weak daylighting, specifically at the back of a space.
In practice, the shading systems are closed during periods of the largest solar gains
(direct solar radiation), darkening the interior and resulting in a need for artificial
lighting. This is a waste of energy that could be avoided, especially since the total
electrical energy for lighting is transformed into heat that must be removed in summer
by an energy-intensive interior cooling system.
76

The above statement addresses the need to exploit light deflection and to control light
penetrating into the space. Instead of possessing only one function, efficiency requires that
shading devices minimize solar heat gain and control light by blocking it or bouncing it off
appropriately into the space, without wasting free solar energy and consuming more electric
light.

75
Koster, Dynamic Daylighting Architecture, 80.
76
Ibid., 13.
79

As previously mentioned, daylight-deflection is the process of re-directing light into the space or
back into the outdoor environment. However, the use of both strategies in a combined scheme
is possible, and may prove more efficient (Figure 3-14). Given simple light reflection techniques,
this combination may allow for efficient daylight optimization inside a space, due to a combined
configuration of blocking unnecessary light, and harvesting it in an appropriate way that does
not adversely affect the occupants’ working environment.

Figure 3-14: Combined Configuration – the shading and harvesting configuration of different panels actuating
independently in a secondary skin system.
77

77
McGuire, “A system for optimizing interior daylight distribution...”
80

Figure 3-15: Angles Calculation – incident and reflected angles.
78

Equations used for calculation
79

(

)

Incident Solar Angle
Louver Angle
Redirected Angle
Room Depth
Window Height

78
Ibid.
79
Ibid.
81

In the field of daylight harvesting, there are clearly many ways of controlling and directing the
light; among which are the actuation pattern, geometry of panel, size of panel, orientation of
space, reflectance of materials, and the panel adaptability to intelligently respond to changes. In
the design stage of this study, some of these ways may be considered for enhancing the skin
design of real-life architecture.

As previously mentioned, daylight-deflection is re-directing light into the space or to the
outdoor environment. However, the use of both strategies in a combined scheme is possible
and may prove more efficiency (figure 3-14). Given the simple light reflection techniques, this
combination may allow for efficient daylight optimization inside the space due to a combined
configuration of blocking unnecessary light and harvesting it in an appropriate way that does not
destruct the occupants’ working environment.
Bring it in high. Bounce it or filter it. Control it. Harvest it.
80

80
Schiler, Daylight Harvesting.
82

4. CHAPTER 4 INTELLIGENT SKIN DESIGN TOOL
Simulating intelligence in building skins and testing it for dynamic daylighting performance is a
challenging study in terms of simulation parameters and the logic of data processing.
Rationalizing the logic processing in the form of parameters is the key element for the success of
the simulation process.

This chapter describes the specification of the proposed algorithmic design tool for simulating
intelligent kinetic elements on building facades and their impact of daylighting performance.
83

4.1. INTRODUCTION
Software advances have drastically enhanced the design process in the profession. The more
parameters we are able to control and to procure, the more solutions generations the software
can produce. The overwhelming responsibility of data exchange between various interfaces in
the design process is crucial for efficiently exploiting the design tool. The demand for integrating
performance-based techniques into the early design stage requires a bidirectional exchange of
data, where information flows back-and-forth between multiple interfaces.

This chapter describes an extensible parametric design tool for assessing the performance of
kinetic facades. Grasshopper parametric definition is used to bridge the gap between the early
design stage and the energy-performance of the building, in terms of daylighting. The proposed
design tool adds to the current performance-based technology by making particular
contribution to the field of integrating energy-performance into the early design phase. The
contribution includes finding the best-possible skin configuration for better daylighting
performance on any day of the year.

Within the framework of this thesis, the performance of daylighting inside office spaces is
studied through the use of new simulation tools that are capable of handling multi-inputs, as
well as considering more than one variable at a time. The use of such tools allows not only for
accurate results, but also almost instant variation of daylight performance when façade
elements actuate. The complexity of this study lies in defining the intelligence of the skin, and
simulating data exchange between multi-sensor layers, management system, and façade
84

elements. A simple example was developed to see if the performance criteria could be achieved
using Rhino as a modeling tool, Grasshopper as a parametric interface, DIVA 1.1 for daylight
evaluation, and Galapagos for problem solving.

Rhino (http://www.rhino3d.com) is a 3d NURB-based modeling program. Until relatively
recently, it has not been easily used in conjunction with simulation software. Now DIVA-for-
Rhino supports a series of performance evaluations including links to Radiance, Daysim, and
Evalglare (Rheinhart et al., 2010).

Grasshopper (http://www.grasshopper3d.com) is a free, graphical algorithm editor tightly
integrated with Rhino's 3d modeling tools. It is possible to integrate pseudo-environmental
effects such as sun and wind to dynamically change form. Sun systems have also been
developed for it to achieve accurate sun shadow simulations, and two-way connections with
Ecotect have been demonstrated.

DIVA (http://www.diva-for-rhino.com) is a Rhino plugin that can be directly run from the
Grasshopper interface using a pre-built definition provided by Harvard GSD (SD)
2
. This definition
allows data exchange between DIVA and Rhino, and uses Rhino as an interface for showing the
results and the visualization. DIVA uses the following third-party software:
81

81
“DIVA for Rhino - Credits.”
85

 Radiance;
 Evalglare;
 GenCumulativeSky;
 Daysim.
Galapagos is a genetic algorithm feature that is used for problem solving cases within
Grasshopper. It creates an evolutionary generic loop that populates generations of possible
solutions with random individuals based on the predefined criteria. The system couples similar
possible solutions together and then finds a best fit solution, which may end up being a locally
optimal solution in some cases. Galapagos is used in this study to find the best possible tilt
angles of the louvers’ configuration for certain times of the day. However, Galapagos is run
using a pre-defined set of parameters, leaving only the calculation for this tool.
4.2. CONCEPTUAL IDEA
Intelligence in buildings has always been influenced by human intelligence. But since human
intelligence has not been powerfully modeled in simulation tools yet, the integration of all of the
above tools into one system – to be used for this study – may enable the simulation of
abstracted characteristics of intelligent kinetic features. Figure 4-1 shows how these features
can be rationalized and abstracted for a simple modeling process using parametric tools.

Grasshopper is responsible for setting the input parameters, according to the desired luminous
environment conditions, and passing these to the daylighting simulation tool, DIVA/Radiance,
which then processes the data and sends the results back to Grasshopper for evaluation.
86

Grasshopper compares the results against the pre-defined performance criteria. If the results
are acceptable, Grasshopper provides the generated solution as the best possible under the
defined simulation conditions. If the results do not match the criteria, Grasshopper sets another
scenario for the skin and triggers the simulation tool to re-run the new scenario and generate an
outcome, and so on until an acceptable result is achieved (Figure 4-1).

Figure 4-1: System logic – the path of data exchange between the proposed algorithm and the daylighting simulation
tool.

The modeled space in Rhino has dimensions of 6.0m width, 7.5m depth, and fully-glazed height
of 3.0m (Figure 4-2). The interior surfaces have been assigned reflectance of 80% for ceiling,
50% for walls, and 20% for floor. The secondary skin louvers have reflectance of 90%. The
opening has been assigned generic doubled-glazed material with 72% visual transmittance.
87

Because of its sunny weather and daylight availability, Los Angeles has been chosen to be the
location of the test and this south-facing office space.

Figure 4-2: Space dimension - the dimensions of the office space used in the simulation.

Initially, the skin system is divided into 8 louver levels, where each level has two louvers (Figure
4-3). It is intended to control each of the ten louvers independently with different tilt angles.
Though the system simulates daylighting according to the actuation of eight louvers, only five
louvers cover the glazing portion of the space. These five louvers have the greatest impact on
the luminous quality of the workplane. Figure 4-3 shows the algorithm for simulating 10
independent louvers based on five levels. However, for better presentation of the proposed
design tool, each two louvers on the same level are treated with the same rotation angle, using
another algorithm that will be shown later (Figure 4-4). This approach does not eliminate the
flexibility of the definition to independently actuate each louver.
88

Figure 4-3: Louvers definition - part of the Grasshopper definition that illustrates the ten louvers with the angle
sliders on the left hand side.

Figure 4-4: Independent split system - the two independent layers of louvers used in the proposed algorithmic design
tool.
89

4.3. DESIGN TOOL DOCUMENTATION
This section documents the proposed design algorithm that can be used to determine an
optimal configuration for the building skin for better daylighting performance. The algorithm is
applied to a secondary skin configuration composed of horizontal louvers split into two
independent layers. The definition utilizes Radiance as the calculation engine for daylighting.
The definition has been divided into screenshots which are shown in this section. Each
screenshot describes one part of the definition.
4.3.1 SKIN SYSTEM
This section of the definition has two similar parts, each of which operates one layer of louvers.
The design of the secondary skin basically originated in Rhino, from a simple pair of rectangular
louvers. Each louver was assigned as a “BRep” component to one of the definitions shown in
Figure 4-5, and was duplicated using “move” components in Grasshopper. Prior to louver
duplication, the geometry was exploded to define the vertices of the louver. Then, a midpoint
was defined on the shorter edge of the louver and was set as the louver center of rotation.

The selected variables for the skin alteration are the rotation angle of the louvers and the
distance between them. The rotation angle was set to a range of 0: to 180:, where 0: to 90:
allows for a shading configuration of the louvers, and 90: to 180: allows for a harvesting
position. The distance between the louvers ranges from 0.50m to 2.00m, where 0.50m allows
for 0.12m overlap of two louvers, if required under certain circumstances, while 2.00m provides
more potential for greater light penetration and better view of the outdoor environment, for
certain overcast sky conditions.

Figure 4-5: Independent louver actuation - the two independent layers of louvers used in the proposed algorithmic design tool.

Figure 4-6: Illuminance Scale - the desired illuminance scale based on the color swatch in the definition

90
91

Though only two variables were chosen for altering the skin for the purpose of this thesis, the
algorithm has the potential to accept more variables, as well as to replace the original ones with
new attributes. All that is needed is to replace this part of the definition by the new skin
variables.
4.3.2 DIVA COMPONENT
This plugin component runs Daysim, the calculation interface, which employs a commonly-used
calculation engine, Radiance. The DIVA version used in the algorithm is version 1.1. The
component requires some inputs to get it properly running, among which are toggle run, metric
input indicating the type of desired test, a slider that controls the kinetic elements, the
geometry of moveable objects, and the material of the objects. The variant input refers to the
name assigned to every simulation run; the component saves the results under this filename. As
shown in Figure 4-7, the material assigned for the external louvers is high reflectance (90%).

On the right-hand side of the DIVA component, there are two different parts: visual adjustment
for the illuminance, and 36 values of illuminance of the calculation grid. The visual adjustment,
shown in Figure 4-7, results in showing the illuminance scale (Figure 4-6) in the Rhino viewport
and over the calculation plane. One color swatch refers to the illuminance scale, and the other
refers to showing the color gradient over the workplane (Figure 4-7). As mentioned in Chapter 3,
the design illuminance range, according to IES, is 30-150 footcandles for office spaces.
4.3.3 ILLUMINANCE VALUES
The output from DIVA is viewed in a “panel” component in the Grasshopper definition, as shown
in Figure 4-8. Though in the Rhino viewport the results do not include decimal places, the
92

component in Grasshopper gives results up to six decimal places. Consequently, an adjustment
to the values was applied for better evaluation, and the results are shown to two decimal
places. Also, values have been converted from lux to footcandles by dividing each value by 10.
The resultant value is an approximation of the footcandle value.
4.3.4 EVALUATION AND PERFORMANCE CRITERIA
After extracting the results from DIVA into a “panel” component, an acceptable range of
illuminance of 30-150 footcandles was set according to the IES recommendations
82
(see Chapter
3). The range components convert all acceptable values into “true”, and unacceptable values
into “false”. As mentioned in Chapter 3, the algorithm evaluates the space for three criteria:
75% of the points should be within the desired illuminance range (30-150 fc.); the luminous
distribution contrast ratio between highest and lowest points should not exceed 10
83
; and the
penetration depth of daylighting into the space should be greater than twice the window height.

82
IES North America, IESNA Lighting Handbook.
83
Ibid.

Figure 4-7: DIVA Component Section - the part of the definition responsible for running the analysis and extracting the results.

93

Figure 4-8: Value adjustment - the value adjustment prior to evaluation.
94
95

Using a “ReMap” component, all “true” and “false” values were converted into numbers: “1” for
“true” and “0” for “false” (Figure 4-9). Then, these numerical values were sorted in descending
order. According to the criteria previously set in Chapters 1 and 3, 75% of the 36 calculation
points should fall within the range for acceptable luminous environment. Hence, the first 28
points should have a numerical value of “1”.

