University of Southern California Dissertations and Theses

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

(USC Thesis Other)

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

PDF

Download a page range

Download transcript

Copy asset link

Request this asset

Transcript (if available)

Content ENVIRONMENTALLY RESPONSIVE BUILDINGS:
Multi-objective Optimization Workflow for Daylight and Thermal Quality
By
Alejandro Alberto Gamas Villamil
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
August 2016
Copyright 2016 Alejandro Alberto Gamas Villamil
I
DEDICATION
I hereby dedicate this thesis to my parents,
a gesture in return for the gift of education you gave me.
Also through this work I would like to honor Fray Gabriel Chávez de la Mora,
who taught me the value of immaterial things,
and how architecture can have a soul when it is embedded with them.
II
ACKNOWLEDGEMENTS
If the purpose of graduate studies consist on the mastery of a particular domain of knowledge and the
unveiling of new frontiers of understanding through the examination and testing of existing and new
paradigms, then I would have to say that I succeeded in accomplishing the purpose of this thesis, not by
my merit alone, but by the input and guidance of those with whom I engaged with for this project.
I would like to express my sincere gratitude to Kyle Konis, whose expertise and input always paved the
route behind the development and progresses of this work. I appreciate your encouragement for
exploration, mentorship and openness. The experience of collaborating with you on this project is
something that I will refer to when collaborating with other people in future projects.
Karen Kensek is the person responsible for ensuring that the dots were always connected. I appreciate
your support in helping me look to the subject through different angles, in helping me tie up the loose ends
throughout the project. Also, thanks for your example of professionalism and authentic interest in
supporting my efforts.
I want to also express my gratitude to Douglas Noble, who help define the structure of this thesis. Your
sense of humor and broad knowledge make it easy to follow you. In addition I would like to acknowledge
Anders Carlson for spending time reviewing the project and providing feedback, Brendon Levitt for
providing valuable directions for the implementation of the thermal autonomy analysis, Brian Lockyear
for his guidance in regards to plug-ins development, and all other that in some way or another help in the
development of this thesis.
Last but not least I would like to thank the National Council of Science and Technology of Mexico
(CONACYT) and the School or Architecture of the USC for the financial support in form of scholarships.
III
EPIGRAPH
The sun is fundamental to all life. It is the source of our vision, our warmth, our energy, the rhythms and
rituals of our lives. Its movements inform our perceptions of time and space and our scale in the
universe. Assured access to sunshine is thus important to the quality of our lives.
Ralph Knowles
IV
ABSTRACT
Creating buildings that are responsive to the immediate environmental conditions, require the building
envelope to become a primary component for the mediation between outdoors and indoors. Fenestration
patterns and shading devices for interior daylighting and thermal comfort are critical elements to enhance
the capacity in which the building envelopes can be improved. However, despite a range of “rules of
thumb” and design “best practices” are available to guide fenestration design decision-making, how to
best apply them is often unclear when overlapped with urban constraints such as orientation,
overshadowing of adjacent buildings, and local climate.
A parametric workflow can facilitate the simulation of annual climate-based daylight and thermal
performances in early stages of design and retrofit scenarios within the environment of Grasshopper and
Rhino3D. The workflow includes a set of built-in components that encompass a conceptual model builder,
a window placer, and an automated shading calculator based on peak temperature climate data. It
implements a preliminary approach to determine and visualize the Thermal Autonomy (TA) of buildings
using the Adaptive Comfort Standard (ACS), building upon other partial frameworks it also provides
visualizations of yearly Useful Daylight Illuminance (UDI), and can perform multi-objective
optimizations of daylight versus thermal calculations with the plugin Octopus. By determining optimal
geometry for daylight aperture configurations and exterior shading elements across the façade, it acts as a
design tool for students, architects and engineers. The approach and its novel features are described in the
context of a hypothetical commercial building façade retrofit scenario located in downtown Los Angeles,
where the best improvements reached a total of 16% for daylighting, 48% for the thermal comfort.
V
TABLE OF CONTENTS
DEDICATION .................................................................................................................................... I
ACKNOWLEDGEMENTS ............................................................................................................... II
EPIGRAPH ...................................................................................................................................... III
ABSTRACT ...................................................................................................................................... IV
LIST OF FIGURES .......................................................................................................................... IX
LIST OF CHARTS ........................................................................................................................ XIV
CHAPTER 1. INTRODUCTION ....................................................................................................... 1
1.1 RESEARCH OBJECTIVE ....................................................................................................... 3
1.2 TOPIC EXPLANATION/ELABORATION: TERMS............................................................. 3
1.2.1 CONCEPTS AND TERMS FROM BUILDING SCIENCE .................................................. 3
1.2.2 COMPUTER AIDED DESIGN (CAD) CONCEPTS AND TERMS ..................................... 7
1.3 TOPIC EXPLANATION/ELABORATION: DOMAIN ........................................................ 11
1.3.1 GOALS/OBJECTIVES ....................................................................................................... 13
1.3.2 METRICS ........................................................................................................................... 14
1.3.3 SOFTWARE ....................................................................................................................... 17
1.3.4 WHAT IS NOT INCLUDED IN THE DOMAIN ................................................................ 26
1.4 TOPIC EXPLANATION/ELABORATION: IMPLEMENTATION ................................... 27
1.4.1 CASE STUDY .................................................................................................................... 27
1.4.2 DELIVERABLES ............................................................................................................... 28
1.5 TOPIC EXPLANATION/ELABORATION: THE IMPORTANCE OF THE SUBJECT ... 28
VI
CHAPTER 2. SIMILAR STUDIES ................................................................................................. 31
2.1. LITERATURE REVIEW WITHIN GRASSHOPPER ......................................................... 31
2.1.1 DAYLIGHTING AND GLARE .......................................................................................... 32
2.1.2 SHADING DEVICES ......................................................................................................... 33
2.1.3 DAYLIGHTING AND THERMAL COMFORT ................................................................ 33
2.1.4 KINETIC FAÇADES FOR DAYLIGHTING...................................................................... 33
2.1.5 NATURAL VENTILATION .............................................................................................. 34
2.1.6 OPTMIZATION STUDIES ................................................................................................ 34
2.2 LITERATURE REVIEW OUTSIDE GRASSHOPPER ....................................................... 35
2.3 CRITICAL MATRIX OF LITERATURE REVIEW ............................................................ 36
2.4 EXISTING GAPS .................................................................................................................... 36
2.5 INNOVATIONS OF THE APPROACH ................................................................................ 37
2.6 KEYWORDS ........................................................................................................................... 38
CHAPTER 3. METHODOLOGY AND IMPLEMENTATION ..................................................... 39
3.1 PHASE 1: SITE ....................................................................................................................... 41
3.1.1 MODELING THE URBAN CONTEXT (GENERAL) ........................................................ 41
3.1.2 MODELING THE URBAN CONTEXT (CASE-STUDY) .................................................. 43
3.2 PHASE 2: GEOMETRY ......................................................................................................... 46
3.2.1 MODELING BUILDING ZONES (GENERAL) ................................................................. 46
3.2.2 MODELING BUILDING ZONES (CASE-STUDY) ........................................................... 47
3.2.3 MODELING WINDOWS (GENERAL).............................................................................. 49
3.2.4 MODELING WINDOWS (CASE-STUDY)........................................................................ 52
3.2.5 MODELING EXTERIOR SHADING DEVICES (GENERAL) .......................................... 52
VII
3.2.6 MODELING EXTERIOR SHADING DEVICES (CASE-STUDY) .................................... 56
3.3 PHASE 3: SIMULATIONS ..................................................................................................... 57
3.3.1 CASE-STUDY DAYLIGHTING SIMULATIONS ............................................................. 57
3.3.2 CASE-STUDY THERMAL SIMULATIONS ..................................................................... 61
3.3.3 OPTIMIZATIONS (GENERAL) ........................................................................................ 68
3.3.3 OPTIMIZATIONS (CASE-STUDY) .................................................................................. 71
3.4 SAMPLE RESULTS ............................................................................................................... 72
3.5 SUMMARY ............................................................................................................................. 76
CHAPTER 4. RESULTS .................................................................................................................. 79
4.1. METHODOLOGY (RECAPITULATION) .......................................................................... 80
4.1.1 PHASE 1: SITE AND OVERSHADOWING ANALYSIS ................................................. 81
4.1.2 PHASE 2: GEOMETRY ..................................................................................................... 81
4.1.3 PHASE 3: SIMULATIONS ................................................................................................ 83
4.2 RESULTS ................................................................................................................................ 85
4.2.1 GROUP 1: IN-DEPTH EXAMINATION OF EACH SIMULATION ................................. 85
4.2.2 GROUP 2: MATRIX OF RESULTS FOR EACH ZONE .................................................... 94
4.2.3 GROUP 3: MATRIX OF RESULTS OF ALL ZONES TOGETHER .................................. 99
4.2.4 GROUP 4: IMPROVEMENT CHARTS ........................................................................... 101
4.3 CHAPTER CONCLUSIONS ................................................................................................ 104
CHAPTER 5. DISCUSSION .......................................................................................................... 105
5.1 THE WORKFLOW .............................................................................................................. 106
5.2 THE RESULTS ..................................................................................................................... 107
5.2.1 ASSESSMENT OF COMPLEX URBAN SCENARIOS AND ORIENTATION ............... 108
VIII
5.2.2 CORRELATION BETWEEN DAYLIGHT AND THERMAL COMFORT ...................... 111
5.2.3 OPTIMIZATION STUDIES ............................................................................................. 117
5.2.4 LIMITATIONS IN CURRENT STATE OF DEVELOPMENT ......................................... 119
5.3 CONCLUSION ...................................................................................................................... 120
CHAPTER 6. CONCLUSIONS ..................................................................................................... 122
6.1 WORKFLOW FUNCTIONALITY ...................................................................................... 122
6.2 FINDINGS FROM THE SIMULATIONS RESULTS......................................................... 123
6.3 FUTURE WORK................................................................................................................... 125
6.4 CONCLUSION ...................................................................................................................... 127
BIBLIOGRAPHY ........................................................................................................................... 129
A3. THIRD CHAPTER APPENDIX ............................................................................................. A-1
A3.1 WORKFLOW DIAGRAM ................................................................................................ A-1
A3.2 GRASSHOPPER DEFINITION ........................................................................................ A-2
A3.3 PEAK ANNUAL HOT DAY, AND LOWEST ANNUAL COLD DAY. …………………A-3
A4. FOURTH CHAPTER APPENDIX.......................................................................................... A-5
A4.1 GROUP 1: IN DEPTH EXAMINATION OF EACH SIMULATION ................................ A-5
A4.2 GROUP 2: MATRIX OF RESULTS FOR EACH ZONE ................................................ A-42
A4.3 GROUP 3: MATRIX OF RESULTS OF ALL ZONES TOGETHER .............................. A-46
A4.4 GROUP 4: IMPROVEMENT CHARTS ......................................................................... A-47
IX
LIST OF FIGURES
Figure 1.1-Typical Office Building Energy Use (US Department of Energy) ........................ 2
Figure 1.2 - Daylighting Design (Behnisch Architekten) ...................................................... 4
Figure 1.3 - Natural Ventilation in a High-rise Building (Wood and Salib) ........................... 4
Figure 1.4a - Façade Optimization using Grasshopper and Rhino3d (Atelier Ten) ................ 9
Figure 1.4b - Façade Optimization using Grasshopper and Rhino3d (Atelier Ten) .............. 10
Figure 1.5 - Typical EUI Values based on Buildings Typology (EPA) ................................ 17
Figure 1.6 – Software Diagram .......................................................................................... 26
Figure 1.7 - Building Zones ............................................................................................... 27
Figure 3.1 - Workflow Diagram (See Appendix A3.1 for a larger Diagram) ....................... 40
Figure 3.2 - Grasshopper Definition (See Appendix A3.2 for a larger Image) ..................... 41
Figure 3.3 - Selection of Urban Context (Open Street Maps) .............................................. 42
Figure 3.4 - Translation of data from Open Street Maps into Grasshopper .......................... 42
Figure 3.5 - Case-Study Urban Context ............................................................................. 43
Figure 3.6 - Overshadowing Conditions during Peak Annual Hot Day (PAHD) and
Lowest Annual Cold Day (LACD) ......................................................................... 45
Figure 3.7 - Geometry Flow-Chart...................................................................................... 46
Figure 3.8 - Example of Other Geometries ......................................................................... 46
Figure 3.9 - Building Modeler: Grasshopper Definition-Inputs and Outputs ....................... 48
Figure 3.10 - Zones Modeling Flow-Chart ......................................................................... 48
Figure 3.11 - Fragments to Study in relation to the Urban Context ..................................... 48
Figure 3.12 - Windows Modeler, Parameters ..................................................................... 49
Figure 3.13 - Windows by Array ....................................................................................... 50
Figure 3.14 - CPlane, Wall Polyline and Windows ............................................................. 51
Figure 3.15 - Examples of Customized Windows ............................................................... 51
Figure 3.16 - Case-Study Building´s Zone Windows (Fully Glazed) ................................... 52
X
Figure 3.17 - Grasshopper Definition for Modeling Shading Devices ................................. 52
Figure 3.18 - Examples of Optimized Shading Devices ...................................................... 54
Figure 3.19 - Shading Projections-Validation (September 24, 9:00am – 3:00pm) ................ 54
Figure 3.20 - Shading Projections-View from the Sun (September 24, 11:00am) ................ 54
Figure 3.21 - Examples of Customized Shading Devices .................................................... 56
Figure 3.22 - Shading Elements from Adjacent Buildings .................................................. 56
Figure 3.23 - Grasshopper Definition for Daylight Simulations using DIVA ...................... 60
Figure 3.24 - Daylight Simulation Visualization ................................................................ 60
Figure 3.25 - Typical EUI Values based on Buildings Typology (EPA) .............................. 61
Figure 3.26 - Sample EUI Results Chart ............................................................................ 62
Figure 3.27 - Thermal Autonomy Sample Outputs ............................................................. 64
Figure 3.28 - Grasshopper Definition for Thermal Simulations using the
plug-in Archsim ..................................................................................................... 67
Figure 3.29 - Grasshopper Definition for Energy Simulations using the
DIVA component Viper ......................................................................................... 67
Figure 3.30 - Octopus as seen in Grasshopper .................................................................... 69
Figure 3.31 - Octopus Navigation Window - Sample Output .............................................. 70
Figure 3.32 – 7
th
Floor SW Fragment “Base Case” Results ................................................ 74
Figure 3.33 – 7
th
Floor SW Fragment “Optimization 1” Results ......................................... 75
Figure 3.34 - Sample Visualizations Comparison ............................................................... 76
Figure 3.35 - Sample Charts Comparison ........................................................................... 76
Figure 3.36 - Workflow Diagram (See Appendix A3.1 for a larger Diagram) ..................... 77
Figure 3.37 - Grasshopper Definition (See Appendix A3.2 for a larger Image) ................... 78
Figure 4.1 - Selection of Urban Context (Open Street Maps) .............................................. 80
Figure 4.2 - Selection of Urban Context (Open Street Maps) .............................................. 81
Figure 4.3 - Building Zones, Case Study Base-case Zones Model ....................................... 82
XI
Figure 4.4 – 7
th
Floor SW Fragment “Base Case” Results .................................................. 86
Figure 4.5 – 7
th
Floor SW Fragment “Optimization 1” Results ........................................... 87
Figure 4.6 – 51
st
Floor SW Fragment “Base Case” Results ................................................. 88
Figure 4.7 – 51
st
Floor SW Fragment “Optimization 5” Results .......................................... 89
Figure 4.8 – 7
th
Floor NE Fragment “Base Case” Results ................................................... 90
Figure 4.9 – 7
th
Floor NE Fragment “Optimization 3” Results ............................................ 91
Figure 4.10 – 51
st
Floor NE Fragment “Base Case” Results ............................................... 92
Figure 4.11 – 51
st
Floor NE Fragment “Optimization 5” Results ........................................ 93
Figure 4.12 - 7
th
Floor, South-West Corner Comparison ..................................................... 95
Figure 4.13 - 51
st
Floor, South-West Corner Comparison ................................................... 96
Figure 4.14 - 7
th
Floor, North-East Corner Comparison ...................................................... 97
Figure 4.15 - 51
st
Floor, North-East Corner Comparison .................................................... 98
Figure 4.16 - Matrix of Results ........................................................................................ 100
Figure 5.1 - Discussion Summary .................................................................................... 105
Figure 5.2 – Comparison of Base-case Scenarios ............................................................. 110
Figure 5.3 - Design Solutions with Similar Outcomes ...................................................... 111
Figure 5.4 - Base-cases: Shaded vs Non-shaded ............................................................... 114
Figure 5.5 - Shaded Scenarios vs. Non-Shaded ................................................................ 116
Figure 5.6 - Octopus Charts 51
st
Floor SW Corner ........................................................... 119
Figure 6.1 – Daylighting, Energy, Natural Ventilation Correlation ................................... 125
Figure A3.1 - Workflow Diagram.................................................................................... A-1
Figure A3.2 - Grasshopper Definition .............................................................................. A-2
Figure A3.3 - Peak Annual Hot Day (PAHD), And Lowest Annual Cold Day (LACD) .... A-3
Figure A4.1 – 7
th
Floor SW Fragment “Base Case” Results .............................................. A-6
Figure A4.2 – 7
th
Floor SW Fragment “Base Case with Natural Ventilation” Results ........ A-7
Figure A4.3 – 7
th
Floor SW Fragment “Base Case with Natural Ventilation
and Shading Devices” Results .............................................................................. A-8
XII
Figure A4.4 – 7
th
Floor SW Fragment “Optimization 1” Results ...................................... A-9
Figure A4.5 – 7
th
Floor SW Fragment “Optimization 2” Results .................................... A-10
Figure A4.6 – 7
th
Floor SW Fragment “Optimization 3” Results .................................... A-11
Figure A4.7 – 7
th
Floor SW Fragment “Optimization 4” Results .................................... A-12
Figure A4.8 – 7
th
Floor SW Fragment “Optimization 5” Results .................................... A-13
Figure A4.9 – 7
th
Floor SW Fragment “Optimization 6” Results .................................... A-14
Figure A4.10 – 51
st
Floor SW Fragment “Base Case” Results ........................................ A-15
Figure A4.11 – 51
st
Floor SW Fragment “Base Case with Natural Ventilation” Results .. A-16
Figure A4.12 – 51
st
Floor SW Fragment “Base Case with Natural Ventilation
and Shading Devices” Results ............................................................................ A-17
Figure A4.13 – 51
st
Floor SW Fragment “Optimization 1” Results ................................. A-18
Figure A4.14 – 51
st
Floor SW Fragment “Optimization 2” Results ................................. A-19
Figure A4.15 – 51
st
Floor SW Fragment “Optimization 3” Results ................................. A-20
Figure A4.16 – 51
st
Floor SW Fragment “Optimization 4” Results ................................. A-21
Figure A4.17 – 51
st
Floor SW Fragment “Optimization 5” Results ................................. A-22
Figure A4.18 – 51
st
Floor SW Fragment “Optimization 6” Results ................................. A-23
Figure A4.19 – 7
th
Floor NE Fragment “Base Case” Results .......................................... A-24
Figure A4.20 – 7
th
Floor NE Fragment “Base Case with Natural Ventilation” Results .... A-25
Figure A4.21 – 7
th
Floor NE Fragment “Base Case with Natural Ventilation
and Shading Devices” Results ............................................................................ A-26
Figure A4.22 – 7
th
Floor NE Fragment “Optimization 1” Results ................................... A-27
Figure A4.23 – 7
th
Floor NE Fragment “Optimization 2” Results ................................... A-28
Figure A4.24 – 7
th
Floor NE Fragment “Optimization 3” Results ................................... A-29
Figure A4.25 – 7
th
Floor NE Fragment “Optimization 4” Results ................................... A-30
Figure A4.26 – 7
th
Floor NE Fragment “Optimization 5” Results ................................... A-31
XIII
Figure A4.27 – 7
th
Floor NE Fragment “Optimization 6” Results ................................... A-32
Figure A4.28 – 51
st
Floor NE Fragment “Base Case” Results ......................................... A-33
Figure A4.29 – 51
st
Floor NE Fragment “Base Case with Natural Ventilation” Results ... A-34
Figure A4.30 – 51
st
Floor NE Fragment “Base Case with Natural Ventilation and
Shading Devices” Results .................................................................................. A-35
Figure A4.31 – 51
st
Floor NE Fragment “Optimization 1” Results .................................. A-36
Figure A4.32 – 51
st
Floor NE Fragment “Optimization 2” Results .................................. A-37
Figure A4.33 – 51
st
Floor NE Fragment “Optimization 3” Results .................................. A-38
Figure A4.34 – 51
st
Floor NE Fragment “Optimization 4” Results .................................. A-39
Figure A4.35 – 51
st
Floor NE Fragment “Optimization 5” Results .................................. A-40
Figure A4.36 – 51
st
Floor NE Fragment “Optimization 6” Results .................................. A-41
Figure A4.37 - 7
th
Floor, South-West Corner Comparison .............................................. A-42
Figure A4.38 - 51
st
Floor, South-West Corner Comparison ............................................ A-43
Figure A4.39 - 7
th
Floor, North-East Corner Comparison ............................................... A-44
Figure A4.40 - 51
st
Floor, North-East Corner Comparison ............................................. A-45
Figure A4.41 - Matrix of Results ................................................................................... A-46
XIV
LIST OF CHARTS
Chart 1.1 - System Requirements ...................................................................................... 18
Chart 1.2 - Software Index ................................................................................................ 19
Chart 1.3 – Deliverables ................................................................................................... 28
Chart 2.1 - Matrix of Literature Review ............................................................................ 36
Chart 3.1 - Settings for Daylight Simulations ..................................................................... 58
Chart 3.2 - Constant Parameters for Daylight Simulations ................................................ 59
Chart 3.3 - Variable Parameters for Daylight Simulations ................................................. 59
Chart 3.4 - Settings for Thermal Simulation ...................................................................... 65
Chart 3.5 - Constant Parameters for Thermal Simulation .................................................. 66
Chart 3.6 - Variable Parameters for Thermal Simulations ................................................. 66
Chart 3.7 - Octopus Settings ............................................................................................. 71
Chart 3.8 - Octopus Genome Values (Parameters) ............................................................. 72
Chart 4.1 - Best-Cases, Improvements over Base-cases ................................................... 101
Chart 4.2 - 7
th
Floor South-West Corner Improvements over Base-cases .......................... 102
Chart 4.3 - 51
st
Floor South-West Corner, Improvements over Base-cases ....................... 102
Chart 4.4 - 7
th
Floor North-East Corner, Improvements over Base-cases .......................... 102
Chart 4.5 - 51
st
Floor North-East corner, Improvements over Base-cases ......................... 103
Chart A4.1 - Best-Cases, Improvements over Base-cases ............................................... A-47
Chart A4.2 - 7
th
Floor South-West corner Improvements over Base-cases ...................... A-47
Chart A4.3 - 51
st
Floor South-West corner, Improvements over Base-cases ................... A-47
Chart A4.4 - 7
th
Floor North-East corner, Improvements over Base-cases ...................... A-48
Chart A4.5 - 51
st
Floor North-East corner, Improvements over Base-cases ..................... A-48
1