As shown in Figure 4-10, a set of “list item” components were used to extract the values of 75%
of the total points. These components extracted the items numbered “0” through “27”, giving a
total of 28 points. For an acceptable illuminance condition inside the space, the numerical value
of 75% of the points should be “1”, which means the sum of those points should be exactly “28”.
If the sum of the points was not equal to 28, the “panel” component would show “false”,
indicating an unacceptable illuminance condition inside the space (Figure 4-11).

The daylighting penetration depth criterion was indirectly included in the illuminance evaluation
part. The simulation was based on a space depth of 2.5 x window height, 7.50m. Calculation
points were evenly distributed all over the space, at a distance greater than twice the window
height. Thus, these points were included in the 36 calculation points that were extracted from
DIVA. If the final result showed “true”, it meant that 75% of the space – which is greater than
twice the window height – fell within the acceptable range.

The third and last performance indicator was the luminous distribution of daylighting inside the
space, which, within the context of this study, is sometimes referred to as “contrast ratio”. The
IES recommends a contrast ratio between the lowest and highest illuminance values that does
96

not exceed 1:10. Since the illuminance values were sorted in descending order, the highest
value would have “0” index and the lowest value would have “35” index. These values were
extracted using the “list item” component and divided by each other. If the resultant was
between 1 and 10, the results were acceptable (Figure 4-12).

Adding to the efficacy of the experiment, a genetic algorithm was incorporated into the
definition to enable a search for the best skin configuration at specific dates and times, or under
different sky conditions. The genetic algorithm works on finding an optimal – but not necessarily
the best – solution under certain parameters and conditions. These parameters could range
from users’ desired illumination levels to externally-reflected daylighting components. Changes
in any of these parameters triggered the system to run and find an optimal configuration for the
skin to maintain the desired luminous environment.

Figure 4-9: Illuminance values adjustment - converting illuminance values to “0” and “1” according to the acceptable range.

97

Figure 4-10: Calculation points extraction - extracting 75% of the calculation points for evaluation.
98

Figure 4-11: Illuminance evaluation result - the final result of the illuminance performance criteria
99

Figure 4-12: Luminous distribution evaluation criteria - the components designed to evaluate the luminous distribution inside the space.

100

Figure 4-13: Genetic algorithm fitness value - the genetic algorithm component connected the fitness value.

101

102

As a final procedure, Galapagos was added to the definition to search for an optimal
configuration of the louvers that achieved the main three performance criteria previously
mentioned. It creates an evolutionary loop that populates generations of possible solutions with
random individuals based on the previously defined criteria. The system couples similar possible
solutions together and then finds a best fit solution. This might end up being a locally optimal
solution in some cases, but Galapagos should at least find a good solution if it exists, even
though it might not be the best possible solution. As shown in Figure 4-13, Galapagos was
connected to three variables of the louver system. However, the main alteration primarily
explored is the independent louver tilt angle.

This definition is intended to be used as a design tool for exploring various skin configurations
and testing them against daylighting performance. Despite the simplified algorithm, data
processing passes through performance criteria for numerical evaluation. Also, the complexity
of the definition is in its openness to the addition of more layers, such as more rotation sliders,
geometry alteration, or more evaluation criteria for different daylight performance indicators.

Though the algorithm is complete, it has not been run for testing, due to technical problems
with the DIVA component. According to the developers, DIVA 1.1 does not allow writing nodes
data for illuminance and showing it in Grasshopper. This limitation puts the success of the
design tool in a “pending” status until the release of a modified version of DIVA which has this
problem fixed; the release date was unknown when writing this thesis. However, to fully
illustrate the idea and concept, Chapter 5 shows some manual simulations for various skin
configurations and their impact on daylighting performance inside the space. For certain dates

103

and times, a series of skin configuration is selected to show the pattern of actuation over the
course of a day for enhanced luminous environment.

104

5. CHAPTER 5 DAYLIGHTING SIMULATION METHOD
Due to technical difficulties experienced with testing the algorithmic design tool, manual
simulation for some skin configurations is presented in this chapter. The chapter provides
various scenarios for the louver configurations for select times of the year. Finally, a scenario for
a specific day is compiled to show how an intelligent-dynamic louver system would actuate in
response to changing conditions.

105

5.1. OVERVIEW
After finishing the algorithm, which was intended to test an intelligent dynamic louver system
for daylight harvesting performance, the simulation run was not successful due to some
technical difficulties with the DIVA 1.1 component scripting, which does not allow extracting
illuminance node values from DIVA/Rhino. This problem made it difficult to provide node values
for evaluation, which consequently eliminated the possibility of running Galapagos to find an
optimal solution for skin configuration for a specific time of the year.

Instead of only documenting the parametric algorithm (Chapter 4), it is useful to manually
simulate some louver configurations, analyse the results, and compile one scenario for a kinetic
louver system on a certain day of the year. The objective of this study is to provide measured
illuminances based on direct and diffuse outdoor illumination under different sky conditions and
numerous settings of the secondary skin of the kinetic louver system. For this manual run, some
variables have been set constant, which contradicts the approach of the algorithm to
automatically run numerous probabilities for the skin configuration under various conditions.
The same design conditions assumed in the algorithm were used for the manual runs.

106

5.2. SIMULATION CONDITIONS
5.2.1 LOCATION, DATE & TIME
Los Angeles, California, was set as the location for the simulation, based on its high ranking in
the list of American cities with most sunny days. Los Angeles is number 10, with 186
84
sunny
days annually; this is approximately 51% of the whole year. This number is an average based on
readings over 30 years.

Los Angeles is at latitude 34.05: N (Figure 5-1) and longitude 118.25: W. The solar altitude
changes at different times of the day, and throughout the year; it ranges from 12: to 83: for the
selected test times. Figure 5-2 shows the solar altitude at different times of the day on June 21
st
,
March-September 21
st
, and December 21
st
(the summer solstice, equinox, and winter solstice,
respectively). The solar times chosen for this simulation were 9am and 12pm, when changing
solar altitude takes place. Though the altitude changes after 12pm, the afternoon times were
not considered because they show a mirroring of the morning altitudes (Figure 5-1). The
simulation was run twice for each date and time, once with a clear sky, and once with an
overcast sky, to test the performance of the dynamic louver system under different sky
conditions.

The office space used in this study is assumed to be in a high-rise building, and on an upper
floor, where no ground reflectance exists. While a proper simulation model should take into

84
“Weather Today - Weather Forecasts, Radar, Maps for 1000s of US and World Cities.”

107

account the surrounding context, this model does not account for neighboring buildings. Hence,
only indirect illumination from the space and the louvers is considered.

Figure 5-1: Space dimensions - the dimensions of the space used in the simulation.
85

5.2.2 SPACE DIMENSIONS & MATERIALS
A space 6.00m wide, 7.50m deep, and 3.00m high was modeled for this study (Figure 5-2). The
louvers are 0.60m deep, 6.00m wide, and set 1.00m from the glazing, to allow for a catwalk. The
distance between the louvers was set to 1.30m, which allows partial overlapping in nearly
closed shading/harvesting conditions. The depth of the space goes beyond the traditional
distance of twice the window height, which is one of the performance criteria, as previously

85
Schiler, Simplified Design of Building Lighting, 97.

108

mentioned in Chapter 3. The calculation grid was placed at 0.70m from finish floor level.
Material selection was based on basic generic materials provided by DIVA. Floors, walls, ceiling,
and louvers were assigned reflectances of 20%, 50%, 80%, and 80%, respectively (Figure 5-3).

Figure 5-2: Modeled Space - conceptual image of the space and its dimensions.

109

Figure 5-3: Materials selection - a snapshot of the “assign materials” window in DIVA for Rhino.
5.2.3 RADIANCE SETTINGS
DIVA for Rhino has a user-friendly interface, which one uses to choose the type of lighting test,
sky condition, solar date and time, and desired illuminance range. Within the context of this
study, the “lighting test” was set to “illuminance values,” for the calculation of illuminance
nodes evenly distributed over the working plane. The sky condition was set to either “sunny CIE
clear sky with sun” or “cloudy CIE overcast sky” (Figure 5-4). Solar dates and times used in the
study were June 21
st
at 9:00am (06 21 09), June 21
st
at 12:00pm (06 21 12), Dec 21
st
at 9:00am
(12 21 09), Dec 21
st
at 12:00pm (12 21 12), March 21
st
at 9:00am (03 21 09), and March 21
st
at

110

12:00pm (03 21 12). The range of desired illuminance was set to 300-1500
86
lux, according to
the IES recommended range for office spaces. Any value smaller than 300 lux or greater than
1500 lux was highlighted in the simulation results in the form of a percentage.

Figure 5-4: Simulation parameters - the settings used for the simulations.

The same advanced radiance parameters were used for all simulation runs. These parameters
assertively impact the accuracy and simulation time of raytracing calculations. Figure 5-5 shows
the radiance parameters used for the study. “Ambient bounces” represents the number of

86
Bradshaw, The Building Environment, 259.

111

diffuse bounces computed by the indirect calculation. A value of zero implies no indirect
calculation.
87
Though it consumes more time for calculation, a value of 5 ambient bounces was
used, which denotes the consideration of the maximum amount of indirect calculation.

Figure 5-5: Radiance parameters - the settings used for “Radiance” parameters.
5.3. SIMULATION METHOD
There are infinite possibilities for the combined skin configuration of the intelligent-kinetic
louver system, since the proposed system depends on independent angle control, where each
louver may have its own tilt angle. Therefore, the best approach for this study is to use
parametric software that automatically generates as many possibilities as the designer desires.
But for manual simulation, it is extremely time consuming to adjust each louver to a specific tilt
angle. Accordingly, some skin configurations have been defined based on previous research in
the field.

87
“Radiance rpict program.”

112

In 2005, Molly McGuire explored the independent blind angle control for venetian blinds, and its
impact on ceiling illuminance
88
. Using a physical modeling approach, she was able to establish
conclusions for light-reflection on the upper surface of venetian blinds. In her research, she
presented three equations for three variables: incident angle, reflected angle, and blind tilt-
angle. These equations are useful in determining the reflected angle, which consequently gives
hints about where the light is going into the space (Figures 5-6 and 5-7). The first equation in
Figure 5-6 gives the redirected (exit) angle as a function of the depth of the space and the height
of the window. The second equation in the same figure expresses the redirected angle as a
function of incident solar angle and louver tilt angle.

Figure 5-6: Redirected angle for single configuration - the equations by Molly McGuire for redirected angle
calculation for systems with either harvesting or shading setting.
89

For example, if daylighting is necessary at a depth of 10.00m in a space which has a window
opening of height 3.20m, using the first equation in Figure 5-6, the desired redirected angle “α”
of the rays is 17.70:. By substituting this value in the second equation, assuming the incident

88
McGuire, “A system for optimizing interior daylight distribution...”
89
Ibid.

113

solar angle is 62: in June at 10:00am, the louver tilt angle should be -22.15:. This angle is
measured from the horizontal reference guide counterclockwise (from the right hand side), and
this explains the negative sign that refers to the shading position.
.
Figure 5-7: Redirected angle for combined configuration - the equations by Molly McGuire for redirected angle
calculation for systems with harvesting and shading settings.
90

For the purposes of this thesis, the value of “ Ɵ” was revised, as shown in Figure 5-8, to measure
the angle from the indoor side of the horizontal plane to the tilted louver counterclockwise. This
makes it easier to differentiate between shading and harvesting positions, where shading
position angles range from 0: to 90:, and harvesting position angles range from 90: to 180:
(Figure 5-8).

90
Ibid.

114

Figure 5-8: Revised angles diagram - the revised value of “ Ɵ” which is used in this study.

Within the framework of this study, eight louvers in a secondary skin layer were placed at a
distance of 1.00m in front of the glazing, which is a typical distance for a catwalk. These louvers
were split into two independent layers of actuation, as shown in Figure 5-9.

Figure 5-9: Louver Configuration - the actual space used in the study with split louver system as a secondary skin.

115

The distance between the louvers was set to 1.30m, to allow for partial overlapping in semi-
closed positions. Overlapping at high-tilt angles ensures multi-surface light deflection, where
light is bounced off the upper surface of the louver, hits the lower surface of the upper louver,
and exits into the space. This phenomenon occurs at any tilt angle, but does not work with high-
tilt angles with no overlapping distance.

Figure 5-10: Semi-closed condition - partial overlapping in semi-closed condition.
91

In her research, McGuire generated a table of angles of combined schemes of harvesting and
shading, for different incident solar angles. The table shows the optimal configurations for
incident solar angles ranging from 10: to 80: (Figure 5-11); the table applies to this study since
solar angles experienced in Los Angeles range from 18: to 79:.

91
Ibid.

116

Figure 5-11: Optimal tilt angles - the optimal configuration for various solar angles.
92

While McGuire's research identifies an optimal tilt angle for each solar altitude, each tilt angle
combination selected for simulation in this study was run for three different altitudes, at 9:00am
and 12:00pm, and under clear and overcast sky conditions, for June 21
st
, March 21
st
, and
December 21
st
. Figure 5-12 shows a list of tilt angle combinations for the simulation runs. Each
configuration, excluding the first three in the figure, is followed by two tilt angles, where each

92
Ibid.

117

represents the tilt angle of one layer of the system; one layer has every other louver at the same
tilt angle. Three out of the thirteen configurations are used for referencing and comparative
analysis; these cases are “glazing only”, “lightshelf”, and “horizontal louvers”. Then, a series of
shading, harvesting and combined configurations were run and tested for daylighting harvesting
and three main performance criteria, as previously mentioned in Chapter 3.

118

Skin Configuration Angle Notes
Glazing only Not Applicable Simulation run with glazing only.
Lightshelf 0: Simulation run with lightshelf only.
Horizontal Louvers 0: Simulation run with static horizontal
louvers.
Shading 0:, 45: Simulation run with louvers in shading
configuration. 10:, 45:
35:, 55:
Harvesting 145:, 160: Simulation run with louvers in harvesting
configuration. 153:, 153:
Combined 24:, 156: Simulation run with combined
configuration of shading and harvesting. 26:, 163:
55:, 168:
170:, 10:
Top-Lower Split 26:, 168: Simulation run with split configuration of
harvesting for upper part and shading for
lower part.
Figure 5-12: Simulation Cases - the various simulation tilt angles for the louver system.

Simulating each angle combination for two sky conditions and 6 different times, results in 156
figures, all of which can be located in Appendix A.

119

Figure 5-13: Visual presentation of tilt angles - all tilt angles used in the simulation.
5.4. UNDERSTANDING SIMULATION RESULTS
This section describes an illuminance node analysis sample. As shown in Figure 5-14, the space
was divided into a 6x6 grid, resulting in 36 evaluation nodes. DIVA shows illuminance values in
lux; dividing each value by approximately 10 gives the illuminance value in footcandles. The
graphical scale shows a value range from 300 to 1500 lux, based on the performance criteria
(see Chapter 3), where 300 lux is blue and gradually changes to become red, indicating 1500 lux.
Also, this tool provides the “mean illuminance” of each run, which is calculated by adding
together all values and dividing the result by 36, the total number of nodes enclosed by the
space.

120

Figure 5-14: Illuminance nodes results sample - a typical output sample of DIVA node point analysis.

According to the performance criteria described in Chapter 3, an acceptable luminous
environment requires at least 75% of the nodes to fall within the IES recommended illuminance
range, 300-1500 lux
93
. Hence, an acceptable case should have no more than 25% of its area

93
Bradshaw, The Building Environment, 259.

121

above 1500 lux or below 300 lux (Figure 5-14). However, the analysis was developed based on
bringing the percentage of acceptable node-points as close as possible to 75%.

122

5.5. VISUALIZATION OF SIMULATED SCHEMES

Figure 5-15: Front view of simulated schemes - a visualization of simulated schemes.

123

Figure 5-16: Perspective view of simulated schemes - a visualization of simulated schemes in the same order as
previous figure.

124

6. CHAPTER 6 SIMULATION ANALYSIS

The actuation of external louvers influences the quality of the luminous environment inside the
office space. Each combined configuration of the louver system impacts the quantity and quality
of daylight. This chapter provides analysis of select case results that were generated by DIVA-in-
Rhino, based on different secondary skin configurations.

125

In this study, assessment and analysis of secondary skin performance, in terms of daylighting, is
based on illuminance values with which illumination, luminous distribution, and depth of
penetration can be evaluated. Using DIVA-in-Rhino, 156 node-analysis diagrams (see Appendix
A) were generated for different dates, times, and sky conditions. The results diagrams showed
variation in the quality of the luminous indoor environment. Some cases showed interesting
results which merit highlighting and analysis. This chapter discusses the data collected and the
results of these select simulation cases, which were obtained by the method discussed in the
previous chapter.

The various tilt angles simulated in this chapter include shading, harvesting, and combined
configurations. The combined configuration, as previously mentioned, is a system where every
other louver has different tilt angle. Figure 6-1 shows a graphical explanation of how the
different angles are incorporated into a single façade system.

Figure 6-1: Independent tilt-angle system - incorporating two different angles into one façade system.

126

6.1. DATA ANALYSIS METHOD
6.1.1 DIAGRAMS AND CHARTS
Data analysis consists of evaluating the secondary louver skin layer for daylighting performance.
As previously mentioned in Chapter 3, the evaluation method assesses the quality and quantity
of daylight inside the office space in terms of illuminance variation, luminous distribution, and
depth of daylight penetration. The method of analyzing and interpreting data involves
quantitative evaluation of the behavior of daylight inside the space, using node analysis grids –
generated by DIVA – and illuminance charts produced based on these grids. Results are
presented by comparing the test case to the pre-defined performance criteria and two base
cases for skin configuration, using lightshelf and horizontal louvers.

127

Figure 6-2: Chart values selection sample - the column of values used for chart plotting.

After running the different simulation cases, approximately 92% of the 156 results figures
showed a left (west)/right (east) mirroring of values. Although not exact, the lack of variation
between the values of both sides is due to the incident angle which, in most of the clear sky
cases, hits the panel and exits into the space in a similar way from both sides. However,
similarity in this context does not denote any duplication; it means that the results from both
sides are relatively close to each other. The difference between two similar nodes on different
sides ranges from 5 to 500 lux. Such differences are significant for 9:00am simulation runs, when
the sun rises from the east side, providing more illumination to the left-hand side of the space.

128

That being said, the third column from the left-hand side has been chosen to provide values for
the analysis charts (Figures 6-2 and 6-3).

Figure 6-3 presents a sample chart for illumination level analysis. Each point on the chart
corresponds to one node value in the DIVA results diagram, specifically in the third column from
the left-hand side. The vertical axis represents the illuminance value in lux, while the horizontal
axis represents the node distance from the window, which ranges from 0.50m to 7.50m. The
blue dashed lines enclose the range of desirable illumination values, according to IES.

Figure 6-3: Chart sample - plotting illuminance values from previous figure.
6.1.2 KINETIC SCENARIO COMPILATION METHOD
The main objective of this thesis is to provide an extensible design algorithm for integrating
daylighting performance into kinetic façade design. Thus, providing a manual compilation for a
Illuminance Value,
0.5, 2901
Illuminance Value,
1.5, 2045
Illuminance Value,
3, 1423
Illuminance Value,
4.5, 1017
Illuminance Value,
6, 774
Illuminance Value,
7.5, 630
Illuminance Value (Lux)
Distance from window

129

proposed kinetic scenario is useful and helpful for the reader to visualize the kinetic process in
response to varying attributes.

Within the context of this study, louvers actuate in separate two-tilt-angle layers, to optimize
the performance of daylighting inside the space. Over the course of a day, the secondary skin
experiences different tilt-angle combinations to maintain adequate illuminance and light
distribution inside the space. At the end of this chapter, the node-analysis diagrams are used to
compile a kinetic scenario for different solar altitudes, based on those for June 21st and
December 21st, at 9:00am and 12:00pm. This scenario provides an actuation pattern selected
from the previously tested configurations for two different days.
6.2. SELECT CASE ANALYSIS UNDER CLEAR SKY
It is obvious that a south-facing space with glazing only will not maintain an optimal luminous
environment, due to numerous reasons, among which are high illumination under clear sky
conditions, and glare from direct illumination. Though this case shows high illuminance,
regardless of the exact values, a base reference case is presented for comparative study
purposes. But the useful reference cases here are those with lightshelf and external horizontal
louvers, and not the case with full glazing.

In the first case analysis, two charts will be shown for each of the lightshelf and horizontal louver
cases. One chart will show the overall illumination, accommodating the 36 nodes within the
chart boundaries; the other is zoomed in and shows only the nodes falling under 5000 lux. The

130

purpose of the zoomed chart and its narrower illuminance scale is to highlight the differences
between node values. In the larger scale plot, these lines overlap.

As previously mentioned in Chapter 3, the performance criteria require that at least 75% of the
surface area falls within the desired illuminance range. Thus, the contrast ratio is also based on
the highest and lowest values within the 75% area. In this study, 75% represents 27 nodes.
6.2.1 LIGHTSHELF AND HORIZONTAL LOUVERS
A lightshelf does not necessarily yield more light at the back of the space, but blocks direct
illumination at the front, which creates more even distribution.
94
The lightshelf used in the
simulation was positioned at a vertical distance of 2.00m from floor level. It is 6.00m wide and
1.00m deep. The reflectance of the lightshelf was set to 90%. As shown in Figure 6-5, using the
lightshelf as a daylighting optimization approach results in an undesirable interior luminous
condition, where only 38% of the different times fall within the recommended range.

94
Schiler, Simplified Design of Building Lighting, 91.

131

Figure 6-4: Lightshelf illuminance results - illuminance as a result of applying lightshelf.

Figure 6-5: Zoomed lightshelf illuminance results - illuminance as a result of applying lightshelf.

132

At 3.00m from the window, the readings show a spike that reads 28,504 lux (Figure 6-4). The
assumed explanation is that light of higher incident angle is reflected on the lightshelf upper
surface, hits the ceiling at a short distance from the window, and then falls on the work plane.
Also, the lightshelf condition results in a contrast ratio of approximately 35 between the highest
and lowest illuminance values, which is not acceptable according to IES, or even the larger ratio
of NRC; it goes beyond both recommendations.

Horizontal louvers are expected to block direct illumination at the front of the space, and also to
redirect light deeper into the space. Replacing the lightshelf with a series of horizontal louvers
impacts the quality and quantity of daylighting in the room. A noticeable change in the behavior
of light is a more even distribution, and fewer high-value nodes (Figure 6-6). This change in
external skin configuration results in better illumination than the lightshelf case; about 58% of
the different times fall within the recommended range (Figure 6-7). Illumination nodes close to
the window have smaller readings than those in the lightshelf case, which means that the
horizontal louvers are more effective in blocking direct incident illumination at the front of the
space.

133

Figure 6-6: Zoomed horizontal louvers illuminance results - illuminance as a result of applying horizontal louvers.

Figure 6-7: Horizontal louvers illuminance results - illuminance as a result of applying horizontal louvers.

134

With this skin configuration, the sectional nodes chart shows an even distribution in illumination
levels along the cut line (Figure 6-6). Though horizontal louvers are effective in cutting down
daylight in the room, they are not effective in bringing the illumination values into the desired
range. As seen in Figure 6-7, the front of the space experiences high illumination at 9:00am on
December 21
st
, due to a low solar altitude: 18: at this day and time. Compared to the lightshelf
case, a spike at the front of the space is more acceptable than one in the middle, so it does not
disturb the function of the space (Figures 6-4 and 6-6); however, this also depends on the
furniture distribution inside the space. If the furniture is close to the window, it will not be
considered an acceptable result, and vice versa.

6.2.2 SHADING CONFIGURATION 35⁰ AND 55⁰
This case shows the performance of the secondary skin in shading configuration. The louvers do
not have the same tilt angle. Rather, they are divided into two layers, with two different tilt
angles on alternate louvers, 35: and 55:. This skin configuration was able to bring 61% of the
times into a desirable illumination range. Moreover, this configuration showed successful results
at 9:00am on December 21
st
and at 9:00am on June 21
st
; 71.4% and 77.2%, respectively, of the
entire surface fell within the recommended illumination range (Appendix A). Though these
values are not exactly 75% or higher, they are still close to the desired percentage.

135

Figure 6-8: Shading configuration illuminance results - illuminance as a result of applying shading configuration of 35:
and 55:.

This skin configuration resulted in a contrast ratio of approximately 10%, which according to IES
is acceptable for decent space performance (Figure 6-8). A contrast ratio below 10 is preferred
to maintain even distribution and eliminate false darkness perception, where the occupant
incorrectly perceives certain spots as dark due to his exposure to extremely high illumination in
other spots. There is no zoomed-in chart for this configuration, since all node values already fall
below 5000 lux.
6.2.3 COMBINED CONFIGURATION 24⁰ AND 156⁰
Within the framework of this study, angles below 90: indicate a shading configuration, while
angles greater than 90: denote a harvesting configuration. In this case, the skin configuration is
50% in harvesting configuration, at 156:, and 50% in shading configuration, at 24:.