CHAPTER 1. INTRODUCTION

The acknowledgment that the burning of fossil fuels is the main cause of global warming is widely
shared by the scientific community. Driven by the need to modify the outputs of human activities,
governments and the private sectors are increasingly injecting efforts and resources to reduce
environmental impacts. The State of California, for example, has defined rigorous measures to be
considered in the incoming decades for all new residential buildings, by building code, to meet the Net
Zero Energy (NZE) denomination. Commercial buildings will go into the same transformation starting
in 2030 (California Public Utilities Commission, 2008).

As the urgency towards reducing the environmental impacts of buildings increases, the need to inform
architects about the outcomes of their designs has become mandatory, especially during early stages of
design when the most effective and economical solutions can be implemented; as well as in retrofit
scenarios, where the transformations needed to reduce the huge stock of energy consumption of the built
environment (40-50% in the US according to the US EIA) can be done.

It is estimated that around 63% of the energy consumption of buildings is in one way or another related
with their envelopes (US Department of Energy, 2013) because of this, it is natural to understand that
huge amount of the attention when attaining NZE should be placed in the design of building envelopes
(Figure 1.1). Adequate apertures for interior daylighting not only reduce the use of electricity for
artificial lighting and enhance people´s productivity (Court, Pearson, and Frewing, 2010), but also,
accompanied by a satisfactory shading system may regulate the indoor thermal comfort by radiation
control. Even though there is vast knowledge on how to maximize daylight access and how maintain
2

thermal comfort in buildings, it is usually difficult to assess and implement solutions that fit into those
requirements due, amongst other things, to the complexity of urban scenarios where buildings are
subjected to unpredictable circumstances pertaining the conditions of the context and the local climate.
Overshadowing of neighboring constructions and plots preventing adequate orientation of building
configurations are two examples of those kinds of complexities.

Figure 1.1-Typical Office Building Energy Use (US Department of Energy)

Another limitation to assess and implement solutions that meet the existing guidelines for enhanced
daylight access and thermal comfort is the incapacity to simulate large numbers of design configurations
in short periods of time.
3

1.1 RESEARCH OBJECTIVE
The workflow was developed to inform, improve, and accelerate early-stage and retrofit design
decision-making related to key parameters in the design of building envelope configurations, including
fenestration and shading strategies. In contrast with conventional simulation applications, this particular
approach simplifies the whole process of fine-tuning optimum design configurations by automating the
analysis of base-case scenarios. The intended users of the proposed workflow include architects and
engineers in the professional or academic realms interested in exploring and identifying optimal
fenestration parameters to achieve desired daylighting and thermal comfort outcomes.

1.2 TOPIC EXPLANATION/ELABORATION: TERMS

1.2.1 CONCEPTS AND TERMS FROM BUILDING SCIENCE
 Environmentally Responsive Buildings
According to Simos Yannas: “An environmentally-responsive architecture is not a fixed ideal, but an
evolving concept that must be redefined and reassessed with each new project. The notion of
environmental responsiveness should be related to occupants and their activities, to the city, and to
change, as well as to the outside climate” (Yannas, 2003). Understanding what an environmentally
responsive building is requires looking deeply into the processes and interactions that occur between the
user´s activities, the behavior around buildings, and the way that the building adapts and deals with the
surrounding conditions (both the one created by the urban context and those created by climate).
Adaptation plays a crucial role when looking into environmental responsiveness, since indoor conditions
are directly dependent to outdoor occurrences, and since outdoor occurrences are never static but always
4

flowing in dynamic cycles, having the capacity to adjust to those changes would mean entailing the
building with environmentally responsive attributes. This is important because in the end, it represents
the most financially and environmentally economic solution to minimize energy consumption and offset
the carbon footprint.

A building could be considered environmentally responsive when implementing a hybrid ventilation
system where natural ventilation, through wind and buoyancy be at work through purposely installed
openings in the building envelope, while being supplemented by fan systems when needed. In this case
the use of energy is subordinated by the naturally occurring cooling effect of wind and buoyancy. In this
sense, incorporating a design scheme based on environmental responsiveness mainly refers to the
adaptation that a building could have, primarily by its envelope, to the climatic conditions of its location
(Figure 1.2, 1.3).

Figure 1.3 - Natural Ventilation in a High-rise Building (Wood and Salib)
Figure 1.2 - Daylighting Design (Behnisch Architekten)
5

 Daylighting
Adequate daylighting has been shown to have multiple benefits for building occupants in different
contexts; for example in hospitals it has been demonstrated that patients perceive less stress, experience
less pain, have shorter lengths of stay, take less analgesic medicines and in overall incur in less
medication costs. The same research demonstrated that doctors and nurses perform better with good
levels of illumination, with the assumption that natural light is the preferred by people in general (Anjali,
2006). In the case of commercial buildings and schools, daylighting has been associated with increased
productivity and performance (Plympton, Conway, Epstein, 2000). In addition to that, daylighting
reduce the consumption of electricity that would otherwise be used for artificial lighting.

 Thermal Comfort
According to ASHRAE (ANSI/ASHRAE Standard 55), “Thermal comfort is the condition of mind that
expresses satisfaction with the thermal environment and is assessed by subjective evaluation.”
Achieving thermal comfort with low energy expenditures is one of the main goals to create energy
efficient buildings.
Thermal comfort is primarily affected by heat gains and losses in buildings, encompassing factors such
as air temperature, mean radiant temperature, air speed, relative humidity, metabolic rates, and clothing
insulation. Also it has been determined that psychological factors such as people´s expectations play a
role in thermal comfort (de Dier and Brager, 1998).
There are different standards to assess the thermal comfort in buildings, one of the most important ones
is the Predicted Mean Vote (PMV) standard, which basically determines a level of satisfaction in a space
based on data collected about satisfaction levels in people subjected to similar conditions to the ones in
the analyzed building (Fanger, 1970). This standard is mainly used in spaces conditioned with the use of
6

mechanical systems. Another standard is the adaptive comfort standard (ACS), which was created after
studies acknowledging a variable thermal comfort range in buildings cooled passively with natural
ventilation, where psychological factors such as expectations on the thermal conditions of the space and
control over the immediate environment (opening or closing windows as needed) were found to have an
effect on the comfort of people (Nicol, Humphreys, 2002). For this thesis, the ACS was used, since
natural ventilation was the basic cooling strategy utilized.

 Net Zero Energy (NZE) Buildings
The term net zero energy represents the notion that all the energy consumed by a building will not be
higher than that produced on site; this may also include the embodied energy for the execution of the
building (California Energy Comission, 2011). It is important to have a common acknowledgement of
the boundaries and metrics amongst the stakeholders of any project over which the final objectives and
strategies will be defined. Because of that, NREL determined four different definitions to encompass the
different realms to which the concept of net zero energy could be applied:
NET ZERO ENERGY BUILDING DEFINITIONS (Crawley, Shanti, Torcellini, 2009)
• Net Zero Site Energy: A site NZEB produces at least as much renewable energy as it uses
in a year, when accounted for at the site.

• Net Zero Source Energy: A source NZEB produces (or purchases) at least as much
renewable energy as it uses in a year, when accounted for at the source. Source energy refers to
the primary energy used to extract, process, generate, and deliver the energy to the site. To
calculate a building’s total source energy, imported and exported energy is multiplied by the
appropriate site-to-source conversion multipliers based on the utility’s source energy type.

• Net Zero Energy Costs: In a cost NZEB, the amount of money the utility pays the building
owner for the renewable energy the building exports to the grid is at least equal to the amount
the owner pays the utility for the energy services and energy used over the year.

• Net Zero Emissions: A net zero emissions building produces (or purchases) enough
emissions-free renewable energy to offset emissions from all energy used in the building
annually. Carbon, nitrogen oxides, and sulfur oxides are common emissions that ZEBs offset.
7

To calculate a building’s total emissions, imported and exported energy is multiplied by the
appropriate emission multipliers based on the utility’s emissions and on-site generation
emissions (if there are any).

 Building Envelope
The origins of the architectural endeavor are directly related with the need to have a shelter against the
outdoor conditions; the primary means to create this transition with the environment has always been
through the physical enclosures called buildings. Subsystems have been developed that work to create
satisfactory indoor conditions and which do not necessarily relate to the physical enclosure of a space as
such. In that sense, the term of building envelope synthesizes the roles that are specifically related with
the exterior enclose or shell of what constitutes a building to protect the interior spaces (Building
Envelope Design Guide, n.a.).

With the advent of HVAC systems, attention that was once given to the building envelopes for passive
measures to maintain the indoor environmental quality (IEQ) was relegated. From there on, the main
paradigm to manage the IEQ of buildings relied on the use of mechanical systems (Grondzik, Kwok,
Stein, Reynolds, 2009). Yet, the building envelope keeps playing a key role, especially because by
adapting it to environmental conditions, great energy savings can be attained while improving the indoor
conditions.