136

Figure 6-9: Combined configuration illuminance results - illuminance as a result of applying combined configuration of
24: and 156:.

This simulation run showed approximately 42% of the results falling within the recommended
illumination range (Figure 6-9). If we compare this configuration against the horizontal louvers,
we see that the horizontal louvers have better performance, with approximately 16% more
nodes within range. However, this configuration brings more light into the middle of the space
(Figure 6-10).

137

Figure 6-10: Zoomed combined configuration illuminance results - illuminance as a result of applying combined
configuration of 24: and 156:.

A behavior that merits highlighting happens on December 21
st
at 12:00pm and December 21
st
at
9:00am. Beyond 4.50m, a steady daylight illumination takes place, running close to the upper
boundaries of the recommended range. This interesting behavior presents the potential success
of this configuration during those times, if a steady and even distribution is desired by the
occupants at the back of the space, regardless of the hotspots at middle of the space. For full
node-analysis values and diagrams, refer to Appendix A.

The most interesting part of this run is the contradictory behavior of daylighting inside the space
on both times discussed previously, when the behavior of the light in the front half of the space
is significantly different than that at the back of the space. The observed conclusion about this
scheme is the good performance of the skin beyond 4.50m, reaching the last node at the back of
the space. Also, Figure 6-10 shows great potential for this configuration to bring light within the

138

desired range to space even beyond 7.50m. This behavior fits the demand for intelligence in
kinetic façades. For example, this configuration may be used if the space sensors detect that
only the back of the space is occupied, or that occupants working at the back are executing less
critical tasks that do not require high illumination. However, the contrast ration is higher than
desired by IES; it is 13.50, which is acceptable according to National Research Council of Canada
(NRC).
6.2.4 COMBINED CONFIGURATION 26⁰ AND 163⁰
On June 21
st
, at 9:00am, this skin configuration was able to achieve illumination of 82.9% of the
entire room surface area within the recommended range (Appendix A); only the front 1.50m
falls out of range. This makes the entire space, except the front strip, optimal for occupancy with
acceptable luminous conditions. The skin shows a successful attempt to provide daylighting
deep into the space within the acceptable range (Figure 6-11).

139

Figure 6-11: Combined configuration illuminance results - illuminance as a result of applying combined configuration
of 26: and 163:.

If we are to evaluate the luminous distribution (contrast ratio), we will consider all times except
the December 21
st
9:00am result, when the readings showed high values at the front of the
space, unlike the majority of the other runs. The contrast ratio of the highest to lowest values in
the space is 6.25 which is acceptable according to the IES recommendation (Figure 6-11).

140

Figure 6-12: Zoomed combined configuration illuminance results - illuminance as a result of applying combined
configuration of 26: and 163:.
6.3. OVERCAST SKY CONDITION DISCUSSION
An overcast sky condition is three times brighter at the zenith than the horizon, since water
particles refract and reflect all wavelengths of sunlight.
95
The calculation of daylight factors is
usually done with an overcast sky condition, since daylight factors do not account for direct
illumination. For this study, illuminance simulations were run under overcast sky conditions to
see if the algorithm provides the potential to find an optimal solution for specific skin
configurations.

Given that direct illumination does not accompany overcast sky conditions, the performance of
horizontal louvers is expected to be limited. However, since overcast skies are possible in the

95
Ander, Daylighting performance and design, 85.

141

real world, three different skin configurations were tested for daylighting performance. The
interesting observed behavior in daylighting at all times for the three configurations is that more
than 75% of the nodes fell within the desired illuminance range for the cases shown in Figures 6-
13 and 6-14. Changing the configuration from one to another impacted the indoor luminous
environment in terms of illuminance values and distribution.

The harvesting configuration shows approximately 78% of the times within range, with a
contrast ratio of approximately 5. However, this configuration does not enhance illumination at
the back of the space, where below-range levels are experienced at certain times. It is more
successful in the middle of the space (Figure 6-13). It is obvious that the illumination range
under an overcast sky is smaller than under a clear sky.

Figure 6-13: Harvesting configuration illuminance results - illuminance as a result of applying harvesting configuration
of 145: and 160:.

142

The skin configuration was changed to 10: and 70: for another simulation test run. Compared to
the harvesting configuration, this configuration maintained almost the same illumination level at
the back of the space, while decreasing illumination at the front of the space, bringing it into a
more desirable level (Figure 6-14). Approximately 81% of the times fall within the desired range,
and the contrast ratio is maintained at 5; though if we include 100% of the nodes, the contrast
ratio is 12% - which is still close to 10%. This scheme shows successful illumination from the
front to the middle of the space. On June 21
st
at 12:00pm, 77.2% of the entire space nodes fall
within range, which makes this configuration a potential solution for the kinetic skin actuation at
this time.

Figure 6-14: Combined configuration illuminance results - illuminance as a result of applying combined configuration
of 10: and 170:.

Switching from a combined configuration, where every other louver has the same tilt angle, to
the combined-split scheme, where the upper half of the skin has the same tilt angle, and the
bottom half has a different angle, resulted in minor changes in the illumination levels. These

143

changes are within the desired range (Figure 6-15). While the combined-split scheme is
expected to block light at the bottom of the space, and to bounce light into the back of the
space through the upper portion of the skin, the results do not show major variation from the
previous combined scheme. While the results do not show major variation, 26 nodes fall within
the recommended range, giving an acceptable percentage of 72.2%. This may have been a
consequence of the spacing between the louvers, the size of the louvers, or the offset distance
of the secondary skin from the glazing; thus, these factors should be studied at a future stage.
This configuration might have proven efficient when applied on smaller elements like venetian
blinds.

If analysis is to be provided for the overcast sky condition, it should focus on the ineffectiveness
of light-deflection elements to influence the behavior of the luminous environment inside
indoor spaces. The absence of direct illumination in an overcast sky condition is the main reason
for the ineffectiveness of the system; less light is bounced off the surfaces of the louvers,
resulting in less illumination values inside the space. This is a very common interpretation in the
field of daylighting for overcast sky conditions.
96
In Appendices A and B, more node-analysis
diagrams and sectional illuminance results are shown.

96
Schiler, Simplified Design of Building Lighting.

144

Figure 6-15: Combined-split configuration illuminance results - illuminance as a result of applying combined-split
configuration of 162: and 28:.
6.4. KINETIC SCENARIO FOR ACTUATION PATTERN
Given the main objective of this thesis, showing a proposed actuation pattern for the secondary
skin is important. The proposed configurations are not limited to the analyzed ones, but include
all simulated schemes (Appendix A). Though the compilation of this scenario may not be
successful at this point, due to the limited number of simulation runs, it would be helpful to
provide an idea about how the kinetic system is intended to operate. For a complete scenario,
the algorithm-based definition should be used as was originally intended.

145

Figure 6-16: Compiled scenario for clear sky - the best configuration selected from simulated cases.

Figures 6-16 and 6-17 present a proposed scenario for the best skin performance available from
the simulation cases run for this study. They do not necessarily represent the optimal solution,
but the best available selection from the manual simulation cases. The algorithm should be able
to automatically generate numerous configuration iterations and provide the designer with an
optimal one.

146

Figure 6-17: Compiled scenario for overcast sky - the best configuration selected from simulated cases.

Each scheme is accompanied by a “performance” value underneath. This value represents the
percentage of illuminance nodes, on the entire surface area, that fall within the recommended
range. While the proposed performance percentage in this study is 75%, some of the manually
simulated cases did not achieve this percentage, but were close. However, there is potential for
finding at least one optimal configuration for each specific date and time.
6.5. ANALYSIS CONCLUSION
In conclusion, a chart has been developed to show the percentage of nodes that fall within the
recommended range for each configuration at different times of the year (Table 6-1). The

147

configuration of the secondary skin with different tilt angle has infinite possibilities; thus, the
use of parametric software is more efficient in determining possible configurations than doing
so manually. Table 6-1 shows the selected configuration for the manual simulation runs. It does
not mean that the kinetic skin will use these cases only for actuation; it is a limited study due to
the endless possibilities and time limitations. Thus, if some of the cases didn’t show successful
results, it does not mean that the study fails. It is just about running more possibilities and
finding the best among all of them.

Table 6-1: Performance percentage table - the percentage of nodes falling within recommended illumination range.

The table shows the effectiveness of the configurations used for simulation during high solar
altitude, especially on June 21
st
. The blue-highlighted values are greater than 70%, while the
grey-highlighted values are greater than 60%. If we are to consider one configuration, as a static
system, for the simulated space, the space will experience inefficiency in the luminous
environment over the course of the year, because one configuration cannot enhance daylighting
9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm
37.10 20.00 74.30 60.00 54.30 42.90 42.90 42.80 48.60 54.30 42.90 48.50
37.10 22.90 68.60 54.30 57.10 48.60 34.30 48.60 54.30 60.00 57.10 51.50
40.00 31.40 85.70 71.40 51.40 45.70 14.30 37.10 51.40 62.90 31.40 51.40
51.40 48.60 82.90 65.70 62.90 48.60 22.90 40.00 57.10 60.00 42.90 60.00
57.10 48.60 82.90 65.70 65.70 54.30 13.30 31.40 48.60 57.20 37.10 48.60
71.40 62.90 77.20 65.70 68.60 62.90 14.30 25.70 42.90 48.60 28.60 42.90
25.70 2.90 82.90 65.70 34.30 20.00 42.90 62.80 65.80 65.70 54.30 62.90
25.70 2.90 82.90 65.70 34.30 8.60 48.60 62.80 60.00 68.50 54.30 68.60
34.30 20.00 82.90 65.70 51.40 37.10 31.40 51.40 71.40 77.20 51.40 68.60
42.90 31.40 82.90 65.70 54.30 48.60 31.40 51.40 62.80 71.50 57.10 62.80
42.90 74.30 82.90 65.70 57.10 48.60 31.40 48.60 65.70 77.20 54.30 74.30
51.40 42.90 77.10 54.30 57.10 48.60 31.40 57.10 57.10 57.10 51.40 71.40
34.30 20.00 82.90 54.30 51.40 42.90 37.10 57.10 77.10 82.90 62.90 71.40
Harvesting 153, 153
December 21st June 21st March 21st December 21st
Top-Lower Split 162, 28
Combined 55, 168
Combined 26, 163
Combined 24, 156
Combined 10, 170
Percentage of points within acceptable illumination range
Secondary Skin
Configuration
Harvesting 145, 160
Shading 35, 55
Shading 10, 45
Shading 0, 45
Horizontal Louvers
Lightshelf
Overcast Sky Clear Sky
Glazing Only
June 21st March 21st

148

at all times. It may be efficient during certain periods and altitudes, but not during others. For
example, the combined 26-163 configuration works best on December 21
st
at 12:00pm and June
21
st
at 9:00am, under a clear sky, and June 21
st
at 12:00pm and March 21
st
at 12:00pm, under
overcast sky conditions. We can make use of this configuration during these times and find
better configurations for the other times.

Moreover, a static system may not be efficient for changing externally and internally reflected
components. A kinetic system is expected to actuate in response to any of these changes, to
enhance the luminous environment. Whether kinetic or static, the system will not show
effectiveness in low solar altitudes, where horizontal louvers are not effective in blocking low
sunlight. Thus, the geometry of the skin should be considered as part of the future work; there
may be a possible skin transformation that is capable of blocking high-angle and low-angle
sunlight.

149

7. CHAPTER 7 CONCLUSION AND FUTURE WORK
This chapter provides a summary of the work presented in this thesis and the concluding
discussion based on the proposed algorithm and simulation runs, as well as presenting possible
future work for the development of this thesis topic.

150

7.1. CONCLUSION
Daylight deflection is about redirecting light into a space or back to the outdoor environment. In
real life, shading elements are closed during high solar gain, which results in dark interior spaces
and the use of artificial lighting. This waste of energy can be overcome by incorporating a
combined scheme of shading and harvesting in one skin configuration. Using both strategies in a
combined scheme is possible, and may result in more efficient and better daylighting
performance inside office spaces. The hypothesis presented in this thesis, stating that “one way
to achieve appropriate daylight harvesting is through the use of advanced daylight deflection
strategies involving an intelligent kinetic façade,” has been proven in its initial stage, which
showed the potential of the tool’s success by running manual simulation of select tilt angles.