1.2.2 COMPUTER AIDED DESIGN (CAD) CONCEPTS AND TERMS
 Parametric Design
Now a days the term of parametric design has become widely use in the realm of architecture modeling,
yet, it is important to make a distinction between two different areas where the term is applied. One of
8

them is in regards to Generative Modeling (GM), while the other is used within Building Information
Modeling (BIM).
Each category has its own approach of implementing parameter-based models into the design process.
While GM primarily focuses on the relation and management of data streams with free geometries, BIM
is specifically geared towards the architectural and engineering industries as a way to integrate and
facilitate the design and execution of buildings. The first one departs from a quasi-abstract approach
where complexity and freedom seem to be the norm, the second departs from a body of industry-derived
knowledge and solutions, such are the cases of the well know building information modeling tools such
as ArchiCAD, Revit, AECO-Sim, etc., whose purpose is to synthesize the whole design process within a
single model. For this thesis though, the use of term of parametric design will refer solely to the GM
category.

Existing state-of-the-art software applications for architecture, in particular GM tools such as
Grasshopper, are characterized by allowing the user to synthesize the digital models through
mathematical functions and algorithms (Stavric, n.a.). This particular modeling approach branches out
from the discipline of programming rather than from traditional paradigms of architectural design like
any conventional 3D modeling tool. Because of this, a parametric design tool like Grasshopper would
offset the palette of capabilities of the modeling tool (Rhino3d in this case) over which it works, to reach
its underlying algorithmic structure and enable the freedom to manipulate it beyond the preconceived
arrangements, in this sense the limits are constrained by the logical syntax behind the user´s input rather
than by a set of discrete standardized utilities and operations. Since conceptual parametric models are
created using algorithmic arrangements, there is a huge potential and freedom to explore innumerable
9

design possibilities and variations in short periods of time by simply adjusting the parameters behind
those arrangements.

In Building Science, the potential of these applications represent an unexplored ground full of
opportunities to create sound architectural solutions. Take for instance the example of Atelier Ten in the
Memorial Sloan-Kettering Cancer Center in New York, who through a parametric design approach,
performed a study on the façades of a building to calculate the sizing of vertical fins based on the solar
radiation. What they did, instead of modeling the shading elements directly into the workspace of a
drawing (as it would be with a conventional approach), was to weave all the information pertaining the
design problem that was examined (to prevent direct solar radiation throughout the façades), so that the
shading elements would respond in direct relation to the amount of energy and the orientation source of
the sunlight (Atelier Ten, Zofchak, 2013) (Figures 1.4a,b). The advantages of an approach like that,
consist on the precision and flexibility to adapt and implement variations in real time without the need to
modify the models manually or element by element.

Figure 1.4a - Façade Optimization using Grasshopper and Rhino3d (Atelier Ten)

10

Figure 1.4b - Façade Optimization using Grasshopper and Rhino3d (Atelier Ten)

 Energy Simulations
Evaluations of the digital models using real time data and weather conditions of a particular place to
measure the energy consumption or the response to environmental conditions such as temperature
variations, solar trajectories, wind, etc.

 Visualizations
Visual representation of the results thrown by a simulation. Depending on the metric under study, the
visualization will provide a specific depiction of what is being study. For example, to visualize the
annual useful daylight illuminance of a space, a logic approach would be to visualize the values of each
of the grid points that were used for the simulation in their respective locations within the space that was
examined.

 Optimization
The term is used to refer to a computational iterative process that can be performed within Grasshopper
to reach user-defined objectives based on a set of design variables (parameters). Optimization studies
consist of exploring different combinations of parameters in order to find the best results in regards to
11

the objectives defined in an automated fashion, which means that a particular problem can be solved
without the recurrence of the user´s input and control.

 Relevance of Studying Urban Areas
The relevance of looking into the urban scale when performing simulations, comes from the need to
have a comprehensive assessment of the conditions surrounding the particular project under study in
order to arrive to more realistic outcomes. This is particularly important if overshadowing from adjacent
building have a direct impact into daylighting, thermal comfort, energy consumption and natural
ventilation.

1.3 TOPIC EXPLANATION/ ELABORATION: DOMAIN
 Defining the Scope
This work is framed in the context of the architectural and building science disciplines. The objectives
behind this project are related with assisting early stage design and retrofit decision-making towards
environmentally responsive buildings, by the implementation of a workflow.

 Approach to examine the problem
It was determined that a workflow developed in Grasshopper-Rhino3D was the best approach to work
on this project because parametric tools have the potential to solve complex design formulations and
offer the freedom to adapt to the particular needs of the user through algorithmic modeling. In that sense
Rhino3D offers the interface over which any kind of modeling solutions can be examined, while
Grasshopper offers the freedom to create a set of capabilities that go beyond the existing options offered
12

by standard energy modeling applications. For instance, some of the existing gaps in energy simulation
tools that can be solved through the implementation of Grasshopper are the following:
- The dislocation between daylight and thermal studies
- The inability to iterate through several design options (>15) in short periods of time
- The capacity to calculate the thermal behavior of buildings using “thermal autonomy” as a metric
- The capacity to automate the process of generating shading systems based on user-defined
constraints of peak days and hours
In order to arrive to the final workflow, a methodology based on the needs to be satisfied was defined. It
was determined that the workflow would be primarily divided in three main areas: 1) Site, 2) Geometry,
3) Simulations. The workflow will be discussed in detail in chapter 3 of this document.

The approach for performing the simulations involves spatially-granular analysis to determine optimal
fenestration and exterior shading solutions for daylighting and thermal comfort. The term spatially-
granular, in this context, refers to the identification of a bounded region of the façade (e.g. one bay) for
analysis rather than the complete facade or the creation of a whole-building energy model. A single
interior thermal zone is modeled adjacent to the facade to assess the annual thermal and lighting
conditions produced by the facade region. This approach follows the “shoebox” modeling approach used
in other early-stage energy modeling tools (e.g. COMFEN) designed to support the systematic
evaluation of alternative fenestration systems for project-specific commercial building applications
(LBNL, 2013). The “shoebox” approach is applicable for modeling scenarios where the building
envelope dominates the energy performance of the zone and where heat transfer surfaces between
adjacent zones can be discounted. To determine the fenestration geometry for a complete façade, a
13

form-finding procedure described in the methodology of this research is used to produce optimized
geometry for each fenestration pattern.

1.3.1 GOALS/OBJECTIVES
The goal of this research consists of developing a parametric workflow to inform early-stage design and
retrofit decision-making by optimizing fenestration geometry patterns in response to annual climate-
based thermal comfort and daylighting performance outcomes. Building upon other partial existing
frameworks, it correlates the daylighting with thermal performance through multi-objective
optimization. It outputs the correlated results in terms of the emerging metrics of thermal autonomy
(TA) and useful daylight illuminance (UDI), and in addition it translates design iterations into energy
use intensity (EUI) for a comprehensive assessment of the results. By determining optimal geometry for
daylight aperture configurations and exterior shading elements across the façade, it acts as a design aide
for architects and consultants working in a Rhino 3D and Grasshopper computing environment. The
approach and its novel features are described and tested in the context of a hypothetical commercial
building facade retrofit scenario located in an urban environment.

An important contribution of the project, relying not only on the elaboration of the workflow by using
the existing capabilities of Grasshopper, Rhino3D and those offered by third parties is the creation of a
set of custom components, also known as “User Objects,” to satisfy certain needs that cannot be attained
by the existing utilities offered by the software. Such are the cases of the automated modeling of the
buildings zones (including windows), the creation of shading devices modeled after climate data, the
capacity to visualize daylighting results and thermal autonomy, and the translation of energy
14

consumption data into energy use intensity. These customized functionalities allowed a consistent
operation and usability of the workflow, including the following qualities:
 Focused
By systematically following the set of steps defined, the user is informed about what to do at any
stage, making it easy to incorporate the adequate inputs along the workflow.
 Flexible
The user have the freedom to define the geometry of the building (encompassing the building
volume with its different floors, windows, and shading devices) as well as the surrounding
conditions in order to model buildings according to the conditions of each project.
 Fast
The optimization capabilities offered in this workflow will enable the automated execution of
several iterations to reach the optimum objectives. This means the capacity to explore above +-
100 iterations in one single workday depending on the design problem.
 Informative
The tool provides the comparison of results between the different options through graphical
representations of the geometry and the output data.
1.3.2 METRICS
The main objective of this project is to develop a workflow capable of informing early-stage and retrofit
design decision-making related with daylight and thermal comfort. In order to examine the indoor
conditions of daylighting and thermal comfort in buildings, there are several routes depending on the
metrics used. The metrics selected for evaluating those outcomes where the emerging metrics of useful
daylight illuminance (UDI), thermal autonomy (TA), and energy use intensity (EUI).

15

 Daylighting and Glare Performance: Useful Daylight Illuminance (UDI)
To evaluate annual daylighting performance, a modified version of the metric Spatial Daylight
Autonomy (sDA) was used. Spatial Daylight Autonomy describes annual sufficiency of ambient
daylight levels in interior environments (IES, 2012). It is a spatial application of the climate-based
daylighting metric Daylight Autonomy (DA) developed by Reinhart & Walkenhorst 2001. Spatial
Daylight Autonomy is defined as the percent of an analysis area (typically a floor area or work plane)
that meets a minimum horizontal daylight illuminance level for a specified fraction of operating hours
(e.g. 9AM – 5PM local clock time) per year. The illuminance level (e.g. 300 lux) and annual time
fraction (e.g. 50%) are included as subscripts (e.g. sDA300,50% ). Daylight illuminance conditions are
generated from typical meteorological year (TMY) data. The metric was modified to exclude periods of
the year where interior daylight illuminances within the analysis region exceed 2000 lux.
This modification was implemented as a proxy indicator for glare and follows the threshold approach
used in the Useful Daylight Illuminance (UDI) metric developed by Mardaljevic and Nabil, 2005. In the
proposed workflow, the modified performance indicator is Spatial Useful Daylight Illuminance (sUDI).
As an example, a façade configuration that achieves sUDI300-2000, 50% over 75% of the analysis region is
preferred to a configuration that achieves sUDI 300-2000, 50% over only 60% of the same area. In the
proposed workflow, a grid of analysis points located 30-inches (0.8m) above the floor and spaced 48-
inches (1.22m) apart is used to record sUDI300-2000, 50% outcomes in a horizontal plane. Individual
outcomes are then tabulated to determine the percent of the analysis region where the sUDI300-2000,
50% criteria was met.

16

 Thermal Comfort Performance: Thermal Autonomy
Thermal comfort is often provided through the use of mechanical systems rather than through
application of passive or active environmentally responsive systems that link occupants more closely
with local climate (e.g. operable windows for mixed-mode ventilation). In the latter case, occupants
have been found to accept and prefer a wider interior thermal set-point range and seasonal adjustment to
heating and cooling thresholds
i
. Consequently, design scenarios that are evaluated assuming a fixed
interior thermal set-point range (e.g 72-76 deg. F) maintained by mechanical space conditioning are
likely to lead to the unnecessary over-design and over-use of mechanical systems. To address this
limitation, the metric thermal autonomy was selected to rank optimal thermal outcomes in terms of
annual “comfort hours” achieved without the use of mechanical space conditioning and rather by the
passive attributes and behavior of the building envelope. Thermal autonomy was developed by the
design and consulting firm Loisos + Ubbelohde as a metric to link occupant comfort to climate, building
fabric, and building operation. In addition to assessing annualized thermal comfort conditions, it enables
rich visual feedback to architects, engineers, and building owners for the patterns, degree, and frequency
of thermal comfort and discomfort for a given design over the course of the year (Levit, Ubbelohde,
Loisos, Brown, 2013). Thermal autonomy allows the user to specify the comfort model used to
determine the acceptable temperature range and represents the hours of the year where the zone
temperature is within the acceptability range determined by the model. In the proposed workflow, the
ASHRAE Adaptive Thermal Comfort Model (ASHRAE, 2004) was used to allow the acceptability
range to vary seasonally in response to outdoor temperature.

17

 Energy Consumption: Energy Use Intensity (EUI)
Correlations between daylighting and thermal comfort for optimal indoor environmental conditions with
the lowest energy consumption possible will be established. In order to assess that outcome, the metric
Energy Use Intensity (EUI) will be used. When using EUI as a metric, the energy consumption is
measured in kBtu (or GJ) divided by the total floor area of the building (kBtu/sf/yr). This metric was
selected because it expresses the building´s energy use as a function of its size (EPA, 2013) facilitating
the comparison of outcomes of different buildings despite their overall square footage. The lower the
EUI values, the more energy efficient the building is. Energy values will be site specific and will include
the values for heating, cooling, and electric lights.

Figure 1.5 - Typical EUI Values based on Buildings Typology (EPA)

1.3.3 SOFTWARE
The implementation of the workflow consisted of bridging a set of functionalities to meet each of the
particular needs to satisfy the research objective of assisting decision-making for daylight and thermally
18

responsive buildings. The work environment in which the workflow was created was within Rhino3D
and Grasshopper. In order to satisfy some of the needs that the workflow demanded, like for instance the
simulation of daylight or thermal behavior, third party plug-ins were implemented, and in addition, user
objects (customized utilities) were also used.

 System Requirements
Hardware:
 8 GB RAM or more is recommended
 DVD drive or an Internet connection for installation
 1 GB free disk space
 OpenGL 2 capable video card recommended
Operating systems:
 Windows 7 or 8 - recommended
 Windows Vista
 Windows XP (32-bit only) Service Pack 3
 Note: Both 32- and 64-bit versions are installed on 64-bit Windows systems.
Not supported:
 Linux
 Windows NT, 95, 98, ME, or 2000
 Windows XP 64-bit
 Mac OS X. (Only with a Windows partition)
 Virtualization systems on OS X such as VMWare and Parallels.
Chart 1.1 - System Requirements

19

 Software Index
A recount of the software, plug-ins, and user objects used for this project.
DASHBOARDS
 Rhinoceros
 Grasshopper
PLUGINS
 DIVA (used for daylight-energy studies)
 Energy Plus (thermal)
 DAYSIM (daylight)
 RADIANCE (daylight)
 Archsim (used for natural ventilation analysis)
 Energy Plus (thermal)
 Elk (urban analysis)
 Octopus (multi-objective optimization)
 Heliotrope (solar analysis)
 DHour (climate analysis)
 Ladybug (shading devices validation)
 Paneling Tools (modeling fenestration patterns)
 Selectable Preview (modeling fenestration patterns)
USER OBJECTS
 Geometry Modeler
 Windows Modeler
 Shading Devices Modeler
 Daylighting Simulations Visualizer
 Thermal Autonomy
 EUI Calculator
Chart 1.2 - Software Index

20

 Dashboards
Rhinoceros (Robert McNeel & Associates)
Rhinoceros is developed by Robert McNeel and Associates. It is as a 3D modeling software commonly
implemented in architecture, industrial design, jewelry design, marine design, automotive design, CAD /
CAM, rapid prototyping, reverse engineering, multimedia as well as in the graphic design industry
(McNeel & Associates, n.a).

Grasshopper (Robert McNeel & Associates)
Grasshopper™ is a visual programming language developed by David Rutten at Robert McNeel &
Associates which runs inside the Rhino3D software. With its algorithmic capabilities, Grasshopper
enables the creation of utilities that go beyond the common palette of commands of Rhino. In its
inception, it started as a way to keep track of the history of steps followed to create a particular design,
later on it evolved into a modeling application that offered the potential to make adjustments of the
different variables throughout the whole process of design, which means that multiple iterations were
able to be executed in real time by adjusting the values of the properties that generated the models.

 Plug-ins
An important part of this workflow consisted of making use of supplemental computational tools
developed by third-parties to incorporate sophisticated additional capabilities into the native
environment of Grasshopper. The term plug-in would be used often to refer to these incorporated
applications. McNeel & Associates deliberately supports the efforts of third-party programmers by
providing the same development tools they use at McNeel to the general public, which eliminates
several big problems. For instance, the Software Development Kit (SDK) used by third-parties has been
21

comprehensively tested by McNeel and therefore there is no need to develop and maintain a separate
product (Herrera, 2011). This condition has promoted the development of a vast number of reliable
added capabilities to the Rhino-Grasshopper environment. Their installation/uninstallation do not
usually go further than the placement/removal of some files in a folder, and their size usually do not
exceed more than some dozens of kilobytes. Because of this, plug-ins should not be seen as cumbersome
or detrimental to the hardware, they could rather be seen as additional options not included in the default
installation of the software due their specificity of purpose.