A study of the simulation of secondary skin configurations and their impact on daylighting
performance inside office spaces was conducted. The study was split into two sections: an
algorithm-based design tool for designers and a manual simulation that provided the reader
with a brief introduction of how the tool is intended to work. This thesis hypothesizes that the
use of light-deflection techniques in intelligent kinetic façades may enhance the luminous
indoor environment and adapt to changing environmental conditions and occupants’ needs,
though not all factors were simulated in the context of the work. Since only a limited number of
different skin configurations were simulated, the hypothesis was not completely proven correct,
but initial results show that it is likely to be correct. The success of the system is dependent on
running hundreds of configurations using a genetic algorithm tool - in this study, Galapagos.

151

However, the success of certain configurations, at certain times of the year, reflects the
potential for the success of this hypothesis, considering that the façade is intended to actuate
and change configuration according to different times of the year.

The external skin actuates to optimize daylight-deflection, maintaining a desirable luminous
indoor environment at certain times of the year, as previously shown in Chapter 6 for some
cases. The louvers rotate using the concept of independent tilt-angle, where every other louver
has the same tilt angle. The louvers could be configured for harvesting, shading, or use a
combined configuration. When skin configuration changes, due to louver actuation, the
algorithm is designed so that DIVA detects the alteration and instantly reflects it onto a
calculation grid inside the space. This allows the designer to run numerous iterations, during the
design stage, and select the best possible one based on pre-defined criteria.

A genetic algorithm was incorporated into the definition to enable a search of the best skin
configurations at specific dates and times or under different sky conditions. Genetic algorithms
work to find an optimal solution – but not necessarily the best solution – under certain
parameters and conditions. These parameters could range from users’ desired illumination
levels, to externally-reflected daylighting components. In this study, one actuation parameter
and three performance indicators for daylighting were defined. Occupants’ behaviors were
indirectly incorporated into the algorithm through the ability to change the range of acceptable
illumination, which denotes changing occupant tasks that could be automatically detected by
intelligent space sensors and cause re-adjustment of the desired range based on the task being
performed.

152

The proposed algorithm is extensible. It is open to additional parameters and performance
indicators, which makes it more complex for better performance assessment. Changes in any of
these parameters trigger the system to run and find an optimal configuration for the skin, in
order to maintain the desired luminous environment. Due to technical difficulties experienced
with the DIVA component in importing node values into Grasshopper, manual simulation was
implemented for select configurations, based on previous research work
97
in similar areas.

DIVA was used as the simulation tool for the manual study, using “Radiance” as its calculation
engine. DIVA, using Daysim, which runs in the background for calculation, has already been
verified against another tool, 3ds Max Design, for accuracy and reliability, and it was found that
both tools produce similar results for sidelit spaces
98
. Running this tool for manual simulation of
the modeled space gave various results for different times of the year. None of the simulated
skin configurations showed acceptable results on all simulated dates. Each configuration
showed effectiveness, based on the thesis’s criteria, at certain times and dates, some of which
were under clear sky conditions and some of which were under overcast sky conditions. Since
the purpose of the study was to find at least one time for each configuration when it is
successful in bringing daylighting levels into a desirable range, so that this configuration could
be used in a kinetic scenario for that time, this behavior would be acceptable.

97
McGuire, “A system for optimizing interior daylight distribution...”
98
Reinhart and Breton, “Experimental Validation of Autodesk (R) 3ds Max (R) Design 2009 and Daysim
3.0.”

153

Among the simulated configurations, only one scheme showed the potential to handle very low
solar altitudes under clear sky conditions. Scheme “shading 35°, 55°” gave the result of 71.4% of
nodes falling within the desired range on December 21st, at 9:00am, with a solar altitude of 18°.
This means that different skin geometries should be tested for better handling of lower angles,
or more skin iterations should be generated and tested. Most of the other configurations were
successful under the same sky condition, on June 21st, at 9:00am, with a solar altitude of 50°,
when most of them were above 75%.

The analysis was extended to evaluate the luminous distribution of illumination levels inside the
space. This provided a daylighting contrast ratio. Most of the runs at different times showed a
contrast ratio less than 10%, taking into consideration that the compared values were within the
lowest 75% of the entire space. Generally, many cases showed 5% as a common approximate
value for contrast ratio, with some exceptions going beyond the desired 10%. The average
contrast ratio for the simulated cases was 8.3%, based on the 75% surface area. Future work
could include a more meaningful overview and research about glare and how to activate a
façade to provide useful illuminance levels while avoiding both high contrast of light in the
interior space and direct glare.

Overcast sky conditions without a direct illumination component were also considered in the
simulation. The secondary skin systems provided almost the same number of times when the
louvers were able to successfully bring illumination into the appropriate range. While most
cases showed only a couple of successful times, the space showed smooth blending of
illumination levels across the entire area, and the highest values shown were close to the

154

highest desired. Some cases did not show successful illuminance values, but did show a
successful desired contrast ratio. Under this sky condition, altering the skin configuration was
found to be slightly effective, which highlights the absence of the direct illumination
component. Thus, the successful different times of the year had almost the same distribution, as
illustrated in Chapter 6.

This study shows that kinetic façades have a greater potential to enhance the indoor luminous
environment than static façades. In Figure 7-1, a comparison between a kinetic scenario and
fixed façade tilt angle is presented. While the actuation of the louvers impacts the performance
of daylighting inside the space, and brings it into the desired performance range, applying a
fixed tilt angle to the louvers results in poor daylighting performance compared to the kinetic
case. This example shows the effectiveness of the “combined 55, 168” scheme on June 21st, at
9:00am, and its inefficiency at the other three times. This is the disadvantage of static systems:
they cannot adapt to changing solar conditions.

155

Figure 7-1: Static versus kinetic - results of the performance of a kinetic façade versus a static façade over four
different times of the year.

7.1.1 SCOPE AND LIMITATIONS
The science of daylighting assessment includes many different attributes and not just those
discussed in the previous chapters: illumination levels, luminous distribution (contrast ratio),
and the penetration depth of daylighting into the back of the space. Due to time limitations,
only these three attributes were studied, thus narrowing down the scope of this thesis to
evaluating the illuminance value inside the space, and comparing the nodes to each other to
discuss the luminous distribution and penetration depth.

156

Four major limitations are discussed below: technical difficulties with the software, the lack of
modeling of the external building surfaces, the absence of realistic occupant behavior modeling,
and the absence of furniture inside the space.

A number of limitations were experienced over the course of this thesis, among which was the
data exchange between DIVA-in-Rhino and the Grasshopper component. DIVA 1.1 was unable to
extract the illuminance node values and bring them back into Grasshopper. This limited its
effectiveness for the performance criteria evaluation as the simulation could not then use the
genetic algorithm component to find an optimal solution for a specific date and time.
Theoretically, the algorithm should be run properly to find solutions for specific design
problems. The results had to be manually extracted to test the algorithm, as shown in Chapter 4.
Rather than running the algorithm through the genetic algorithm component, a manual
simulation had to be run for select configurations, based on previous work in similar areas of
study
99
. The manual simulation involved running 13 different configurations for 6 different times
of the year under clear and overcast sky conditions. This technique had its drawbacks, and the
selected configurations are not necessarily what the algorithm would have picked as optimal
solutions. There could be better solutions that have not been simulated within the framework of
this study. Thus, running more skin iterations would be a useful step if done as future work. A
better solution, however, is to wait until the next release of DIVA when that limitation might be
fixed.

99
McGuire, “A system for optimizing interior daylight distribution...”

157

A major element of the urban surrounding context is the external building surfaces. These
surfaces have certain reflectances and light reflection properties. Given that the simulation has
been run on a generic office space, the calculation does not account for surrounding context,
which could have impacted the indoor luminous environment depending on the location of the
office. This limitation could have changed the behavior of daylighting inside the space by adding
another layer of calculation, the externally reflected daylight component.

Moreover, this thesis addresses the integration of intelligent features into the building façade.
Such features are intended to be used for occupant behavior detection and to make the façade
react with consideration for this behavior. The proposed tool does not integrate directly an
occupant model, due to the limitation of scripting in this area; however, the tool integrates an
indirect approach for taking into account one attribute of human behavior. The possibility of
changing the desired illuminance range according to the tasks performed inside the space
symbolizes the detection by sensors of changing occupant activities and tasks, requiring
different acceptable illumination ranges.

Different actuation schemes impact the performance of daylighting inside the space. Some
configurations maintain better illumination at the back of the space than the front area and vice
versa. Accordingly, deciding on the best solutions should take into account the furniture layout,
which has not been taken into consideration in this study. Depending on the location of the
workplanes inside the space, the secondary skin will actuate to optimize the quality and quantity
of daylighting in these areas. For example, if the architect decides that furniture will be located

158

closer to the window, the louvers will actuate to bring the front portion of the space into the
desirable range of illumination and luminous distribution.
7.2. FUTURE WORK
The proposed algorithm is designed to assess the performance of the secondary skin in
optimizing illumination, luminous distribution, and depth of penetration, but it does not take
into account many other factors, among which are glare, heat gain, different space function,
skin geometry, and surrounding urban context. Figure 7-3 shows the suggested additions for
future development of the algorithm. These include occupant behavior, glare remediation, heat
gain and thermal comfort, skin geometry, and surrounding urban context.
7.2.1 OCCUPANTS’ BEHAVIOR
The behavior of occupants is an independent topic on its own; however, some layers of the
topic could be incorporated into the proposed design algorithm. While the algorithm
incorporates a simplified symbolization of occupant behavior, as previously mentioned in
section 2.2, it does not integrate more complex layers of human behavior, such as detecting
changing tasks and working moods through facial recognition techniques. Integrating this
attribute into the algorithm represents an important step in the development of this area of
study; it could be one step to better performing architectural spaces. In a user empowered
version, he might be able to change the performance specifications of the system to suit his
work location, work time, and preferred illuminance levels to enable a different set of louver
activation.

159

Carniege Mellon Robotics Institute has an established research center called “People Image
Analysis Consortium” that researches human behavior and facial recognition
100
. The image
below shows the architecture of detecting human behavior and facial emotions. The institute’s
website provides an example and coding for different techniques of behavior detection. A
possible way of integrating this into the proposed algorithm is using one of the designed codes
provided by Carniege Mellon as a reference for developing a similar technique using
Grasshopper scripting, maybe with the aid of a third interface for human agent and behavior
detection.

Figure 7-2: Architecture for human behavior detection system - the architecture of detecting human behavior and
facial emotions.
101

100
“PIA Carnegie Mellon Robotics Institute.”
101
Ibid.

160

7.2.2 GLARE REMEDIATION
Glare is a crucial problem in architectural spaces such as offices, hospitals and museums. Simply,
it is caused by direct light falling on the workplane – whether vertical, like computers, or
horizontal, like desks – or hitting the occupant’s eyes. Accounting for glare in office spaces, in
addition to the three performance indicators explored in this study, would make this design tool
much more useful. Calculation and assessment of glare conditions inside the space may be
incorporated through using DIVA and glare calculation equations. Defining performance criteria
for glare that can be expressed in the form of parametric calculation components is necessary
for the execution of this development. Although this is more complex than calculating
illuminance levels, it is possible to predict potential glare locations, suggest mediation before
the building is constructed, or provide an activation pattern for the louvers that results in a few
number of glare incidents.
7.2.3 HEAT GAIN AND THERMAL COMFORT
Allowing sunlight to penetrate into architectural spaces is sometimes good for illumination and
energy-saving, if appropriately controlled according to desired preferences. However, direct
sunlight always carries heat that affects the thermal comfort inside the space. This study does
not take into consideration heat gain and thermal comfort. It would be more valuable to
optimize daylighting and thermal comfort inside the space using the same algorithm-based
design tool.
7.2.4 SKIN GEOMETRY
Within the framework of this study, the actuation of the louvers in rotational motion is tested
for daylighting performance. But this is not the only parameter that can enhance the quality of

161

the indoor luminous environment. Changing the geometry of the secondary skin, the louvers’
shape, and the actuation pattern, may have impact on the performance of daylight harvesting.
This parameter, specifically, adds not only to the performance of the space, but also to the
aesthetic aspect of architecture, which makes it an exciting potential attribute for future
exploration. Varying geometry parameters would allow architects to produce numerous
iterations for skin design and find an optimal design that matches the aesthetic requirements
and the desired performance of the luminous environment.
7.2.5 SURROUNDING URBAN CONTEXT
Adding a layer of surrounding urban context to the simulation model is important, especially in
architecturally-dense cities, like Los Angeles, where every project is affected by neighboring
building on an urban and architectural scale.