DIVA (Solemma, 2013): Daylighting and Energy Simulations
The plug-in DIVA for Grasshopper (Solemma, 2013), is a daylighting and energy modeling tool to run
thermal and daylighting simulations. It performs calculations of daylight using DAYSIM, a validated
RADIANCE-based analysis software tool, and of thermal behaviors using the validated EnergyPlus
energy simulation program. Both underlying simulation programs allow annual climate-based
simulation outcomes informed from hourly weather data.
ARCHSIM Energy Modeling (Dogan, 2013):Thermal Simulations
Archsim is a plug-in that supports energy and thermal simulations with Energy Plus. It allows to create
multi-zone thermal models, supporting advanced shading controls, and ventilation modules such as wind
and stack natural ventilation, airflow-networks, amongst other functionalities. The plugin was primarily
selected to perform the thermal behavior analysis to provide answers in relation to passive cooling
schemes based on natural ventilation. This plugin requires the additional installation of Energy Plus in
order to be used. The version of Energy Plus installed by DIVA is not the same that Archsim use,
therefore the adequate version would have to be installed.

22

OCTOPUS (Vierlinger, 2013): Optimization Studies
Octopus is a multi-objective optimization tool developed after the Improved Strength Pareto
Evolutionary Algorithm SPEA-2, the HypE algorithm, and the Grasshopper native evolutionary solver
Galapagos. As Robert Vierlinger puts it, “Octopus introduces multiple fitness values to the optimization.
The best trade-offs between those objectives are searched, producing a set of possible optimum solutions
that ideally reach form one extreme trade-off to the other” (Vierlinger, 2014). This approach of
optimization enables the examination of correlations between different objectives and provide a more
comprehensive arrangement of outcomes compared to single objective optimization analysis (Deb,
2014).

ELK (Logan, 2013): Urban Design
Elk provides a set of utilities to create map and topographical surfaces importing data from the open
source Open Street Map collaboration and the Shuttle Radar Topography Mission (SRTM) data from
USGS (Logan, n.a.). The data is translated into point coordinates that can be further interrelated to
generate contextual geometries.

HELIOTROPE (Lockyear, 2013): Solar Vectors
“Heliotrope is a Grasshopper plug-in for manipulating solar geometry” (Lockyear, 2013). The
functionalities of this tool makes it a very good companion of the energy and daylight simulation tools
in the sense that specific solar conditions can be analyzed with great precision and in an easy manner. Its
relevance is tightly connected with the analysis of conditions that required shading devices based on
solar incidence.
23

DHOUR (Steinfield, 2013): Climate Analysis
This plug-in is a toolkit to perform bioclimatic analysis and visualizations that could assist design
explorations that are driven by climate conditions. The capabilities that were primarily used were the
analysis of yearly temperature to generate shading conditions that would prevent heat gains from direct
solar radiation in seasonal peak hours, as well as its allowance during seasonal cold hours.

LADYBUG (Sadegh, 2013): Climate Analysis
Ladybug is a free open source plugin to support the analysis of environmental data for architectural
purposes. Even though it has many comprehensive capabilities in that regard, for the purposes of this
project the utility that was used was the “view from sun” option, which helped validating the
implementation of shading devices.

SELECTABLE PREVIEW (TAI, 2013): Selectable Grasshopper Geometry in Rhino
This is a simple component that enables the selection of objects created in Grasshopper, within the
Rhino work area, thus allowing the manipulation and use of the sophisticated tools of Rhino to interact
with what was created in Grasshopper. This plug-in was used in connection with other customized
components to model customized fenestration patterns and shading devices.

PANELING TOOLS (ISSA, 2013): Manipulation of Rectangular Grid
This plug-in offers a number of parametric utilities that allow the creation of rectangular grids that can
adapt to any kind of surfaces to facilitate their manipulation and the logical repartition of elements
24

throughout their coordinates. Some capabilities of this plugin were used for creating the windows
modeler user object.

 User Objects
A relevant contribution in this project consisted on the creation of a number of elements to satisfy some
specific needs that were not able to be met with the existing functionalities of the software. These
customized functionalities allowed the adequate elaboration and representation of design solutions and
results.

Geometry Modeler
This utility serve the purpose of generating the geometries that represent the building volumes. The
parameters used to model the building volume were the orientation, number of stories, floor-to-floor
height, floor-to-ceiling height, floor to be studied, and the fragment within the floor that was used for the
simulations.
Windows Modeler
This component supports windows modeling by creating an array in the façade defined by the user. The
parameters to model those arrays consist of the number of windows, the windows’ width, height, and the
sill height. In addition, this component allows the creation of customized windows through the use of
Rhino’s modeling capabilities with the assistance of the plug-in “selectable preview.” That capability
cannot be implemented for optimization studies though.
25

Shading Devices Modeler
The shading devices modeler was based on an innovative approach that use the peak annual hot day
(PAHD), and office schedule. Because a fixed shading scheme implies that the shading will be active
throughout the whole year, both during overheated and under heated periods, creating a shading scheme
based on a PAHD reduces the chance to overcast shadows during under-heated periods where direct
radiation is actually needed. This is in contrast to doing it based on a threshold of daylight hours like the
summer solstice. This component also allows the creation of customized shading devices making use of
the plug-in selectable preview, like when creating customized windows, this capability cannot be
implemented for optimization studies.

Daylighting Visualization
This utility was created to provide a visual understanding of the daylight behavior in buildings. It allows
the examination and comparison of results in both a numerical way (only data), or through a graphical
representation (in the form of false-color meshes) of the grid points simulated.

Thermal Autonomy
Another user object that was created was the one to obtain the thermal autonomy. This component
determines the indoor conditions in terms of hourly thermal comfort, during the year, in a particular
range of hours/day.
This component connects with the plugin Archsim to retrieve results in terms of zone dry-bulb
temperature to further translate them into a thermal comfort value, using the Adaptive Comfort Standard
(ACS) to define the proximity, immersion, or distance from the range of comfort.

26

EUI Calculator
Because there are no current capabilities within Grasshopper to obtain outputs from the simulations in
terms of energy use intensity (EUI), a component was created in order to make the calculation from
kWh (annual) into kBtu/sf/yr.

Figure 1.6 – Software Diagram

1.3.4 WHAT IS NOT INCLUDED IN THE DOMAIN OF STUDY
Some of the aspects that were not included within the domain of study are the following:
- Operable shading devices for daylighting analysis
27

- Implementation of hybrid systems to assess energy consumption encompassing natural ventilation
schemes
- Consideration of different materials (walls, roof, floor) for the optimization studies
- Consideration of different glazing types for the optimization studies
- Modification of predetermined building geometry for the optimization studies

1.4 TOPIC EXPLANATION/ELABORATION: IMPLEMENTATION
1.4.1 CASE STUDY
In order to implement and test the capabilities of the workflow, a case study consisting on examining a
hypothetical commercial building in downtown Los Angeles was created. A typical floor plan was
repeated for 55 stories where the overshadowing conditions from adjacent buildings created different
conditions in terms of daylight and thermal performance for each façade. In order assess the capacity of
the workflow to deal with various simulation conditions, several building zones were partitioned
throughout the building to be examined through the simulations. The zones consisted of the south-west,
and north-east corners of the 7
th
and 51
st
floors (Figure 1.7). A comprehensive description of the case-
study will be given in Chapter 3.

Figure 1.7 - Building Zones

28

1.4.2 DELIVERABLES
1.- Ready to use Workflow
2.- Custom Components (user objects)
- Geometry
- Windows
- Shading Devices
- Daylighting Visualization
- Thermal Autonomy
- EUI Calculator
3.- Evaluation of the workflow through a case study
- Evaluation of the capabilities of the tool
- Evaluation of the results obtained
Chart 1.3 – Deliverables

1.5 TOPIC EXPLANATION/ELABORATION: IMPORTANCE OF THE SUBJECT
The relevance of this project lies in the capacity to facilitate the implementation of informed solutions in
regards to daylight and thermal quality with minimum energy consumption, doing it so in a software
environment that is widely use in the architectural realm. A by-product of the use of this workflow
would consist of reducing the investment of time and money to perform energy and daylight analysis
due the optimization capabilities it offers.

One drawback from existing software tools that perform energy analysis is the steep learning curves
they carry and a lack of seamless integration with the architectural packages. Usually there is a need to
create a new model from scratch or to spend time cleaning geometry from pseudo-imported models to fit
the requirements of the energy simulation tools. This condition can be time consuming and expensive. In
addition to that, the operation of software applications to perform daylight and energy simulations,
29

demand an understanding of several specialized notions of building science in order to asses a design
problem satisfactorily, which narrows the scope of implementation of such tools and might be
cumbersome for designers looking to develop quick schematic studies in early stages of projects.

Designers would benefit from tools capable of performing daylighting and thermal analysis without the
need to know the nuts and bolts of how simulations work, the tools can actually become gateways into
the learning process of performing simulations, it would be helpful to simply follow a predefined route
created by others to arrive to informed solutions for their designs without the need to be an specialist.
Because of this, not so long ago some companies and institutions started developing tools for simulating
the energy performance of buildings in integration with mainstream architectural packages where
designers already work. Some examples of that are for instance the VE-Ware plug-in for Sketchup and
Revit created by Integrated Environmental Solutions (IES), or Autodesk Vasari and Green Building
Studio. Eco-Designer works with ArchiCAD and in the case of Microstation an existing tool is
AECOsim Energy Simulator.

In this case, despite the already existing interventions to bridge simulations with designers in their native
work environments, there are still unexplored horizons with a lot of potential for facilitating this process
through simplifying routines and iterating through multiple solutions in short periods of time with
available parametric design applications and generative modeling tools such as the plugin Grasshopper
for Rhino. Through them, designers are capable of taking the strings over explorations of designs in
ways that have no precedent. Without previous knowledge of scripting, rather through a visual-based
interface, the users of these packages are allowed to define the complex routines that are particular of the
realm of programming but applied to concerns proper to the discipline of architecture, for example
30

iterating through several design possibilities in an automated fashion to improve daylight access and
thermal comfort in buildings for instance. Moreover, once this one-time effort capabilities have been
developed, the utility can be used indefinitely (some maintenance involved), and because the routines
follow a streamline process, they are easy to use for people with basic prior experience on the subject or
software.

31

CHAPTER 2. SIMILAR STUDIES

With the advent of a generative tool like Grasshopper, the scope that designers have to iterate between
different solutions in short periods of time, has arrived to a spot never seen before. In this sense these
kind of tools allow users to assess solutions based on the data or attributes embedded in models in order
to reach a desired result. This carries the opportunity to arrive to different possibilities of design with a
great degree of specificity and customization, simply by modifying the embedded values of the models
rather than having to manually perform modifications on the geometry, as usually happens.

2.1. LITERATURE REVIEW WITHIN GRASSHOPPER
There have been previous studies covering different areas related with the use of Grasshopper to
perform daylight, thermal, and optimization studies. Those projects were examined, and analyzed to
determine current gaps, and to clarify what the contributions of this project would be.

The literature review was separated in two main groups, the first one refers to the approaches that were
created exclusively within the environment of Rhino-Grasshopper, and that were geared towards
examining (and in some cases optimizing) indoor environmentally quality (IEQ) and energy efficiency
considerations in response to building envelope configurations. The second group is identical to the first
one with the difference that it refers to algorithmic approaches that go beyond what has been done
within Rhino-Grasshopper.
The considerations pertaining IEQ and energy efficiency that were taken into account for both groups,
explicitly reduce to the following: Daylighting access, glare, thermal comfort, energy consumption,
natural ventilation, shading systems, and annual based results. Based on those boundaries, the literature
32

review is an effort to compile and present previously relevant efforts to solve energy modeling questions
through the use Grasshopper and in some instances, by other algorithmic design approaches.

Increasing progresses have been made with regards to performing energy simulations with Rhino-
Grasshopper. One of the qualities that generative modeling brought in the arena of architectural design is
that the functionalities that the tool offer are complemented by the capacity to insert scripting code to
attain customized features. Plug-ins in the core of existing approaches for daylight, thermal comfort, and
optimization analysis have determined in some ways the scope and possibilities of the approaches
defined.

2.1.1 DAYLIGHTING AND GLARE
Due development of the plug-in DIVA, which was one of the first attempts to uncover the possibilities
of performing simulations with Rhino-Grasshopper, daylighting and glare could be studied within the
Rhino-Grasshopper environment. Evidence of that can be found in the work of Kera Lagios, Jeff
Niemasz, Christopher Reinhart, amongst others. For instance, in “Animated Building Performance
Simulation (ABPS) - Linking Rhinoceros/Grasshopper with Radiance/Daysim”, published in 2010, they
studied the use of Rhinoceros in connection with Radiance and Daysim to perform daylight simulations.
From there on, more sophisticated approaches started taking place, for example, in 2011 Gagne and
Andersen developed a method for studying daylighting through a design guide depicting the effects of
different design parameters in relation to daylight autonomy and glare.

33

2.1.2 SHADING DEVICES
Another area of exploration for performing simulations within Grasshopper has been the one related
with the examination of shading devices to improve performance. In 2013 Tyler Tucker conducted a
research focusing on studying shading configurations for different building typologies while
determining the variable trends that would lead to better façade design choices. He make use of the plug-
in DIVA, and in addition he implemented Galapagos, the evolutionary solver for grasshopper to perform
optimizations.

2.1.3 DAYLIGHTING AND THERMAL COMFORT
There have already been some examples of integrating daylight and thermal comfort through the use of
DIVA. An example is the demonstration given by Alstan Jakubiec in 2011 to connect DIVA with
DAYSIM and Energy Plus, for daylighting and energy simulations respectively. The methodology was
created to inform designers about daylight behavior and energy consumption of buildings in connection
by making use of the lighting schedule from the daylight simulations and using it for the energy
calculations.

2.1.4 KINETIC FAÇADES FOR DAYLIGHTING
One of the areas in regards to energy simulations that have an important level of difficulty to be
analyzed is that of examining outcomes when using operable shading devices to adapt to climate
conditions. Grasshopper is particularly well fitted to undertake those examinations, an example of what
has been done is a research conducted by Mohamed Mansour El Sheik (2011) to evaluate the
performances of a façade equipped with kinetic louvers that respond to daylighting variations and
occupants´ preferences using a parametric process.
34

2.1.5 NATURAL VENTILATION
Another area that is of great potential to be examined in relation to thermal comfort is natural
ventilation. In 2012 Francisco Valdes and Yuming Sun developed a methodology to perform natural
ventilation simulations of buildings in the early stages of design. This workflow encompassed a set of
tools that connected grasshopper with an equation solver that allowed the architectural scrutiny of the
geometric input and provided natural ventilation feedback while complying with an engineering
meticulosity to assess realistic results.

2.1.6 OPTIMIZATION STUDIES FOR DAYLIGHT AND ENERGY PERFORMANCE
One of the capabilities that stands out from the available palette of functionalities in grasshopper is the
capacity to perform optimization studies. This capability allows the user to examine a set of design
variables to be automatically accommodated to reach a particular objective defined by the user. It
basically consists on the automated iteration between several possible arrangements of parameters to
reach a desired outcome. Because of this, that functionality has not escaped from implementation in
approaches to meet IEQ and energy efficiency assessments. In 2013, Mahmoud Gadelhak presented a
research where he integrated computational and building performance simulation techniques for
optimized façade designs for enhanced daylighting assessments.

In addition other approaches involving optimization analysis have been performed within grasshopper,
in virtue not of evolutionary solvers like Galapagos, but rather simply by the capacity to analyze and
examine multiple results in an easy way through the modification of the parameters generating
increasingly better performance outcomes. An example of that application of optimization approaches is
35

depicted by an analysis performed by Azadeh Omidfar in 2001, where the design optimization of a
contemporary high performance shading screen integrating the ornamental qualities based of
contemporary architecture, with results provided by the simulations in terms of daylighting and energy
consumption.

2.2. LITERATURE REVIEW OUTSIDE GRASSHOPPER
Aside from what has been done within Grasshopper, there have also been efforts to implement
approaches that incorporate similar algorithmic functionalities with the use of other software tools. The
relevance of examining those projects lie in the capacity to examine and compare potential
improvements of what can be done within Grasshopper. For instance in 2007 Luisa Caldas developed a
methodology to assess energy efficiency through the use of GENE_ARCH (an evolutionary based
generative design system), with which multi-objective optimizations were able to be performed. This is
interesting because the capabilities to perform those kinds of analysis were not available for
Grasshopper until the advent of the plug-in Octopus which became available in 2013.

A similar study was the one performed by Yun Kyu Yi and Ali M. Malkawi in 2009 to perform
optimization studies for hierarchical geometry modeling based on energy performance values, which
allow the modeling of site-specific building geometries that are not restricted to boxes or simple forms.

Another example is a thesis research developed by Christian Anker in 2010, through this project a
software tool capable of using a thermal model, a ventilation model accounting for stack and wind
ventilation, as well as a daylight model was developed. The possibilities to examine cooling schemes
36

based on natural ventilation were studied in order to propose envelope solutions that could drastically
reduce energy consumption and improve thermal comfort.

2.3 CRITICAL MATRIX OF LITERATURE REVIEW
In order to have a better understanding of the literature review and in order to facilitate the assessment of
gaps and possible improvements a matrix of studies was created.

Chart 2.1 - Matrix of Literature Review

2.4 EXISTING GAPS

Based on the literature review, different areas where improvements in current methodologies to perform
energy simulations within Grasshopper were identified. It was found that one limitation in similar
approaches consist of the incapability to follow a systematic process to bridge together some of the
different functionalities that are currently available in the software environment used. For instance,
Grasshopper allows the manipulation of geometries with great flexibility, yet there have not been solid
efforts to enable automated geometry modeling capabilities for performing energy simulations.

37

Another aspect that can be improved, is in regards to the use of natural ventilation as a cooling strategy
in buildings. Even though there have been attempts to do this, they have not taken place within the
native environment of Grasshopper and they have been disengaged from daylighting analysis.
As a summary, a listing of certain gaps that were looked to be assessed through this research were the
following:
- Implementation of multi-objective studies
- Implementation of natural ventilation schemes in correlation with daylighting
- Automated modeling of shading devices based on climate data
- Automated geometry creation (windows, shading devices)

It is worth noting, that the progresses reported in various examples of the literature review in terms of
capabilities, scope of work, metrics used, etc., have been sequential, meaning that improvements have
been built on top of precedent work. This research has not been an exception to that, in that sense, some
of the gaps that were bridged with this work (like the multi-objective optimization, and analysis of
natural ventilation), represent an expression of the current frontiers to perform energy simulations within
Grasshopper.

2.5 INNOVATIONS OF THE APPROACH
The manner in which this project differs from previously existing approaches as previously mentioned,
consists of the implementation of some capabilities that have not been used before. There have been
substantial efforts to make use of the potential of Grasshopper to perform daylight and energy
simulations, some have consisted on testing unusual daylighting metrics and approaches (Lagios et al.,
2010), others have combined daylighting with energy simulations (Jakubiec, et al., 2011), in addition to
38

some few examples in which optimization studies of fenestration features for daylighting (Gadelhak,
2013), and shading devices (Omidfar, 2011) have been performed. Yet, no previous attempts have been
done to combine all those capabilities in a single workflow. This project was an effort to integrate those
capabilities, which consist of allowing the automated modeling of building geometries, windows, and
shading devices; the incorporation of natural ventilation as a cooling strategy; and the implementation of
multi-objective analysis for daylighting and thermal quality. Making use of such instruments will allow
users to assess design problems in a more effective and timely fashion.

2.6 KEYWORDS
Keywords: Evolutionary multi-objective optimization, parametric, thermal autonomy, useful daylight
illuminance, daylighting, thermal comfort, shading strategies, environmentally responsive buildings,
workflow.