162

Figure 7-3: Algorithm logic for future work - additions to the extensible algorithmic design tool as future development
for the investigated topic.
7.3. SUMMARY
In conclusion, a high integration of design and research flowing between architects,
computational designers, and consultants is important to achieve innovation and efficiency.
Communicating to the designer the importance of integrating performance-based approaches at
the early design stage, and their impact on the design, may shift the logic of executing an
architectural project. The integration of daylighting factors into the design phase, through
design tools and computation, results in the improved performance of daylight harvesting and
therefore tackles issues of human comfort and energy efficiency. Taking into consideration

163

human behavior in the simulation will enhance the performance of an architectural space in
terms of energy-consumption.

Assessing the performance of intelligent systems in buildings does not have an absolute
benchmark; it is a relative evaluation process that depends on the occupants’ needs and
preferences, and the function of the space. Accordingly, the presence of occupants-model
during simulation is necessary for efficiently investigating the topic. Simulating such models in
the study expands the layers of investigation and results in more complex algorithm; however, it
is important to the development of the performance-based design area in architecture.

People involved in the design and construction process can have different views on the
performance of the space; the occupant is the one who will judge the success of the indoor
environment. Hence, this idea emphasizes the need for incorporating human agents into such
studies for early consideration of human behavior at the design stage. A more complex
development for the model may include programming codes for occupants’ behavior by
research centers, like “Carniege Mellon Robotics Institute”; it is not the only institute that
researches this topic, but one of the known ones that has many studies in this area.

This thesis presented an integration of three different tools, Rhino, Grasshopper, and DIVA, used
to develop one algorithm that is intended to be used for optimal tilt angle search. The technical
difficulties experienced resulted in carrying out manual simulation runs for the search for an
optimal scheme. Though some of the simulated schemes showed success at certain times of the
year, manual simulation is not an efficient way to find an optimal solution given the infinite

164

possibilities of skin configurations. Solving the DIVA component scripting problem will prove the
efficiency of the system in a more reliable and acceptable discussion.

Moreover, intelligent skins are controversial; they may present solutions for better energy-
performing architecture, but they cost a lot of money and can be difficult to maintain. If
research keeps exploring the performance of these kinds of skins independently without
comparing them to feasibility models for cost and development, intelligent skins may not be
applicable in the real architecture world. This area requires effort and input from many
disciplines other than architecture; among which are business and feasibility, mechanical,
electronics, material science, and physics. The integration of inputs from all previous disciplines
may result in a system that is cost effective and has a short payback period. However, the
architect should be a dominant element in the design process to support new trends in form
geometry such as complex geometry and make sure other disciplines are not changing the
architecture to make their lives easier. We need to support changing architecture trends rather
than narrowing it down to cube and linear geometry only.

165

BIBLIOGRAPHY
Asefi, Maziar. Transformable and Kinetic Architectural Structures: Design, Evaluation
and Application to Intelligent Architecture. VDM Verlag Dr. Müller, 2010.

Atkin, Brian. Intelligent Buildings: Applications of It and Building Automation to High
Technology Construction Projects. Ashgate Publishing, 1993.

Baird, George. The Architectural Expression of Environmental Control Systems. Taylor
& Francis, 2001.

Baker, Nick V., A. Fanchiotti, and Koen Steemers. Daylighting in Architecture: A
European Reference Book. Earthscan Publications Ltd., 1993.

Baker, Nick, and Koen Steemers. Daylight Design of Buildings. Annotated edition.
Earthscan Publications Ltd., 2002.

Becker, Eike. Headquarters Building of Verbundnetz Gas Ag Leipzig. Illustrated edition.
Prestel Publishing, 2000.

Bernaden, John A. The Intelligent Building Sourcebook. Prentice Hall, 1988.

Boubekri, Mohamed. Daylighting, Architecture and Health: Building Design Strategies.
Architectural Press, 2008.

Bradshaw, Vaughn. The Building Environment: Active and Passive Control Systems. 3rd
ed. Wiley, 2006.

Brambley, M.R., P. Haves, and S.C. McDonald. Advanced Sensors and Controls for
Building Applications: Market Assessment and Potential R&D Pathways, April
2005. http://apps1.eere.energy.gov/buildings/publications/pdfs/corporate/pnnl-
15149_market_assessment.pdf.

―Brise soleil - Wikipedia, the free encyclopedia‖, n.d.
http://en.wikipedia.org/wiki/Brise_soleil.

California Energy Commission. Daylight Harvesting Made Simple. California, USA,
September 2008. http://www.esource.com/esource/getpub/public/pdf/cec/CEC-
TB-36_Daylighting.pdf.

Carlson, Reinhold A., and Robert A. DiGiandomenico. Understanding Building
Automation Systems. R.S. Means Company, Inc., 1991.

166

Clements-Croome, Professor Derek. Intelligent Buildings: Design Management and
Operation. Thomas Telford Publishing, 2004.

Compagno, Andrea. Intelligente Glasfassaden / Intelligent Glass Façades: Material,
Anwendung, Gestaltung / Material, Practice, Design. 5th ed. Birkhäuser Basel,
2002.

―Curtain wall - Wikipedia, the free encyclopedia‖, n.d.
http://en.wikipedia.org/wiki/Curtain_wall.

Dasgupta, Ujjaini. The Impact of Windows on Mood and Performance of Judgmental
Tasks. Rensselaer Polytechnic Institute, 2003.

DiLouie, Craig. Advanced lighting controls: energy savings, productivity, technology and
applications. The Fairmont Press, Inc., 2006.

―DIVA for Rhino - Credits‖, n.d. http://www.diva-for-rhino.com/credits.html.

―DIVA for Rhino Website‖, 2011. http://www.diva-for-rhino.com/.

―D-LITE: Database of Light-Interacting Technologies for Envelopes.‖ D-LITE ABOUT,
n.d. http://www.d-lite.org/.

Dubois, Marie-Claude. ―Impact of Solar Shading Devices on Daylight Quality:
Measurements in Experimental Office‖. Sweden: Lund University, 2001.
http://www.grap.arc.ulaval.ca/attaches/Dubois/BOK-3061.PDF.

Edwards, Brian. Green Buildings Pay. 2nd ed. Taylor & Francis, 2003.

Encelium Technologies. ―Lighting Energy Management System: Six Smart Strategies‖,
n.d. www.encelium.com.

Fathy, Hassan. Natural Energy and Vernacular Architecture: Principles and Examples
with Reference to Hot Arid Climates. University Of Chicago Press, 1986.

Flagge, Ingeborg, Verena Herzog-Loibl, and Anna Meseure. Thomas Herzog:
Architecture and Technology. Bilingual. Prestel, 2002.

―FLARE-facade‖, n.d. http://www.flare-facade.com/.

Fontoynont, Marc. Daylight Performance of Buildings. Illustrated edition. Earthscan
Publications Ltd., 1999.

167

Fox, Michael, and Miles Kemp. Interactive Architecture. 1st ed. Princeton Architectural
Press, 2009.

Galasiu, A.D., G.R. Newsham, C. Suvagau, and D.M. Sander. ―Energy saving lighting
control systems for open-plan offices: a field study.‖ Leukos 4, no. 1 (July 1,
2007): 7-29.

Government of Canada. National Research Council Canada. Institute for Research in
Construction. ―Daylight-linked Lighting Control Systems - Projects - NRC-
CNRC‖, n.d. http://www.nrc-cnrc.gc.ca/eng/projects/irc/daylighting.html.

Grimmer, D.P., K.G. Zinn, K.C. Herr, and B.E. Wood. ―Augmented solar energy
collection using different types of planar reflective surfaces; theoretical
calculations and experimental results.‖ Solar Energy 21, no. 6 (1978): 497-501.

Guzowski, Mary. Daylighting for Sustainable Design. 1st ed. McGraw-Hill Professional,
1999.

Hansanuwat, Ryan. ―Kinetic facades as environmental control systems: using kinetic
facades to increase energy efficiency and building performance in office
buildings‖, 2010.

Hauer, Erwin. Erwin Hauer: Continua-Architectural Screen and Walls. Princeton
Architectural Press, 2007.

Hensel, Michael, Achim Menges, and Michael Weinstock. Emergent Technologies and
Design: Towards a Biological Paradigm for Architecture. Routledge, 2010.

Hindrichs, Dirk U., and Winfried Heusler. Fassaden / Façades: Gebäudehüllen für das
21. Jahrhundert / Building Envelopes for the 21st Century. 2nd ed. Birkhäuser
(Princeton Architectural Press), 2005.

Howlett, O., C. Eaton, R. Raghavan, and R. P. Leslie. ―The potential of simplified
concepts for daylight harvesting.‖ Lighting Research & Technology 37, no. 1
(March 1, 2005): 21-38.

Hu, Jia, and Svetlana Olbina. ―Illuminance-based Slat Angle Selection Model for
Automated Control of Split Blinds.‖ Building and Environment (October 20,
2010).

Hunter, C M, C C Jones, P R Boyce, M. Myer, J A Veitch, J. Heerwagen, and G R
Newsham. ―Occupant use of switching and dimming controls in offices.‖ Lighting
Research & Technology 38, no. 4 (December 1, 2006): 358-376.

168

―iBuilding Website‖, n.d. http://www.ibuilding.gr/definitions.html.

IES North America. IESNA Lighting Handbook. 9th ed. Illuminating Engineering Society
of North Ame, 2000.

―Improving the Energy Performance of Buildings.‖ Product Page, 2009.
http://www.rand.org/pubs/technical_reports/TR728.html.

ISO/CIE. ISO/CIE 8995-1:2002, Lighting of indoor work places - Part 1: Indoor.
Multiple. Distributed through American National Standards Institute (ANSI),
2007.

Kenzari, Bechir, and Yasser Elsheshtawy. ―The Ambiguous Veil: On Transparency, the
Mashrabiy’ya, and Architecture.‖ Journal of Architectural Education 56, no. 4
(2003): 17-25.

Knaack, Ulrich, Tillmann Klein, Marcel Bilow, and Thomas Auer. Facades: Principles
of Construction. 1st ed. Birkhäuser Architecture, 2007.

Koster, Helmut. Dynamic Daylighting Architecture: Basics, Systems, Projects. Springer
Verlag, 2004.

Kronenburg, Robert. Portable Architecture: Design and Technology. 1st ed. Birkhäuser
Architecture, 2008.

Kwok, Alison, and Walter Grondzik. The Green Studio Handbook: Environmental
Strategies for Schematic Design. 1st ed. Architectural Press, 2006.

Lechner, Norbert. Heating, Cooling, Lighting: Sustainable Design Methods for
Architects. 3rd ed. Wiley, 2008.

Lee, E.S., and S.E. Selkowitz. ―The New York Times Headquarters daylighting mockup:
Monitored performance of the daylighting control system.‖ Energy and Buildings
38, no. 7 (July 2006): 914-929.

―Lighting Control System - Encelium’s Energy Control System (ECS)‖, n.d.
http://www.encelium.com/.

―Liverpool Architecture‖, n.d.
http://www.liverpoolarchitecture.com/tours/buildings/building.php?id=25.

Lovell, Jenny. Building Envelopes: An Integrated Approach. Princeton Architectural
Press, 2010.

169

Marmot, Alexi, and Joanna Eley. Office space planning: designing for tomorrow’s
workplace. McGraw-Hill Professional, 2000.

―Mashrabiya - Wikipedia, the free encyclopedia‖, n.d.
http://en.wikipedia.org/wiki/Mashrabiya.

McDonough, William, and Michael Braungart. Cradle to Cradle: Remaking the Way We
Make Things. 1st ed. North Point Press, 2002.

McGuire, Molly E. ―A system for optimizing interior daylight distribution using
reflective Venetian blinds with independent blind angle control‖. Massachusetts
Institute of Technology, 2005.

Melendo, Jose Manuel Almodovar, Jose Maria Cabeza Lainez, and Juan Ramon Jimenez
Verdejo. ―Nineteen Thirties Architecture for Tropical Countries: Le Corbusier′s
Brise–Soleil at the Ministry of Education in Rio de Janeiro.‖ Journal of Asian
Architecture and Building Engineering 7, no. 1 (2008): 9-14.

Michael, Simms, and Gerber David. ―SOM + SCI-Arc on CF:Responsive Kinetic
Facade‖, n.d. http://www.core.form-ula.com/2009/04/15/som-sci-arc-on-
cfresponsive-kinetic-facade/.

Moloney, Jules. ―Building Skins as Kinetic Process: Some Precedent from the Fine Arts‖.
Sydney, Australia, 2007. http://hdl.handle.net/2100/484.

Moore, T. ―Harvesting daylight inside buildings.‖ EPRI JOURNAL 23, no. 4 (July 1,
1998): 18-25.

Murray, Scott. Contemporary Curtain Wall Architecture. 1st ed. Princeton Architectural
Press, 2009.