39
CHAPTER 3. METHODOLOGY AND IMPLEMENTATION
In order to assess daylight quality and thermal comfort, a workflow to develop simulations of Spatial
Useful Daylight Illuminance (SUDI), Thermal Autonomy (TA), and Energy Use Intensity (EUI) was
developed. The creation of the workflow encompassed the creation of custom components in
Grasshopper to perform specialized tasks, an explanation of them will be provided throughout the
chapter.
There are gaps in existing energy simulation tools that make them unsuitable for the studies being taken
including the following:
· The dislocation between daylight and thermal studies.
· The inability to iterate through several design options (>15) in short periods of time.
· The incapacity to calculate the thermal behavior of buildings using thermal autonomy as a
metric.
· The incapacity to automate the process of generating shading devices based on user defined
constraints of peak days and hours.
The capacities and potential of the workflow will be examined through the explanation and presentation
of a case study throughout this chapter. The case study consisted of examining a hypothetical
commercial building in downtown Los Angeles. A typical floor plan is repeated for 55 stories where the
overshadowing conditions from adjacent buildings will create a different outcome in terms of daylight
and thermal performance for each of the facades. In order to test the capability of the workflow to
examine the complexity of the subject, the south-west, and north-east corners of the 7
th
and 51
st
floors
were modeled and simulated.
40
The workflow was divided into three main phases that cover the different aspects of the analysis: 1) the
site or urban context; 2) the geometry of the proposed buildings; 3) the simulations, broken into the
daylight, thermal, and optimization analysis (Figure 3.1).
Figure 3.1 - Workflow Diagram (See Appendix A3.1 for a larger Diagram)
41
Figure 3.2 - Grasshopper Definition (See Appendix A3.2 for a larger Image)
3.1 PHASE 1: SITE
3.1.1 MODELING THE URBAN CONTEXT (GENERAL)
The first step has to do with defining a site for the building to be simulated. This is important because of
the repercussions of adjacent buildings in the daylight and thermal outcomes. In order to do that, a data
file from the open source GIS project “Open Street Map” (Figure 3.3) was loaded into Grasshopper
through the third party plug-in Elk. Once the file has been downloaded, the file should be plugged into
the application by defining its location into a component. Secondly, the data is retrieved for the user´s
purposes; it could be to examine one building, several buildings, high-ways, main streets, local streets,
etc. Because this case study examined the impact of overshadowing of adjacent buildings in regard to
daylight and thermal performance outcomes, and the analysis was performed in a fairly small urban
area, the only data necessary to be retrieved was that related to adjacent buildings. The output of this
data comes in the form of a series of point-coordinates that define the boundaries of existing volumes.
After this data has been gathered it is converted into polylines by connecting the series of points (Fig
3.4).
42
Figure 3.3 - Selection of Urban Context (Open Street Maps)
Figure 3.4 - Translation of data from Open Street Maps into Grasshopper
One of the current limitations to explore urban contexts within Rhino and Grasshopper is the inability of
these software to import three-dimensional entities from GIS or other applications; the user would have
to determine and input the height of each contextual building.
43
3.1.2 MODELING THE URBAN CONTEXT (CASE-STUDY)
For the case-study, the previously mentioned procedure to model the urban conditions was implemented
to model an area in downtown Los Angeles where the overshadowing conditions of adjacent buildings is
particularly complex (Figure 3.5).
Figure 3.5 - Case-Study Urban Context
Overshadowing Analysis
An important element of this workflow is the capacity to assess the impacts in daylight and thermal
performance that surrounding urban conditions play, in particular the overshadowing of the urban
context. In order to provide a better understanding of this, an overshadowing analysis from adjacent
buildings was also modeled based on the site configuration previously created. The building zones that
were selected to be analyzed illustrate the contrasting conditions in relation to the overshadowing of
adjacent buildings in the south-west and north-east corners of the 51
st
and the 7
th
floor.
44
Before performing the analysis it was important to examine the overheated and under-heated periods
during the year in order to determine a Peak Annual Hot Day (PAHD) as well as the Lowest Annual
Cold Day (LACD). This was needed to set the boundaries to determine what are the heating and cooling
needs of the building zones (e.g. when will direct radiation be needed, when will it be undesired, etc.)
during the year. Based on the Energy Plus Weather file (EPW) for the California Climate Zone 09 (Los
Angeles), the PAHD was determined to be the 24th of September, while the LACD was the 13th of
December (for the methodology followed to find these values see Appendix A3). After determining that
information, a study of the different solar incidences through office-hours during those days was
modeled (Figure 3.6). These analyses were performed with the Sun rendering tools within Rhino. In
order to see the shadows casting from the urban context, the viewport was set to Rendered. This was
done by right-clicking the viewport title in the Rhino workspace or by clicking View in the Menu Bar at
the top of the window and then selecting Rendered.
45
Figure 3.6 - Overshadowing Conditions during Peak Annual Hot Day (PAHD) and Lowest Annual Cold Day (LACD)
46
3.2 PHASE 2: GEOMETRY
3.2.1 MODELING BUILDING ZONES (GENERAL)
Figure 3.7 - Geometry Flow-Chart
Figure 3.8 - Example of Other Geometries
The second phase of the workflow consists of modeling the building and the zone/s that will be
simulated. A set of customized components was developed to do this. This phase is subdivided in three
different parts (Figure 3.7): modeling the geometry of the building, modeling the fenestration patterns,
47
and modeling the shading devices to prevent undesired solar radiation throughout the year. The
capability to model complex building geometries is also enabled through the tool (Figure 3.8).
At the same time, the modeling of fenestration patterns and shading devices are subdivided in two
different categories. The first one is the Parametric Modeling Approach, which consists of manipulating
the windows and shading devices using Grasshopper parameters; the second one is the Customized
Modeling Approach, which allow the user the full flexibility to define complex design solutions using
the modeling capabilities of Rhinoceros. In the current state of development of the workflow, the
drawback of the Customized Modeling Approach lies in its incapacity to be used to perform
Optimization studies. More comprehensive explanations of these approaches will be described in section
3.2.2.
3.2.2 MODELING BUILDING ZONES (CASE-STUDY)
For the case study, the basic floor plate of the building was modeled in the form of a polyline and was
fed into the Geometry Modeler component in Grasshopper. The parameters that were used to model the
building and its zones were the orientation, number of stories, floor-to-floor height, floor-to-ceiling
height, floor to be studied, and the fragment within the floor that was used for the simulations (Figure
3.9).
48
Figure 3.9 - Building Modeler: Grasshopper Definition-Inputs and Outputs
Figure 3.10 - Zones Modeling Flow-Chart
Figure 3.11 - Fragments to Study in relation to the Urban Context
49
3.2.3 MODELING WINDOWS (GENERAL)
Figure 3.12 - Windows Modeler, Parameters
There are two ways to create windows, the first one is through an Automated Modeling Approach , which
is the Windows Array option. The second one is through the Customized Modeling Approach , which is
the Customized Windows option. Each option satisfies different needs and therefore would be use to
solve different design problems. In some cases, the design needs will require the uniform arrangement of
windows through the façade, like for instance in a skyrise building where the fenestration pattern
consists of the repetition of a standardized window type along the different levels. In that case the Array
option would be more useful. In addition to that, the current development of the workflow only allow
the implementation of optimizations (which will be explained later on this chapter in section 3.3.3) with
this option. On the other hand, in some cases the freedom to implement more complex fenestration
50
patterns is needed, like in the design of a museum or a library where specific daylight needs have to be
met for example. In those cases using the Customized Windows option would be preferable.
· Windows Array (General)
The first thing to do when generating the windows, regardless of the specific approach is to provide the
windows component (Figure 3.12-A) with the specific wall (B) where the windows are located. After
that, the user will start specifying the different parameters to generate the array of windows (C). The
basic inputs for creating a windows are number of windows, width and height, and sill height. Another
parameter that was incorporated was a “punch-in” value to create windows whose planes are not aligned
with the wall (examples in Figure 3.13). The outputs that the window modeler component provide (D),
are the geometry of the windows, the wall without the area of the openings, and the windows-wall ratio.
The figure 3.14 depicts some examples of different arrangements of windows using the array method.
Figure 3.13 - Windows by Array
· Customized Windows (General)
The approach is considerably different to create customized windows. In this case the creation of
windows consists of creating polylines in Rhino that could be fed into the geometry of the wall where
they will be placed. In order to do this, the third party plug-in Selectable Preview from the Rhino library
is used (Figure 3.12-E). The function of this component is to allow the user to select Grasshopper
objects directly in the Rhino workspace, which is fundamental for the execution of this step. Walls from
51
the Geometry Modeler are fed into this component; the user traces the desired windows in Rhino. One
consideration to be taken into account is that before starting to trace the windows it is important to set a
CPlane to the surface of the wall, with the origin in the lower left corner and the X-axis pointing its right
end, this in addition to drawing the whole boundary of the wall with a curve is needed, otherwise it
would not be possible to reference the windows created with the Grasshopper model (Figure 3.14). Once
all the curves have been traced, they are input into the Window Modeler by loading them into a Curve
component as shown in (F).
Figure 3.14 - CPlane, Wall Polyline and Windows
Figure 3.15 - Examples of Customized Windows
52
3.2.4 MODELING WINDOWS (CASE-STUDY)
They windows for the case-study were designed based on a typological floor plan of a commercial
building, for the fragment that was modeled, the Windows Array option (see 3.2.3) was used. Two fully
glazed façades were created.
Figure 3.16 - Case-Study Building´s Zone Windows (Fully Glazed)
3.2.5 MODELING EXTERIOR SHADING DEVICES (GENERAL)
Figure 3.17 - Grasshopper Definition for Modeling Shading Devices
53
· Optimized Shading Devices Modeler (General)
Window shades are one way to reduce the heat gains during undesired periods of the year. A component
was created to automate the procedure of creating a shading device that would prevent direct solar
incidence through the windows. By defining a latitude and longitude of a location, a Peak Annual Hot
Day (PAHD) in the year, and a set of hours during that day to shade the building, the information
regarding the azimuths, altitudes, and solar vectors can be retrieved to generate a shading device to
cover the whole area of the windows during those periods of time. In relation to this capability, a
drawback in the current state of development of the workflow lies in the incapacity to simulate the
shading devices as operable, activating them only during the cooling season (summer) when they are
primarily needed, rather than during the whole year as it currently occurs. Because of this, creating a
shading scheme based on a PAHD rather than the threshold of summer solstice, reduces the chance to
overcast shadows during underheated periods where direct radiation is actually needed.
In order to generate the geometry of the shading devices, the formulas for obtaining the Vertical Shading
Angle (VSH), and the Overhang Projection (OP) are used (La Roche, 2012) . It is important to note that
the main purpose of the Optimized Shading Devices Modeler is to ensure that the whole areas of the
windows are shaded when specified. Its use has not been intended to provide design solutions for
shading devices, although further developments of this workflow may offer that option in the future.
54
Figure 3.18 - Examples of Optimized Shading Devices
Figure 3.19 - Shading Projections-Validation (September 24, 9:00am – 3:00pm)
Figure 3.20 - Shading Projections-View from the Sun (September 24, 11:00am)
55
· Customized Shading Devices (General)
This method to create shading devices follows the need to allow the inclusion of shading schemes that
can be directly defined by the user without restrictions of design. The value of this approach primarily
lies in the capacity to implement scenarios of existing circumstances in retrofit projects. It is not
recommended to be used as part of the problem-solving framework unless the user has a clear
understanding of the shading conditions that are looked to be analyzed. This is because the impacts that
a given shading device could have in an opening will not necessarily be satisfactory unless it is designed
with some key climatic considerations in mind. In a further development of this workflow, attention
would be given to integrate the Optimized Shading Devices Modeler , with the Customized Shading
Devices option in order to facilitate the modeling of realistic design schemes based on the conditions
that would be needed to be met by the shading devices.
Similarly to how the Customized Windows are created, for Customized Shading Devices , the user will
take advantage of the design tools within Rhino in order to model the desired elements. By using the
Selectable Preview plugin, the user will be able to reference elements of the Grasshopper model into the
workspace of Rhino to implement in a user-friendly manner the desired adjustments. To facilitate this
process it is recommended to become familiar with the use and adjustment of CPlanes (See "Creating
Customized Windows").
56
Figure 3.21 - Examples of Customized Shading Devices
3.2.6 MODELING EXTERIOR SHADING DEVICES (CASE-STUDY)
For this case study there were no exterior devices in the Base-Case, therefore there was no need to
model any of them along the façade of the building. On the other hand, the adjacent buildings have to be
loaded in Grasshopper to be accounted as part of the simulations. In order to reduce the complexity of
the simulations, the only elements that were loaded into Grasshopper were the surfaces that would
directly overcast shadows to the analyzed building any time during the year. The geometries that did not
affected the building´s zones were not used (Figure 3.22)
Figure 3.22 - Shading Elements from Adjacent Buildings
57
3.3 PHASE 3: SIMULATIONS
After creating the building zone with its windows and shading devices, the model is ready to be used to
perform simulation studies, both of daylight and thermal performance. The building zones that were
used for the simulations as previously mentioned were the south-west and north-east corners of the 7
th
and 51
st
floors. This approach was based on the interest to showcase the variations in terms of daylight
and thermal response of the same floor layout in different altitudes of a building based on the contextual
conditions.
3.3.1 CASE STUDY DAYLIGHTING SIMULATIONS
To evaluate annual daylighting performance, a modified version of the metric Spatial Daylight
Autonomy (SDA) was used. SDA describes annual sufficiency of ambient daylight levels in interior
environments (IES, 2012). It is a spatial application of the climate-based daylighting metric Daylight
Autonomy (DA) developed by Reinhart & Walkenhorst, 2001. Spatial Daylight Autonomy is defined as
the percent of an analysis area (typically a floor area or work plane) that meets a minimum horizontal
daylight illuminance level for a specified fraction of operating hours (e.g. 9AM – 5PM local clock time)
per year. The illuminance level (e.g. 300 lux) and annual time fraction (e.g. 50%) are included as
subscripts (e.g. sDA 300,50% ). Daylight illuminance conditions are generated from typical meteorological
year (TMY) data. The approach used was to modify the metric to exclude periods of the year where
interior daylight illuminance within the analysis region exceed 2000 lux.
This modification was implemented as a proxy indicator for glare and follows the threshold approach
used in the Useful Daylight Illuminance (UDI) metric developed by Mardaljevic and Nabil, 2005. In the
proposed workflow, the performance indicator was referred to as Spatial Useful Daylight Illuminance
(sUDI). As an example, a facade configuration that achieves sUDI 100-2000, 50% over 75% of the analysis
58
region is preferred to a configuration that achieves sUDI 100-2000, 50% over only 60% of the same area. In
the proposed workflow, a grid of analysis points located 30-inches (0.8m) above the floor and spaced
48-inches (1.22m) apart is used to record sUDI 100-2000, 50% outcomes in a horizontal plane. Individual
outcomes are then tabulated to determine the percent of the analysis region where the sUDI 100-2000,
50% criteria was met.
PROCEDURE
The third part of the workflow has to do with performing the daylight analysis for the defined geometry.
As mentioned in Chapter 1, the studies of daylighting, thermal comfort, and energy consumption are
performed with the plug-in DIVA (daylighting and energy), and the plug-in Archsim (thermal comfort).
The first step was to define the properties of the building assemblies, windows, and shading devices so
that they could be evaluated for the simulation. After this, another set of parameters were defined in
order to specify the type of simulation and its outputs. A weather file for the particular location of the
project was defined, as well as the metric for the simulation, which was Useful Daylight Illuminance.
Once the entire attributes to perform the analysis were set, then the simulations were run.
SETTINGS
Setting Attribute
· Location and Weather Station: - Los Angeles International Airport
· Simulation Type: - Climate Based
· Occupancy Schedule: - Weekdays 9 to 5with DST.60min
· Minimum Illuminance: - 100 lux
· Lighting Control: - Photosensor Controlled Dimming
· Lighting Parameters: - W 250.0 -Set 300 -Loss 20 -Standby 0.0
· Radiance Parameters: - ab 2 -ad 1000 -as 20 -ar 300 -aa 0.1
· Outputs: - Useful Daylight Illuminance (100-2000 lux)
- Lighting Load Schedule File Path
Chart 3.1 - Settings for Daylight Simulations
59
CONSTANTS
Parameter Attribute
· Terrain Material: - Outside Ground 20
· Building Zone Dimensions
- Floor-Floor height:
- Floor-Ceiling height:
- Depth:
- Width:
- 13 ft
- 9 ft
- 72 ft
- 72 ft
· Building Materials
- Walls:
- Windows:
- Roof:
- Ceiling:
- Shades:
- Floors:
- Generic Interior Wall 50
- Glazing Single Pane 88
- Outside Façade 35
- Generic Ceiling 80
- Outside Façade 35
- Generic Floor 20
· Analysis Grid: - 30” above the floor level
- 4 feet of separation between grid points
· Building Orientation: - Based on existing building (7.76º CCW)
Chart 3.2 - Constant Parameters for Daylight Simulations
VARIABLES
Parameters
· Number of Windows
· Windows Sill-Height
· Windows Dimensions
· Shading Devices (Dependent on Windows)
Chart 3.3 - Variable Parameters for Daylight Simulations
60
Figure 3.23 - Grasshopper Definition for Daylight Simulations using DIVA
Figure 3.24 - Daylight Simulation Visualization
61
3.3.2 CASE STUDY THERMAL SIMULATIONS
· Energy Use Intensity
In order to assess the thermal behavior of the building, the metric Energy Use Intensity (EUI) was used.
This metric is adequate because it expresses the building´s energy use as a function of its size (EPA,
2013). This metric is defined as the energy consumed during the year per square foot, which facilitates
the comparison of energy consumption between different buildings no matter their overall square
footage.
When using EUI as a metric, the energy consumption is measured in kBtu (or GJ) divided by the total
floor area of the building (kBtu/sf/yr). The lower the EUI values, the more energy efficient the building
is. For this study all the energy values will be site specific and will include the consumption from
heating, cooling, electric lights, and the total (Figure 3.26).
Figure 3.25 - Typical EUI Values based on Buildings Typology (EPA)
62
Figure 3.26 - Sample EUI Results Chart
· Thermal Autonomy
Thermal Autonomy (TA) was developed by the design and consulting firm Loisos + Ubbelohde as a
metric to link occupant comfort to climate, building fabric, and building operation. In addition to
assessing annualized thermal comfort conditions, it enables rich visual feedback to architects, engineers,
and building owners for the patterns, degree, and frequency of thermal comfort and discomfort for a
given design over the course of the year (Levitt et al, 2013). TA allows the user to specify the comfort
model used to determine the acceptable temperature range and represents the hours of the year where the
zone temperature is within the acceptability range determined by the model.
A customized component was developed in Grasshopper to determine the TA based on the results of
indoor temperature provided by the thermal simulations performed with the plugin Archsim. The
component allows the specification of the range of hours during the day that will be displayed as a final
output. This makes a difference when analyzing commercial buildings because the results reflect the
indoor temperature of a space without the intervention of a mechanical system to modify it. Therefore it
is important to look to the results based on the range of time when the building is mostly occupied
(Figure 3.27). In order to obtain the TA, the ASHRAE Adaptive Thermal Comfort Model (ASHRAE,
6.7
2.74
6.75
16.19
0
5
10
15
20
Energy Use Intensity
(kBtu/sf/yr)
7th Floor, South-West Corner, "As Is"
Heating Cooling Lights Total
63
2004) was considered in the calculations to allow the acceptability range to vary seasonally in response
to outdoor temperature.
Because the assessment of TA is directly related with natural ventilation, the simulations in Archsim
consisted of analyzing the indoor temperatures in an hourly basis during the year, with the incorporation
of a cooling scheme that considered the effects of wind forces and buoyancy, and discarded the
incorporation of any mechanical system.
Procedure
The fourth step in this process is similar to the third one with the exception that two extra components
were created in order to determine the thermal autonomy (TA) and energy use intensity (EUI) of the
models examined. As noted in section Chapter 1, thermal autonomy defines the percentage of time
during the year that the building temperature is expected to be in a range of comfort, while energy use
intensity reflects the efficiency of energy consumption of buildings despite their overall size.
64
Figure 3.27 - Thermal Autonomy Sample Outputs
65
SETTINGS
SETTING Attribute
(TA Simulations)
Attribute
(EIO Simulations)
· Location - Los Angeles
· Occupancy Schedule - Office
· Run Period - Annual
· Time Steps per Hour - 4
· Solar Distribution - Full Exterior with Reflections
· Shadow Calculation
Thresholds
- 30-15000
· Outputs Reporting (Hourly):
- Indoor Temperature
- Outdoor Temperature
Reporting (Annual):
- Heating Energy (Site)
- Cooling Energy (Site)
- Lights Energy (Site)
Chart 3.4 - Settings for Thermal Simulation
CONSTANTS
Parameter Attribute
(TA Simulations)
Attribute
(EUI Simulations)
· Number of People (people/m2) 0.1 (Default)
· Lighting Load (W/m2) 20 (Default) 20 (Default)
· Equipment Load (W/m2) 4 (Default)
· Heating Set-point (ºC) N/A 22ºC (Default)
· Cooling Set-point (ºC) N/A 26ºC (Default)
· Heating Coefficient of Performance
(COP)
N/A 0.8 (Default)
· Cooling Coefficient of Performance
(COP)
N/A 3.0 (Default)
· Infiltration Rate (Air Changes per Hour) 0.5 (Default) 0.5 (Default)
· Fresh Air ((m3/s)/person) N/A 0.00944 (Default)
· Natural Ventilation - Buoyancy
- Wind Driving
Force
(Defaults)
N/A
· Terrain - N/A, Floor not exposed to terrain
66
· Thermal Zone Dimensions
- Floor-Floor height
- Floor-Ceiling height
- Depth
- Width
- 13ft
- 9 ft
- 72 ft
- 72 ft
· Building Orientation: - Based on Existing Building (7.76º CCW)
· Building Assemblies
- Exterior Walls
- Interior Walls
- Windows
§ U Value
§ Solar Heat Gain Coefficient
§ Visible Transmittance
- Roof
- Ceiling
- Floor
- Shading Elements
§ Solar Reflectance
§ Visible Reflectance
§ Transmittance
§ Glazed Fraction
§ Glazing Construction
- ASHRAE CZ 3 (R-13 + R-3.8)
- Adiabatic
- ASHRAE CZ 3
0.65
0.25
0.45
- Adiabatic
- Adiabatic
- Adiabatic
(Defaults)
0.35
0.35
N/A
0
N/A
Chart 3.5 - Constant Parameters for Thermal Simulation
VARIABLES
Parameters
· Number of Windows
· Windows Sill-Height
· Windows Dimensions
· Shading Devices (Dependent on Windows)
Chart 3.6 - Variable Parameters for Thermal Simulations
67
Figure 3.28 - Grasshopper Definition for Thermal Simulations using the plug-in Archsim
Figure 3.29 - Grasshopper Definition for Energy Simulations using the DIVA component Viper
In order to calculate the EUI of buildings, a customized component was created in order to translate the
outcomes from kWh into kBtu/sf/yr. One important characteristic of this component is that in order to
have a clearer and comprehensive understanding of energy efficiency fostered by daylighting, a formula
to measure the consumption of electricity by artificial lighting in function of the useful daylight
68
illuminance (UDI) was implemented. Therefore the results of energy consumption derived from lights
should be understood as the percentage of the time during the year where UDI levels where not met and
the lights were on (i.e. UDI = 70%, then, Lights = (1-0.7)* electricity when all lights are on ). Since the
implementation of such artificial lighting scheme would rely on an idealized electrical systems in real
projects, the aforementioned formula was not applied to base-case scenarios where the assumption is
that in a retrofit they would not have the appropriate lighting controls to activate the artificial lighting
based on daylight behaviors in the base case scenario.
3.3.3 OPTIMIZATIONS (GENERAL)
The last step of the workflow comes once a base case scenario has been modeled, and the daylight and
thermal simulations have been tested. This task is performed by the plugin Octopus, which is a multi-
objective optimization tool. As Robert Vierlinger puts it, “Octopus introduces multiple fitness values to
the optimization. The best trade-offs between those objectives are searched, producing a set of possible
optimum solutions that ideally reach form one extreme trade-off to the other” (Vierlinger, n.a).
The relevance of performing this optimization analysis comes from the capacity to narrow the possible
outcomes to those that best meet a particular objective. For instance, if the objective is to increase the
daylight access of a building, the optimizations would consist of running multiple iterations in which
each of them would be given different genome values (parameters used to model the building) to
generate various fenestration patterns in order to find the genome values that best satisfy the predefined
objective. An advantage of implementing the use of Octopus, in contrast to other similar optimization
approaches within Grasshopper, is the ability to define multiple objectives to be examined
simultaneously, providing the results that represent the best trades between them.
69
It is necessary to define the parameters (genomes) to perform the optimization; these are modified in the
model to improve a fitness outcome. For example, the genomes could be the sliders modifying the
values for the number of windows, the sill-height, or the windows depth and height for instance (Figure
3.30-A). In addition to defining the genomes, the objectives that are being optimized have to be set as
well. They would be the metrics that are being analyzed, which are the Useful Daylight Illuminance
(UDI) for the daylight studies and the Thermal Autonomy (TA) and Energy Use Intensity (EUI) in the
case of the thermal studies (B). Another thing that can optionally be defined is a phenotype (C). The
phenotype is the group of meshes defining the object that will be modified. This option can be enabled
in order to visualize and keep track of the different iterations performed by Octopus, which can be seen
in the navigation window and can be retrieved by its output node (D).
Figure 3.30 - Octopus as seen in Grasshopper
70
After the scenario for performing the optimizations has been set, the settings are defined. This consists
of selecting the objectives that will be used for the optimizations (Figure 3.31-A) In some instances
some objectives might be wanted to be visualized in response to the results of other objectives without
necessarily being used for obtaining the results, enabling an option to prioritize diversity (B), defining
the algorithms for the analysis (C), and defining the values to permute the optimizations (D). For this
study the default settings were used. There are other settings that can be changed, but they have to do
with the display of the results within the navigation window (E), and other additional things (F). They
were not used.
Figure 3.31 - Octopus Navigation Window - Sample Output
71
3.3.4 OPTIMIZATIONS (CASE-STUDY)
Only the UDI and the EUI were used to find results within Octopus. The reason for that was that since
the implementation of TA is still in an early stage (it is still not possible to calculate it effectively due
the incapacity to simulate natural ventilation), it was only used for visualization and compared against
the results of the other metrics. In addition another set of optimizations would have been necessary to be
run in order to analyze it effectively against UDI. This is because the EUI is a metric with which TA can
only be compared with since both have are used for thermal analysis and both are simulated using
different settings for the thermal zone. EUI works with the output of energy consumed by mechanical
systems, where TA discounts all mechanical systems to evaluate the behavior of the building based on
the envelope (See Chapter 1 to find additional information).
SETTINGS
SETTING Attribute
· Elitism 0.5
· Mutation Probability 0.1
· Crossover Rate 0.8
· Population Size 30
· Multi-objective Algorithm - SPEA-2 Reduction
· Mutation Algorithm - HypE Mutation
· Objectives
- EUI
- UDI
- Thermal Autonomy:
- Genetic Diversity
- Active
- Active
- Inactive (Just Visualization)
- Active
Chart 3.7 - Octopus Settings
72
GENOME (Parameters)
Parameters Attributes
· Window/s Width 1-71 ft
· Window/s Height 1-13
· Number of Windows 0-10
· Sill Height 0-12
· Shading Device On/Off
Chart 3.8 - Octopus Genome Values (Parameters)
The optimizations were performed on the south-west sections of the 51
st
and 7
th
floors.
3.4 SAMPLE RESULTS
After the optimizations, optimal fenestration configurations were compared and ranked using
performance outcomes. The results are visualizations of the different results generated from the
simulations. For the daylight studies the results would be the set of values corresponding to each control
node for the simulation in regards to the defined metric, in this case the Useful Daylight Illuminance.
The values would be the percentage of time during the year that the particular control node is within the
specified threshold of luxes. That outcome is graphically seen with a color gradient showing the daylight
variations created by all the control nodes set within the examined space during the year. The horizontal
plane is used to examine the output values of illuminance throughout a plane located thirty inches from
the floor within the range where tasks are usually performed.
In the other hand, the thermal studies would be understood in terms of Thermal Autonomy and Energy
Use Intensity. Thermal Autonomy is shown by a chart depicting the hours during the year in which the
space would be within each of the different ranges of temperature used to evaluate the thermal comfort.
73
The main output of Thermal Autonomy is the obtaining the values in which a particular space would be
within the comfort zone during the year, which could be in terms of the total of hours or as a yearly
percentage, for this study the emphasis will be given to the yearly percentage.
For the Energy Use Intensity the results are shown on bar charts displaying the values of site energy
consumption related with heating, cooling, and interior lighting fixtures.
In the end, the results of all the different iterations will be compared against each other in charts to
explicitly manifest the variations amongst the three different metrics studied.
· Assessment of Research Objectives
A simple way to determine if the appropriate assessment of the research objectives would be by looking
at the results displayed by throughout the different stages of the workflow. The intended goals of this
workflow were to analyze the daylight and thermal response of buildings in complex urban settings by
implementing certain customized components and making use of already existing state of the art
functionalities within the Grasshopper environment. Therefore, the results will reflect the degree of
effectiveness in which the current workflow is operating, in other words, by making it work in the first
place would be the first indicator of success, after that it would be a matter of how good, fast, and easy
to use it is in contrast with similar workflows.
74
7
th
Floor SW Fragment
“Base-Case”
Window-Wall Ratio: 93%
Figure 3.32.1 - Building Zone
Figure 3.32.2 - Useful Daylight Illuminance
Figure 3.32.3 - Thermal Autonomy
Figure 3.32.4 - Energy Use Intensity
0
5
10
15
20
25
EUI (kBtu/sf/yr)
Heating Cooling Lights Total
75
7
th
Floor SW Fragment
“Optimization 1”
Window-Wall Ratio: S-83%, W-39%
Figure 3.33.1 - Building Zone
F
i
F
Figure 3.33.2 - Useful Daylight Illuminance
Figure 3.33.3 - Thermal Autonomy
Figure 3.33.4 - Energy Use Intensity
0
5
10
15
20
25
EUI (kBtu/sf/yr)
Heating Cooling Lights Total
76
Figure 3.34 - Sample Visualizations Comparison
Figure 3.35 - Sample Charts Comparison
3.5 SUMMARY
An explanation of the assessment of daylight quality and thermal comfort, through the development of a
workflow that performs simulations of Spatial Useful Daylight Illuminance (SUDI), Thermal Autonomy
(TA), and Energy Use Intensity (EUI) while analyzing their interrelations through multi-objective
optimizations was developed and tested. The creation of the workflow encompassed the creation of
custom components in Grasshopper to perform specialized tasks such as modeling building geometries,
fenestration schemes, shading devices, in addition to visualizing the outputs of sUDI and TA.
0
20
40
60
80
100
Yearly %
Spatial Useful Daylight
Illuminance (sUDI)
51st Floor_As-Is
7th Floor_As-Is
0
20
40
60
80
100
Yearly %
Thermal Autonomy (TA)
51st Floor_As-Is
7th Floor_As-Is
0
0.5
1
1.5
2
kBtu/sf/yr
Energy Use Intensity (EUI)
51st Floor_As-Is
7th Floor_As-Is
77
Some of the gaps in existing energy simulation tools that were assessed by this workflow are the
following:
· The dislocation between daylight and thermal studies.
· The inability to iterate through several design options (>15) in short periods of time.
· The incapacity to calculate the thermal behavior of buildings using thermal autonomy as a
metric.
· The incapacity to automate the process of generating shading devices based on user defined
constraints of peak days and hours.
Figure 3.36 - Workflow Diagram (See Appendix A3.1 for a larger Diagram)
78
Figure 3.37 - Grasshopper Definition (See Appendix A3.2 for a larger Image)
79

CHAPTER 4. RESULTS

The purpose behind this research consist of developing a workflow to support decision-making during
early stages of design and retrofit scenarios towards environmentally responsive goals through the
implementation of a set of novel capabilities. These capabilities include the assessment of complex
urban settings; the implementation of cooling schemes based on natural ventilation, using the emerging
metric of thermal autonomy; automating the modeling of shading devices based on an annual peak hot
day; and performing multi-objective optimization analysis to correlate and determine best trade-offs
between daylighting and thermal performance. Even though there have been several substantial efforts
to make use of the potential of Grasshopper to perform daylight and energy simulations (Lagios,
Jakubiec, Gadelhak, Omidfar, etc), no previous attempts have been done that combine the capabilities
incorporated in this workflow. For instance, thermal autonomy is an emerging metric which has not
been incorporated in any mainstream software application thus far, making this a unique capability of
this particular work.

The relevance of this workflow consist on the capacity to reduce the gap towards attaining net zero
energy goals in early stages of design and retrofit scenarios. By enabling users to perform optimization
analysis of daylight performance and thermal comfort in correlation, to determine the adequate
fenestration patterns and shading systems within complex urban settings, the capacity to reduce energy
consumption through passive means is maximized. A case-study to examine the capabilities of the
workflow was created, even though the methodology was presented in the previous chapter, it will be
reintroduced in a summarized way in the beginning of this chapter. Its final results will be portrayed and
partially discussed. More comprehensive discussion issues will be presented in the Chapter 5.

80

4.1 METHODOLOGY (RECAPITULATION)

Figure 4.1 - Selection of Urban Context (Open Street Maps)

The case-study to examine the functionality of the workflow consisted of a hypothetical office building
in downtown Los Angeles. A typical floor plan was repeated for 55 stories. The south-west and north-
east corners of the 7th and 51st floor were examined as the overshadowing conditions from adjacent
buildings create a different outcome in terms of daylight and thermal performance for these specific
cases. The workflow is divided in three major steps: site and overshadowing analysis, geometry input,
and simulations.

81

4.1.1 PHASE 1: SITE AND OVERSHADOWING ANALYSIS
The first phase was to model the site in its urban context and analyze the overshadowing of adjacent
buildings. The translation of 2D data from Open Street Maps was done with the Grasshopper component
Elk. Then the buildings were extruded within Rhino.

Figure 4.2 - Selection of Urban Context (Open Street Maps)

The Energy Plus Weather File (EPW) for Los Angeles (climate zone 09) was used to determine the
lowest annual cold day (December 13th) and the peak annual hot day (September 24th). These dates
were used to define when direct radiation would be desired or not during the year. Using the urban
context and the solar angles for these days during regular work hours, the overshadowing conditions for
the thermal zones were examined. The peak annual hot day lead to the design of the overhangs for the
offices.
4.1.2 PHASE 2: GEOMETRY
With a set of customized components, the second phase consisted of modeling the building volume with
its multiple floors, the windows (by creating an array or through customizing their designs and
locations), and the shading devices.

82

 Modeling the Building Zones (Space, Windows, and Shading Devices)
The parameters used to model the building volume were the orientation, number of stories, floor-to-floor
height, floor-to-ceiling height, floor to be studied, and the fragment within the floor that was used for the
simulations.

Figure 4.3 - Building Zones, Case Study Base-case Zones Model

 Modeling the Windows
Windows are modelled by defining a particular array in the façade. The parameters to model those
arrays consist of the number of windows, the windows’ width, height, and the sill height. In addition, the
workflow allows for the creation of customized windows through the use of Rhino’s modeling
capabilities (that capability does not applied for the optimization studies).
 Modeling the Shading Devices
The shading devices were based on an innovative approach that used the peak annual hot day (PAHD),
office schedule, and over shading analysis of neighboring buildings. Because a fixed shading scheme
implies that the shading will be active throughout the whole year, both during overheated and under
heated periods, creating a shading scheme based on a PAHD reduces the chance to overcast shadows
during under heated periods where direct radiation is actually needed. This is in contrast to doing it
based on a threshold of daylight hours like the summer solstice.

83

4.1.3 PHASE 3: SIMULATIONS
The final phase is to evaluate annual daylighting performance, thermal comfort, and energy
consumption. To do this the emerging metrics of Useful Daylight Illuminance (UDI) (Mardaljevic and
Nabil, 2005), Thermal Autonomy (TA) (Levitt et al, 2013), and Energy Use Intensity (EUI) were
implemented. In order to consolidate the simulations, a multi-objective optimization analysis was
incorporated.
 Spatial Useful Daylight Illuminance (sUDI)
To evaluate annual daylighting performance, a modified version of the metric Spatial Daylight
Autonomy (sDA) was used. Spatial Daylight Autonomy describes annual sufficiency of ambient
daylight levels in interior environments (IES, 2012). Our approach was to modify the metric to exclude
periods of the year where interior daylight illuminances within the analysis region were lower than 100
lux and exceeded 2000 lux. “These limits are based on reports of occupant preferences and behavior in
daylit offices with user-operated shading devices.” (Nabil and Mardaljevic)
This modification was implemented as a proxy indicator for glare and follows the threshold approach
used in the Useful Daylight Illuminance (UDI). A grid of analysis points located 30-inches (0.8m) above
the floor and spaced no more than 48-inches (1.22 m) apart is used to record sUDI 100-2000 outcomes.
Individual outcomes are then tabulated to determine the percent of the analysis region where the
sUDI100-2000 criteria was met.
 Energy Use Intensity (EUI)
This metric was used because it expresses the building´s energy consumption as a function of its use.
This metric is defined as the energy consumed during the year per square foot, which facilitates the
comparison of energy consumption between different buildings no matter their overall size. When using
84

EUI as a metric, the energy consumption is measured in kBtu (or GJ) divided by the total floor area of
the building (kBtu/sf/yr). The lower the EUI values, the more energy efficient the building is.
 Thermal Autonomy (TA)
Thermal autonomy was developed by the design and consulting firm Loisos + Ubbelohde as a metric to
link occupant comfort to climate, building fabric, and building operation. In addition to assessing
annualized thermal comfort conditions, it enables rich visual feedback to architects, engineers, and
building owners for the patterns, degree, and frequency of thermal comfort and discomfort for a given
design over the course of the year. Thermal autonomy allows the user to specify the comfort model used
to determine the acceptable temperature range and represents the hours of the year where the zone
temperature is within the acceptability range determined by the model.
A customized component was developed in Grasshopper to determine the TA based on the results of
indoor temperature provided by the thermal simulations performed with the Archsim plugin. In order to
obtain the TA, the ASHRAE Adaptive Comfort Standard (ASHRAE, 2004) was considered in the
calculations to enable the acceptability range to vary seasonally in response to outdoor temperature.
 Optimization
The last step of the workflow was to provide an optimal solution. This task was performed by the plugin
Octopus. The optimization studies consisted of running three generations (sets) with a population of 30
elements each to determine the best correlations between sUDI and TA. In addition, EUI was also
visualized to support the assessment and understanding of the results.
The design variables (parameters) that were considered for the simulations were the width, height, sill-
height, number of units, and the activation of the shading devices, all in relation to the windows design.
85

Octopus iterates through different arrangements of the design variables to determine the best
combinations, the ones that define the optimum fenestration patterns.
Each of the four zones were analyzed (7th floor south-west corner, 51st floor south-west corner, 7th
floor north-east corner, and 51st floor north-east corner), from all the results the best six solutions were
retrieved.
4.2 RESULTS
The results are organized into four groups. The first group represents the comparison between the base-
case scenarios with the best optimization result for each zone analyzed. The second group is composed
of a matrix of results including the spatial visualizations for each zone. The third group is the
compilation of the results of all the different zones together. In group four, a set of charts with the
overall improvements of the different iterations over the base-case scenario are included.
4.2.1 GROUP 1: IN DEPTH EXAMINATION OF EACH SIMULATION

Introduction

The first group of results manifest the details behind each scenario simulated, because of that, the data
can be comprehensively used and examined. For instance, in the case of UDI the visualization include
the detailing of the analysis values for each control point in the grid. In the case of the TA, the chart
includes a stratification of the results based on the temperature level, which ensures a better
understanding of the results. Because there are many results, this chapter only presents the base-case
scenarios with the best optimization result for each zone analyzed, the other results can be find in the
Appendix A4.1.
86

7
th
Floor SW Fragment
Base-Case
Window-Wall Ratio: 93%
Figure 4.4.1 - Building Zone

F
i
g
u
r
e

F
igure 4.4.2 - Useful Daylight Illuminance

Figure 4.4.3 - Thermal Autonomy

Figure 4.4.4 - Energy Use Intensity
0
5
10
15
20
25
EUI (kBtu/sf/yr)
Heating Cooling Lights Total
87

7
th
Floor SW Fragment
“Optimization 1”
Window-Wall Ratio: S-83%, W-39%

Figure 4.5.1 - Building Zone

F
i

F
Figure 4.5.2 - Useful Daylight Illuminance

Figure 4.5.3 - Thermal Autonomy

Figure 4.5.4 - Energy Use Intensity
0
5
10
15
20
25
EUI (kBtu/sf/yr)
Heating Cooling Lights Total
88

51
st
Floor SW Fragment
Base-Case
Window-Wall Ratio: 93%

Figure 4.6.1 - Building Zone

F
i

F
Figure 4.6.2 - Useful Daylight Illuminance

Figure 4.6.3 - Thermal Autonomy

Figure 4.6.4 - Energy Use Intensity
0
5
10
15
20
25
EUI (kBtu/sf/yr)
Heating Cooling Lights Total
89

51
st
Floor SW Fragment
“Optimization 5”
Window-Wall Ratio: S-34%, W-53%

Figure 4.7.1 - Building Zone

F
i

F
Figure 4.7.2 - Useful Daylight Illuminance

Figure 4.7.3 - Thermal Autonomy

Figure 4.7.4 - Energy Use Intensity
0
5
10
15
20
25
EUI (kBtu/sf/yr)
Heating Cooling Lights Total
90

7
th
Floor NE Fragment
Base-Case
Window-Wall Ratio: 93%

Figure 4.8.1 - Building Zone

F
i

F
Figure 4.8.2 - Useful Daylight Illuminance

Figure 4.8.3 - Thermal Autonomy

Figure 4.8.4 - Energy Use Intensity
0
5
10
15
20
25
EUI (kBtu/sf/yr)
Heating Cooling Lights Total
91

7
th
Floor NE Fragment
“Optimization 3”
Window-Wall Ratio: N-85%, E-22%

Figure 4.9.1 - Building Zone

F
i

F
Figure 4.9.2 - Useful Daylight Illuminance

Figure 4.9.3 - Thermal Autonomy

Figure 4.9.4 - Energy Use Intensity
0
5
10
15
20
25
EUI (kBtu/sf/yr)
Heating Cooling Lights Total
92

51
st
Floor NE Fragment
Base-Case
Window-Wall Ratio: 93%

Figure 4.10.1 - Building Zone

F
i

F
Figure 4.10.2 - Useful Daylight Illuminance

Figure 4.10.3 - Thermal Autonomy

Figure 4.10.4 - Energy Use Intensity
0
5
10
15
20
25
EUI (kBtu/sf/yr)
Heating Cooling Lights Total
93

51
st
Floor NE Fragment
“Optimization 5”
Window-Wall Ratio: N-60%, E-41%

Figure 4.11.1 - Building Zone

F

Figure 4.11.2 - Useful Daylight Illuminance

Figure 4.11.3 - Thermal Autonomy

Figure 4.11.4 - Energy Use Intensity
0
5
10
15
20
25
EUI (kBtu/sf/yr)
Heating Cooling Lights Total
94

GROUP 1: Conclusion
By examining the results of the base-cases in this group, it was clearly manifested that the thermal
autonomy in particular was not satisfactory. This is because the base-case scenarios are sealed envelopes
without any sort of natural ventilation, therefore the indoor spaces are hot most part of the time (Figures
4.4.3, 4.6.3, 4.8.3, 4.10.3). In the case of the best optimization results, it is easy to see the drastic
changes occurred in all the different metrics analyzed, it is particularly interesting to see that UDI values
were improved against the base-cases even though they consist of fully glazed façades. That occurs
because the metric of UDI poses an upper limit of illuminance, penalizing those conditions in which
daylight is excessive according to this threshold.

4.2.2 GROUP 2: MATRIX OF RESULTS FOR EACH ZONE
Introduction

The second aggrupation of results present the compilation of the collection of base-cases and best-cases
for each different zone separately. This aggrupation of results is very useful because through a mosaic
arrangement of the outcomes, the various possibilities of design can be compared and analyzed against
the rest, for each particular zone. Some of the highlights that these figures present are the explicit
differences between daylight and thermal behaviors, as well as the correlations between them in
reference to specific fenestration patterns, and window-wall-ratio.
95

Figure 4.12 - 7
th
Floor, South-West Corner Comparison
96

Figure 4.13 - 51
st
Floor, South-West Corner Comparison
97

Figure 4.14 - 7
th
Floor, North-East Corner Comparison
98

Figure 4.15 - 51
st
Floor, North-East Corner Comparison
99

GROUP 2: Conclusion
The results presented in the second group clearly depicted the important improvements in terms of
thermal comfort, through passive means, that come after implementing natural ventilation capabilities in
the simulations. Going even further, the figures in the second group portray some very interesting
notions in regards to daylight and thermal behaviors, for example, the 3
rd
and 4
th
optimizations of the
south-west corner in the 7
th
floor, have very similar outcomes even though they have drastically
different fenestration patterns (Figure 4.12). The 3
rd
optimization has a UDI of 65%, a TA of 71%, and
an EUI of 12, while the 4
th
optimization has a UDI of 65%, a TA of 70%, and an EUI of 13. Yet, the 3
rd

opt. has a window-wall ratio (WWR) of 14% for the south façade, and a WWR of 75% in the west
façade, while the 4
th
opt. has a WWR of 85% in the south, and a WWR of 35% in the west. More
information in regards to these findings will be given the Chapter 5 (Discussion).