National Research Council Canada. ―Lighting Design for Open-Plan Offices‖, n.d.
http://www.nrc-cnrc.gc.ca/eng/ibp/irc/ctus/ctus-n62.html.

Nixon, Paddy, Gerard Lacey, Simon Dobson, and University of Dublin Trinity College.
Managing Interactions in Smart Environments. Springer, 2000.
O’Connor, Jennifer. Tips for daylighting with windows: the integrated approach. Ernest
Orlando Lawrence Berkeley National Laboratory, 1997.
http://windows.lbl.gov/daylighting/designguide/dlg.pdf.

O’Neill, Brendan. ―Model-based control of venetian blinds‖, 2008.

―People Image Analysis (PIA) Consortium, Carnegie Mellon Robotics Institute‖, n.d.
http://www.consortium.ri.cmu.edu/.

170

Petersen, Marianne Graves. ―Interactive spaces: towards a better everyday?‖ interactions,
July 2005.

Phillips, Derek. Daylighting: Natural Light in Architecture. 1st ed. Architectural Press,
2004.

―Radiance rpict program‖, n.d. http://radsite.lbl.gov/radiance/man_html/rpict.1.html.

Reinhart, C., and P. F. Breton. ―Experimental Validation of Autodesk (R) 3ds Max (R)
Design 2009 and Daysim 3.0.‖ LEUKOS 6, no. 1 (July 1, 2009): 7-35.

―Robotics Institute: Agent-based Composition of Behavioral Models‖, n.d.
http://www.ri.cmu.edu/research_project_detail.html?project_id=551&menu_id=2
61.

Rosenfeld, Arthur H., and Stephen E. Selkowitz. ―Beam daylighting: an alternative
illumination technique.‖ Energy and Buildings 1, no. 1 (May 1977): 43-50.

Santamouris, Mat. Advances in Building Energy Research: Volume 2. Earthscan
Publications Ltd., 2008.

Sauerbruch, Matthias, Louisa Hutton, Isabelle Hartmann, and Sauerbruch Hutton
Architekten. GSW Headquarters Berlin: Sauerbruch Hutton Architects. 1st ed.
Lars Müller Publishers, 2000.

Schiler, Marc. Simplified Design of Building Lighting. Wiley-Interscience, 1992.

Schiler, Marc(first). Daylight Harvesting. Southern California Gas Company, 1995.

Schmalzel, J., F. Figueroa, J. Morris, S. Mandayam, and R. Polikar. ―An Architecture for
Intelligent Systems Based on Smart Sensors.‖ IEEE Transactions on
Instrumentation and Measurement 54, no. 4 (2005): 1612-1616.

―Sennari House In Cairo: Home to Napoleon’s Scholars‖, n.d.
http://www.touregypt.net/featurestories/sennarihouse.htm.

Soldner, J. G. v, J. G. v. ―On the deflection of a light ray from its rectilinear motion, by
the attraction of a celestial body at which it nearly passes by.‖ Berliner
Astronomisches Jahrbuch (1804): 161–172.

―The light side of gravity — Einstein Online‖, n.d. http://www.einstein-
online.info/elementary/generalRT/Light_deflection.

171

―Thiele AG - Transparente Innovation‖, n.d. http://www.thiele-
ag.de/en/management.html.

Tregenza, Peter, and Michael Wilson. Daylighting: Architecture and Lighting Design. 1st
ed. Routledge, 2011.

Trumpler, R. J. ―The Relativity Deflection of Light.‖ Publications of the Astronomical
Society of the Pacific 41 (February 1, 1929): 23.

Veitch, J. A. ―Psychological processes influencing lighting quality.‖ Journal of the
Illuminating Engineering Society 30, no. 1 (April 1, 2001): 124.

Veitch, J.A. Determinants of Lighting Quality I: State of the Science - NRC-IRC.
Cleveland, OH: Annual Conference of the Illuminating Engineering Society of
North America, n.d. http://www.nrc-cnrc.gc.ca/obj/irc/doc/pubs/nrcc39866.php.

Wang, Shengwei. Intelligent Buildings and Building Automation. T & F Books UK,
2009.

―Weather Today - Weather Forecasts, Radar, Maps for 1000s of US and World Cities‖,
n.d. http://www.weathertoday.net/weatherfacts/numbersunny_bycity.php.

Wigginton, Michael, and Jude Harris. Intelligent Skins. Architectural Press, 2002.

APPENDIX A: ILLUMINANCE NODE POINT RESULTS

Figure 9-1: Glazing only results - The figure shows the illuminance node values inside the space.
172

Figure 9-2: Glazing only results - The figure shows the illuminance node values inside the space.
173

Figure 9-3: Glazing only results - The figure shows the illuminance node values inside the space.
174

Figure 9-4: Glazing only results - The figure shows the illuminance node values inside the space.
175

Figure 9-5: Glazing only results - The figure shows the illuminance node values inside the space.
176

Figure 9-6: Glazing only results - The figure shows the illuminance node values inside the space.
177

Figure 9-7: Lightshelf results - The figure shows the illuminance node values inside the space.
178

Figure 9-8: Lightshelf results - The figure shows the illuminance node values inside the space.
179

Figure 9-9: Lightshelf results - The figure shows the illuminance node values inside the space.
180

Figure 9-10: Lightshelf results - The figure shows the illuminance node values inside the space.
181

Figure 9-11: Lightshelf results - The figure shows the illuminance node values inside the space.
182

Figure 9-12: Lightshelf results - The figure shows the illuminance node values inside the space.
183

Figure 9-13: Horizontal louvers results - The figure shows the illuminance node values inside the space.
184

Figure 9-14: Horizontal louvers results - The figure shows the illuminance node values inside the space.
185

Figure 9-15: Horizontal louvers results - The figure shows the illuminance node values inside the space.
186

Figure 9-16: Horizontal louvers results - The figure shows the illuminance node values inside the space.
187

Figure 9-17: Horizontal louvers results - The figure shows the illuminance node values inside the space.
188

Figure 9-18: Horizontal louvers results - The figure shows the illuminance node values inside the space.
189

Figure 9-19: Shading 0° and 45° results - The figure shows the illuminance node values inside the space.
190

Figure 9-20: Shading 0° and 45° results - The figure shows the illuminance node values inside the space.
191

Figure 9-21: Shading 0° and 45° results - The figure shows the illuminance node values inside the space.
192

Figure 9-22: Shading 0° and 45° results - The figure shows the illuminance node values inside the space.
193

Figure 9-23: Shading 0° and 45° results - The figure shows the illuminance node values inside the space.
194

Figure 9-24: Shading 0° and 45° results - The figure shows the illuminance node values inside the space.
195

Figure 9-25: Shading 10° and 45° results - The figure shows the illuminance node values inside the space.
196

Figure 9-26: Shading 10° and 45° results - The figure shows the illuminance node values inside the space.
197

Figure 9-27: Shading 10° and 45° results - The figure shows the illuminance node values inside the space.
198

Figure 9-28: Shading 10° and 45° results - The figure shows the illuminance node values inside the space.
199

Figure 9-29: Shading 10° and 45° results - The figure shows the illuminance node values inside the space.
200

Figure 9-30: Shading 10° and 45° results - The figure shows the illuminance node values inside the space.
201

Figure 9-31: Shading 35° and 55° results - The figure shows the illuminance node values inside the space.
202

Figure 9-32: Shading 35° and 55° results - The figure shows the illuminance node values inside the space. 203

Figure 9-33: Shading 35° and 55° results - The figure shows the illuminance node values inside the space. 204

Figure 9-34: Shading 35° and 55° results - The figure shows the illuminance node values inside the space.
205

Figure 9-35: Shading 35° and 55° results - The figure shows the illuminance node values inside the space. 206

Figure 9-36: Shading 35° and 55° results - The figure shows the illuminance node values inside the space.
207

Figure 9-37: Harvesting 145° and 160° results - The figure shows the illuminance node values inside the space.
208

Figure 9-38: Harvesting 145° and 160° results - The figure shows the illuminance node values inside the space.
209

Figure 9-39: Harvesting 145° and 160° results - The figure shows the illuminance node values inside the space.
210

Figure 9-40: Harvesting 145° and 160° results - The figure shows the illuminance node values inside the space.
211

Figure 9-41: Harvesting 145° and 160° results - The figure shows the illuminance node values inside the space.
212

Figure 9-42: Harvesting 145° and 160° results - The figure shows the illuminance node values inside the space.
213

Figure 9-43: Harvesting 153° and 153° results - The figure shows the illuminance node values inside the space.
214

Figure 9-44: Harvesting 153° and 153° results - The figure shows the illuminance node values inside the space.
215

Figure 9-45: Harvesting 153° and 153° results - The figure shows the illuminance node values inside the space.
216

Figure 9-46: Harvesting 153° and 153° results - The figure shows the illuminance node values inside the space. 217

Figure 9-47: Harvesting 153° and 153° results - The figure shows the illuminance node values inside the space.
218

Figure 9-48: Harvesting 153° and 153° results - The figure shows the illuminance node values inside the space.
219

Figure 9-48: Combined 10° and 170° results - The figure shows the illuminance node values inside the space.
220

Figure 9-49: Combined 10° and 170° results - The figure shows the illuminance node values inside the space.
221

Figure 9-50: Combined 10° and 170° results - The figure shows the illuminance node values inside the space.
222

Figure 9-51: Combined 10° and 170° results - The figure shows the illuminance node values inside the space.
223

Figure 9-52: Combined 10° and 170° results - The figure shows the illuminance node values inside the space.
224

Figure 9-53: Combined 10° and 170° results - The figure shows the illuminance node values inside the space.
225

Figure 9-54: Combined 24° and 156° results - The figure shows the illuminance node values inside the space.
226

Figure 9-55: Combined 24° and 156° results - The figure shows the illuminance node values inside the space. 227

Figure 9-56: Combined 24° and 156° results - The figure shows the illuminance node values inside the space.
228

Figure 9-57: Combined 24° and 156° results - The figure shows the illuminance node values inside the space.
229

Figure 9-58: Combined 24° and 156° results - The figure shows the illuminance node values inside the space.
230

Figure 9-59: Combined 24° and 156° results - The figure shows the illuminance node values inside the space.
231

Figure 9-60: Combined 26° and 163° results - The figure shows the illuminance node values inside the space.
232

Figure 9-61: Combined 26° and 163° results - The figure shows the illuminance node values inside the space.
233

Figure 9-62: Combined 26° and 163° results - The figure shows the illuminance node values inside the space.
234

Figure 9-63: Combined 26° and 163° results - The figure shows the illuminance node values inside the space.
235

Figure 9-64: Combined 26° and 163° results - The figure shows the illuminance node values inside the space.
236

Figure 9-65: Combined 26° and 163° results - The figure shows the illuminance node values inside the space.
237

Figure 9-66: Combined 55° and 168° results - The figure shows the illuminance node values inside the space.
238

Figure 9-67: Combined 55° and 168° results - The figure shows the illuminance node values inside the space.
239

Figure 9-68: Combined 55° and 168° results - The figure shows the illuminance node values inside the space.
240

Figure 9-69: Combined 55° and 168° results - The figure shows the illuminance node values inside the space.
241

Figure 9-70: Combined 55° and 168° results - The figure shows the illuminance node values inside the space.
242

Figure 9-71: Combined 55° and 168° results - The figure shows the illuminance node values inside the space.
243

Figure 9-72: Top-lower split 162° and 28° results - The figure shows the illuminance node values inside the space.
244

Figure 9-73: Top-lower split 162° and 28° results - The figure shows the illuminance node values inside the space.
245

Figure 9-74: Top-lower split 162° and 28° results - The figure shows the illuminance node values inside the space.
246

Figure 9-75: Top-lower split 162° and 28° results - The figure shows the illuminance node values inside the space.
247

Figure 9-76: Top-lower split 162° and 28° results - The figure shows the illuminance node values inside the space.
248

Figure 9-77: Top-lower split 162° and 28° results - The figure shows the illuminance node values inside the space.
249

250

APPENDIX B: RESULTS FOR CHART PLOTTING

Table 10-1: Glazing only chart values - The table shows the illuminance values, in the middle of the space, used for
charts plotting.

Table 10-2: Lightshelf chart values - The table shows the illuminance values, in the middle of the space, used for
charts plotting.

Table 10-3: Horizontal louvers chart values - The table shows the illuminance values, in the middle of the space, used
for charts plotting.

9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm
0.50m 2345 3577 26772 44631 17067 30148 2729 3414 2055 2874 1212 1895
1.50m 1584 2379 2597 3301 16455 29490 1436 1851 1116 1530 669 1036
3.00m 1061 1569 1699 2230 15573 28656 787 987 591 847 344 558
4.50m 729 1061 1125 1531 1914 2367 472 587 332 472 212 315
6.00m 525 770 762 1067 1357 1773 289 373 221 320 127 206
7.50m 435 612 586 821 1060 1417 216 279 173 239 100 149
Glazing Only
Distance
from
Window
Clear Sky Overcast Sky
June 21st March 21st December 21st June 21st March 21st December 21st
9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm
0.50m 3130 4403 26725 44637 16624 29900 1807 2253 1375 1907 827 1322
1.50m 2131 2971 2593 3393 16122 3615 1240 1477 898 1244 533 775
3.00m 1383 1912 1662 2285 15379 28504 698 918 540 738 299 477
4.50m 892 1245 1086 1498 1777 2153 425 554 310 445 186 273
6.00m 615 850 736 1043 1241 1623 281 326 214 269 130 203
7.50m 472 659 569 770 995 1295 210 262 167 235 96 145
Lightshelf
Distance
from
Window
Clear Sky Overcast Sky
June 21st March 21st December 21st June 21st March 21st December 21st
9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm
0.50m 1550 2667 3751 4960 16539 5049 995 1268 751 1109 473 715
1.50m 1115 1806 2678 3457 15958 3857 638 764 456 610 273 433
3.00m 734 1186 1755 2334 2178 2773 400 475 272 449 155 341
4.50m 517 834 1161 1479 1596 1956 262 346 188 297 136 203
6.00m 391 614 746 1016 1145 1375 219 266 148 219 102 132
7.50m 328 510 573 751 841 1034 161 223 115 181 79 127 Horizontal Louvers
Distance
from
Window
Clear Sky Overcast Sky
June 21st March 21st December 21st June 21st March 21st December 21st

251

Table 10-4: Shading 0° and 45° chart values - The table shows the illuminance values, in the middle of the space, used
for charts plotting.

Table 10-5: Shading 10° and 45° chart values - The table shows the illuminance values, in the middle of the space,
used for charts plotting.

Table 10-6: Shading 35° and 55° chart values - The table shows the illuminance values, in the middle of the space,
used for charts plotting.

Table 10-1: Harvesting 145° and 160° chart values - The table shows the illuminance values, in the middle of the
space, used for charts plotting.

9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm
0.50m 1902 2849 3107 3853 3011 4213 1243 1560 923 1380 546 865
1.50m 1380 2053 2227 2848 2413 3065 710 945 593 773 319 498
3.00m 990 1450 1517 1900 1723 2053 469 610 334 490 211 335
4.50m 693 1052 1020 1284 1253 1469 313 369 249 344 140 217
6.00m 505 767 707 959 837 1062 217 314 179 243 105 169
7.50m 411 627 579 743 693 877 178 213 133 193 82 117 Shading 0: and 45:
Distance
from
Window
Clear Sky Overcast Sky
June 21st March 21st December 21st June 21st March 21st December 21st
9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm
0.50m 1788 2868 2919 3726 2958 4269 1202 1517 883 1238 549 767
1.50m 1301 2078 2144 2699 2248 2972 689 843 488 719 282 473
3.00m 925 1463 1440 1852 1572 1995 423 539 314 414 192 276
4.50m 668 1046 971 1247 1051 1304 276 362 215 286 124 195
6.00m 505 770 680 909 739 933 185 282 157 219 85 142
7.50m 406 609 553 691 616 787 150 194 127 174 78 123
Shading 10: and
45:
Distance
from
Window
Clear Sky Overcast Sky
June 21st March 21st December 21st June 21st March 21st December 21st
9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm
0.50m 1812 2664 2464 2990 2396 3167 1002 1325 803 1101 454 722
1.50m 1343 1911 1799 2184 1706 2184 532 624 410 577 252 377
3.00m 916 1295 1167 1503 1093 1528 307 397 234 341 144 214
4.50m 625 862 812 1013 747 1036 212 250 159 222 92 140
6.00m 443 619 552 680 542 661 132 168 104 150 60 96
7.50m 329 491 452 544 439 604 101 143 82 112 47 73
Shading 35: and
55:
Distance
from
Window
Clear Sky Overcast Sky
June 21st March 21st December 21st June 21st March 21st December 21st
9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm
0.50m 1935 3325 27716 46029 17174 4497 2198 2708 1665 2240 966 1496
1.50m 1254 2111 3461 4574 16501 29607 1443 1772 1066 1510 631 967
3.00m 849 1454 2410 3236 15666 28887 834 1047 662 882 371 611
4.50m 631 1026 1729 2438 2178 2554 510 669 410 530 238 378
6.00m 453 816 1296 1821 1560 1941 346 443 272 370 156 248
7.50m 383 654 1024 1462 1245 1603 290 354 207 299 130 212
Harvesting 145:
and 160:
Distance
from
Window
Clear Sky Overcast Sky
June 21st March 21st December 21st June 21st March 21st December 21st

252

Table 10-7: Harvesting 153° and 153° chart values - The table shows the illuminance values, in the middle of the
space, used for charts plotting.

Table 10-8: Combined 24° and 156° chart values - The table shows the illuminance values, in the middle of the space,
used for charts plotting.

Table 10-9: Combined 26° and 163° chart values - The table shows the illuminance values, in the middle of the space,
used for charts plotting.

Table 10-10: Combined 55° and 168° chart values - The table shows the illuminance values, in the middle of the space,
used for charts plotting.

9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm
0.50m 1991 3318 27791 6121 17010 4603 2153 2764 1638 2247 968 1490
1.50m 1295 2174 3573 4656 16600 29718 1449 1792 1075 1459 657 971
3.00m 873 1484 2498 3351 15751 28896 858 1087 649 910 367 589
4.50m 614 1061 1810 2461 2208 2666 530 665 412 584 230 386
6.00m 456 810 1372 1849 1548 2043 367 476 283 382 159 253
7.50m 405 682 1098 1501 1238 1725 281 335 200 293 123 201
Harvesting 153:
and 153:
Distance
from
Window
Clear Sky Overcast Sky
June 21st March 21st December 21st June 21st March 21st December 21st
9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm
0.50m 1913 2835 3408 4244 16018 3968 1563 1776 1146 1544 659 1020
1.50m 1345 1994 2481 3084 15623 3023 934 1144 785 1015 419 611
3.00m 910 1370 1649 2116 2155 27977 653 739 440 672 267 420
4.50m 649 974 1161 1482 1554 1809 393 512 320 430 190 316
6.00m 484 760 819 1069 1160 1379 291 378 215 324 127 205
7.50m 423 627 671 861 972 1201 236 292 179 247 100 160 Combined 24: and
156:
Distance
from
Window
Clear Sky Overcast Sky
June 21st March 21st December 21st June 21st March 21st December 21st
9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm
0.50m 1984 2901 3372 4304 16327 1887 1397 1867 1064 1555 680 995
1.50m 1414 2045 2480 3120 15806 1627 868 1068 674 996 422 619
3.00m 978 1423 1643 2116 2273 1201 624 762 435 764 289 431
4.50m 674 1017 1129 1475 1630 884 389 493 319 499 198 290
6.00m 539 774 807 1067 1145 710 291 380 203 344 152 200
7.50m 440 630 665 837 959 665 242 302 190 298 125 171 Combined 26: and
163:
Distance
from
Window
Clear Sky Overcast Sky
June 21st March 21st December 21st June 21st March 21st December 21st
9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm
0.50m 1945 3365 3341 4144 2971 3789 1255 1569 1019 1435 620 1031
1.50m 1475 2424 2471 3065 15271 2918 767 1024 648 907 382 647
3.00m 982 1628 1625 2096 1772 2140 504 657 471 602 261 442
4.50m 659 1135 1096 1408 1264 1540 363 444 302 398 177 330
6.00m 486 805 791 999 925 1193 243 299 218 314 139 231
7.50m 370 652 618 795 783 921 210 255 169 253 116 193 Combined 55: and
168:
Distance
from
Window
Clear Sky Overcast Sky
June 21st March 21st December 21st June 21st March 21st December 21st

253

Table 10-11: Combined 10° and 170° chart values - The table shows the illuminance values, in the middle of the space,
used for charts plotting.

Table 10-12: Top-lower split 162° and 28° chart values - The table shows the illuminance values, in the middle of the
space, used for charts plotting.

9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm
0.50m 1785 2933 3811 4895 16801 5038 1480 1854 1087 1567 639 1028
1.50m 1319 2087 2897 3627 3282 4018 970 1125 700 994 431 654
3.00m 938 1473 1944 2503 15412 3016 585 762 454 645 270 437
4.50m 681 1074 1318 1726 1812 2243 433 525 337 464 197 301
6.00m 526 820 948 1239 1397 1656 315 391 224 320 140 213
7.50m 464 684 742 995 1103 1350 264 309 194 272 116 182
Combined 170:
and 10:
Distance
from
Window
Clear Sky Overcast Sky
June 21st March 21st December 21st June 21st March 21st December 21st
9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm 9:00am 12:00pm
0.50m 1991 3309 27153 5265 17362 5456 1412 1800 1110 1539 651 1025
1.50m 1485 2338 2845 3597 3637 4183 1081 1342 768 1176 472 828
3.00m 1062 1613 1805 2316 15643 28635 682 964 576 749 322 499
4.50m 761 1163 1177 1558 1995 2266 472 574 346 504 206 317
6.00m 585 869 843 1113 1437 1732 329 433 252 343 153 240
7.50m 481 699 697 921 1195 1433 258 334 201 288 112 181
Combined-Split
162 : and 28:
Distance
from
Window
Clear Sky Overcast Sky
June 21st March 21st December 21st June 21st March 21st December 21st

Linked assets

Intelligent building skins: Parametric-based algorithm for kinetic facades design and daylighting performance integration

Conceptually similar

Zip

Intelligent building skins: Parametric-based algorithm for kinetic facades design and daylighting performance integration [program]

PDF

Kinetic facades as environmental control systems: using kinetic facades to increase energy efficiency and building performance in office buildings

PDF

The intelligent control strategy of kinetic façades for daylight and energy performance: evaluating the daylight effect of adaptive systems based on parametric workflow

PDF

Energy saving integrated facade: design and analysis using computer simulation

PDF

Daylighting study of a LEED platinum laboratory building: a post-occupancy evaluation comparing performance in use to design intent

PDF

Performative shading design: parametric based measurement of shading system configuration effectiveness and trends

XLS

Glass beam design for architects: brief introduction to the most critical factors of glass beams and easy computer tool [spreadsheet]

PDF

Structural design tool for performative building elements: a semi-automated Grasshopper plugin for design decision support of complex trusses

PDF

The acoustic performance of double-skin facades: a design support tool for architects

PDF

Environmental adaptive design: building performance analysis considering change

PDF

Evaluation of daylighting circadian effects: Integrating non-visual effects of lighting in the evaluation of daylighting designs

PDF

Comparative evaluation of lighting design software programs for daylighting in buildings

PDF

Glass beam design for architects: brief introduction to the most critical factors of glass beams and easy computer tool

PDF

Environmentally responsive buildings: multi-objective optimization workflow for daylight and thermal quality

PDF

Multi-domain assessment of a kinetic facade: determining the control strategy of a kinetic façade using BIM based on energy performance, daylighting, and occupants’ preferences; Multi-domain asse...

PDF

A step towards urban building information modeling: measuring design and field variables for an urban heat island analysis

PDF

Adaptive façade controls: A methodology based on occupant visual comfort preferences and cluster analysis

PDF

Biomimetic design of the building envelope: biological climate adaptations and thermal controls in the Sonoran Desert

PDF

Net zero energy building: the integration of design strategies and PVs for zero-energy consumption

PDF

Energy savings by using dynamic environmental controls in the cavity of double skin facades

Asset Metadata

Creator El Sheikh, Mohamed Mansour (author)

Core Title Intelligent building skins: Parametric-based algorithm for kinetic facades design and daylighting performance integration

School School of Architecture

Degree Master of Building Science

Degree Program Architecture

Degree Conferral Date 2011-05

Publication Date 04/27/2011

Defense Date 03/21/2011

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag adaptive facades,daylight harvesting,daylighting,design integration,design optioneering,interactive architecture,kinetic facades,OAI-PMH Harvest,parametric design,performative design,real-time feedback

Format 270 pages (extent)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Kensek, Karen (committee chair), Gerber, David (committee member), Miller, Nathan (committee member)

Creator Email elsheikh@usc.edu,mans@mansworks.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c127-15574

Unique identifier UC182732

Identifier usctheses-c127-15574 (legacy record id)

Legacy Identifier etd-Sheikh-4166

Dmrecord 15574

Document Type Thesis

Format 270 pages (extent)