4.2.3 GROUP 3: MATRIX OF RESULTS OF ALL ZONES TOGETHER

Introduction
This group compiles the results retrieved from the optimization analysis for each zone and represent the
results in the form of graphics. The advantage of this representation of results lies in the possibility to
compare and analyze all the different fenestration patterns and results in one singular arrangement. Each
vertical block of graphs accompanied by a vertical column of design solutions represent each zone.
From first sight, something that stands out is the diversity of fenestration possibilities.
100

Figure 4.16 - Matrix of Results

101

GROUP 3: Conclusion
By this group it was possible to define trends occurring not only in regards to one single zone, but in
regards to all of them, which means that rough approximations could be asserted about how the building
in general might respond to the climate and urban conditions in which it sits. For instance, the UDI
values demonstrate that the south-west orientations has less overall daylight quality than the north-
eastern ones, this would be because UDI has un upper threshold of 2000 lux, which penalizes spaces
where levels go higher, because sunlight comes primarily from the south, all zones oriented to this
direction will exceed that limit, bringing the overall lower daylight levels than northern orientations.

4.2.4 GROUP 4: IMPROVEMENT CHARTS

Introduction

The presentation of results is given in the form charts to explicitly inform the values of the results and
the improvements over the base-case in each zone. The first chart presents specifically the best outcome
in each zone, while the rest of the charts present all the outcomes for each zone.

BEST-CASES
EUI
(kBtu/sf/yr)
EUI
Improvement
Over Base-Case
(kBtu/sf/yr)
sUDI
sUDI %
Improvement
Over Base-Case
TA
TA %
Improvement
Over Base-Case
7th Floor SW Opt1 13 -11 71% 4% 70% 48%
51st Floor SW Opt5 11 14 76% 16% 86% 45%
7th Floor NE Opt3 10 12 79% 5% 78% 29%
51st Floor NE Opt5 11 12 79% 6% 85% 13%
Chart 4.1 - Best-Cases, Improvements Over Base-cases

102

7th Floor SW
EUI
(kBtu/sf/yr)
EUI
Improvement
Over Base-Case
(kBtu/sf/yr)
sUDI
sUDI %
Improvement
Over Base-Case
TA
TA %
Improvement
Over Base-Case
Base-Case 24 N/A 66% N/A 22% N/A
Base-Case, NV, LC 15 8 66% 0% 66% 44%
Base-Case, NV, LC, SD 11 12 72% 6% 79% 57%
Opt1 13 11 71% 4% 70% 48%
Opt2 13 10 64% -3% 71% 49%
Opt3 12 11 65% -1% 71% 49%
Opt4 13 10 65% -2% 70% 48%
Opt5 13 10 63% -3% 70% 48%
Opt6 13 10 55% -11% 73% 51%
Chart 4.2 - 7
th
Floor South-West Corner Improvements Over Base-cases

51st Floor SW
EUI
(kBtu/sf/yr)
EUI
Improvement
Over Base-Case
(kBtu/sf/yr)
sUDI
sUDI %
Improvement
Over Base-Case
TA
TA %
Improvement
Over Base-Case
Base-Case 26 N/A 60% N/A 41% N/A
Base-Case, NV, LC 18 7 60% 0% 75% 34%
Base-Case, NV, LC, SD 12 13 87% 27% 80% 39%
Opt1 12 14 72% 12% 88% 47%
Opt2 12 14 66% 7% 88% 46%
Opt3 11 14 67% 7% 90% 49%
Opt4 12 14 71% 11% 88% 47%
Opt5 11 14 76% 16% 86% 45%
Opt6 13 12 72% 12% 87% 46%
Chart 4.3 - 51
st
Floor South-West Corner, Improvements over Base-cases

7th Floor NE
EUI
(kBtu/sf/yr)
EUI
Improvement
Over Base-Case
(kBtu/sf/yr)
sUDI
sUDI %
Improvement
Over Base-Case
TA
TA %
Improvement
Over Base-Case
Base-Case 22 N/A 75% N/A 49% N/A
Base-Case, NV 13 9 75% 0% 73% 24%
Base-Case, NV, SD 11 11 83% 9% 75% 26%
Opt1 10 12 80% 5% 76% 27%
Opt2 9 12 76% 1% 77% 28%
Opt3 10 12 79% 5% 78% 29%
Opt4 11 11 74% -1% 76% 27%
Opt5 11 11 63% -11% 78% 29%
Opt6 10 12 75% 0% 79% 30%
Chart 4.4 - 7
th
Floor North-East Corner, Improvements over Base-cases

103

51st Floor NE
EUI
(kBtu/sf/yr)
EUI
Improvement
Over Base-Case
(kBtu/sf/yr)
sUDI
sUDI %
Improvement
Over Base-Case
TA
TA %
Improvement
Over Base-Case
Base-Case 23 N/A 73% N/A 72% N/A
Base-Case, NV, LC 14 9 73% 0% 82% 10%
Base-Case, NV, LC, SD 13 10 83% 10% 82% 10%
Opt1 11 12 69% -4% 89% 18%
Opt2 11 12 73% 0% 87% 15%
Opt3 11 12 73% 0% 87% 15%
Opt4 11 12 65% -8% 89% 18%
Opt5 11 12 79% 6% 85% 13%
Opt6 11 12 80% 7% 84% 12%
Chart 4.5 - 51
st
Floor North-East corner, Improvements over Base-cases

GROUP 4: Conclusion
The group of results helped determine specific ranges of improvement over base-cases. For example,
when looking at the best scenarios for each zone (Chart 4.1), the most outstanding finding was the
increase in terms of thermal autonomy ranging between 13 to 48% against the base-cases. In the case of
daylight performance, we can see that the zone with most dramatic improvement was the 51
st
floor
oriented to the south-west (Chart 4.3), with a range of improvement of 7 to 27% against the more
modest, or even detrimental, outcomes of the other zones like in the case of the 51
st
floor oriented
towards the north-east, which have outcomes of -8 to 10% against the base-case. The reason for that is
that by having a base-case with fully glazed façades, the possible improvements in an orientation facing
south are higher than one facing north, in other words it is more difficult to improve the orientation
facing north because there is no direct sunlight coming to the space, meaning that daylight quality is
usually within a satisfactory range. That goes in contrast with the south-west orientation where direct
sunlight is present on a regular basis, affecting the overall quality of daylight in the space. More
information will be given in the Chapter 6 of this document.

104

4.3 CHAPTER CONCLUSIONS

As seen throughout this chapter, the workflow proved useful to provide results in a variety of levels.
Very important, is the fact of recognizing the capacity it showed to retrieve outcomes that were specific
to conditions of the zones analyzed, this can be considered as a validation of the underlying objective to
perform simulations in complex urban conditions. Additionally other capabilities that were defined for
this work were met, such were the cases of correlating daylight and thermal outcomes, implementation
of thermal autonomy as a metric, and performing multi-objective studies.

By this case-study, it was proven that with support of the results, the user is in a good stance for making
informed choices about specific design problems. As seen in Chart 4.5, the improvements for each zone
is considerably important, demonstrating how building designs can be improved. An important
consideration to put on the table after examining the results, is the advantage that performing multi-
objective analysis have to output a wide variety of design possibilities that in terms of performance
respond more or less in the same way. The positive aspect of that, is that it provides the freedom to
select an option based on complex qualitative attributes that only the team of people working behind the
simulations can determine (i.e. the views towards a particular direction, the aesthetics of particular
fenestration patterns, the threat they pose against birds, etc). This is a capability that was encountered
after the results were inspected.

105

CHAPTER 5. DISCUSSION

Figure 5.1 - Discussion Summary

106

5.1 THE WORKFLOW

The research objective was to develop a workflow to support decision-making during early stages of
design and retrofit scenarios towards environmentally responsive goals through the implementation of a
set of novel capabilities within the Rhino 3D-Grasshopper environment. These capabilities include the
assessment of intricate urban contexts; the implementation of natural ventilation for cooling; making use
of the emerging metrics of thermal autonomy (TA) and useful daylight illuminance (UDI); allowing the
automation of shading devices modeling based on climate data; and performing multi-objective
optimization analysis to correlate results between daylighting and thermal analysis.
The final workflow as demonstrated in the case study achieved many of its objectives in that it was able
to provide:
 A strong connection between daylight and thermal studies through the correlating their results.
 Multi-objective optimization of thermal versus daylight parameters.
 The ability to iterate through several design options in short periods of time (+-100 in one work day).
 A method to calculate the thermal behavior of buildings using thermal autonomy as a metric.
 The capacity to automate the process of generating shading devices based on user defined constraints
of peak days and hours.

An important contribution consisted of the elaboration of a set of “User Objects” (custom components),
to model the building geometry, the windows, and the shading devices; and also to visualize the results
of the daylighting simulations, TA, and the calculation of energy use intensity (EUI). These customized
functionalities were important not only because of the functionality they met, but because they
complement the plug-ins incorporated and facilitate the use of the workflow.

107

This method differs from previously existing approaches in the implementation of the capabilities
previously mentioned. There have been several substantial efforts to make use of the potential of
Grasshopper to perform daylight and energy simulations. Some have consisted on testing unusual
daylighting metrics and approaches (Lagios et al., 2010). Others have combined daylighting with
thermal simulations (Jakubiec, et al., 2011). There have been a few examples in which optimization
studies of fenestration features for daylighting and shading devices have been performed (Gadelhak,
2013) (Omidfar, 2011). Yet, no previous attempts have been done to combine the capabilities
incorporated in this workflow. Making use of this workflow will allow user to assess design problems in
a more effective and timely fashion.

5.2 THE RESULTS
This part of the chapter will present a discussion of the simulations performed based on the results of the
case-study. Some of the questions that will be addressed, supported by the different results obtained in
each of the different zones analyzed, would be those related with the role of urban conditions and
orientation on daylighting and thermal performance, and the correlations between the results of the
daylight and thermal simulations. This will encompass an examination into each of the different metrics
incorporated. It will be of particular importance to examine the results in terms of useful daylight
illuminance (UDI) and thermal autonomy (TA) since both are emerging metrics. In the case of the UDI,
it will be interesting to determine which areas in the results exceed the upper threshold of 2000 lux to
speculate about the connection between them and possible glare issues. TA will be examined to
determine the degree in which natural ventilation will satisfy the cooling demands of buildings. To
conclude with this chapter, the overall improvements of the optimization studies against the base-case
scenario will be inspected to provide answers in regards to the overall potential and capabilities of the
workflow.
108

5.2.1 ASSESSMENT OF COMPLEX URBAN SCENARIOS AND ORIENTATION (ANALYSIS OF
EACH ZONE)
The best way to analyze the impacts of urban conditions and the orientation of buildings is by looking
into the results of the first three simulations in the set of each of the different zones: 1) Base-case
scenario, 2) Base-case with natural ventilation and improved lighting controls, 3) Base-case with natural
ventilation, improved lighting controls, and shading devices (Figure 5.2).
The variations in the outcomes due to the different overshadowing conditions from adjacent buildings,
as seen in the comparisons between the 7
th
and 51
st
floors, indicate that when no shading devices are
present, having direct exposure through most part of the year is detrimental both in terms of UDI and
energy consumption. In the case of UDI, it will be due the upper threshold of 2000 lux that would
penalize the overall performance when exceeded. In the other hand, for the EUI, having increased solar
radiation would raise the energy consumption for cooling the building. That is why for the first two
scenarios the 7
th
floor in the SW corner show better outcomes than the 51
st
floor, because the
overshadowing of adjacent buildings plays in favor to improving the objectives. In contrast to that, the
best performances are when the different zones are appropriately shaded, in which case, the 51
st
floor of
the SW corner represent a better outcome that the 7
th
floor because the latter is overshaded by adjacent
buildings. In the case of the results for the NE orientations, it is easy to see that they provided better
outcomes because they were not overlit by continuous direct sunlight exposure.

In the case of TA, except fot the base-case scenarios, there are two main trends that can be recognized.
The first one is that the outcomes for the SW orientations are lower than those oriented towards NE, and
the other is that lower floors tend to have lower performance than upper floors. In the first trend, the
explanation can ituitively be considered as having to do with solar incidence in spaces, turning them
109

warmer in southern orientations compared to those oriented to the north. For the second trend, what can
be inferred is that possibly there is a variation in the natural ventilation calculations of energy plus,
where higher coefficients of wind pressure are given based on the elevation of the anzlyzed zones. This
is only an hypothesis that would have to be validated with further research. For instance, simulating the
same zones in a different climate would bring more evidence to clarify that point. Something that also
needs to be ellucidated, are the outcomes obtained by the base-case scenario, where the TA results looks
disproportionate with the other scenarios. Both of the previously mentioned trends are dramatically seen
in that case, where the lack of natural ventilation for cooling, prevents thermal comfort to be enhanced,
depicting the unbalanced conditions of each zone.
110

Figure 5.2 – Comparison of Base-case Scenarios
111

5.2.2 CORRELATION BETWEEN DAYLIGHT AND THERMAL COMFORT

These results expressed that there are various possible designs to obtain satisfactory outcomes based on
the objectives defined. The solutions varied in great degree making it difficult to define specific
conclusions in regards to the outcomes of the optimization analysis. In some cases, the comparison
between the outcomes obtained looked contradictory between each other and against intuitive notions
about what would constitute a good solution for daylighting and/or thermal performance. For example,
in the SW corner of the 7
th
floor, one of the solutions provided a better daylighting performance even
though it had a considerably lower WWR than other solutions (Opt.3 vs 4,5 in Figure 5.3). At the same
time very similar results were obtained by design solutions where the WWR percentages were
distributed in a different orientation. This is relevant because it elucidates the notion that south facing
façades might have an advantage for daylighting over other orientations because it is where the source of
light comes most often. That defines the gap between daylighting and thermal performance because in
this case thermal outcomes “do” are directly influenced by the orientation of the building.

Figure 5.3 - Design Solutions with Similar Outcomes
112

This could also be understood by examining the outputs of the simulations with fully glazed façades that
include shading devices, where the results in terms of daylighting turned to be very similar for all the
different zones despite their orientations. The main variation of results (8%) occurred for the 7
th
floor
SW compared against the 51
st
floor SW, which was due the overshadowing conditions of adjacent
buildings (Figure 5.4) in the 7
th
floor. In relation to the variations between the 51
st
floor SW, with the 7
th

and 51
st
floors NE (4%), it could be argued that one of the causes was the lack of shading devices to
cover the northern façade of these two zones. This can be inferred by inspecting the floor areas adjacent
to the northern façades, where despite not being subjected to direct solar radiation, exceeded the upper
threshold of 2000 luxes thus reducing the overall UDI for those zones in comparison with 51
st
floor SW
corner. The reason for the lack of shading devices in the northern façades is because they are activated
whenever there is direct solar incidence coming through the windows, and because northern façades
have no such incidences, the shading devices are never active on that orientation. In addition, another
reason for having variations between the 51
st
SW, and the NE zones, could be due light reflections from
adjacent buildings that may cause some small variations of UDI between the zones, in order to prove
that idea more testings would be needed.

Useful daylight, with an upper threshold of illuminance, penalizes those solutions where daylighting
levels go above the set point. For that reason, the visualizations depict black areas adjacent to the
location of the windows, particularly in the iterations where no shading devices were implemented. This
is contrary to the assumption that in northern façades glare issues should not occur due the lack of direct
sunlight; the simulations illustrated something that might point that assumption as debatable, which is
that daylighting levels went beyond the threshold of 2000 lux in areas that were in close proximity with
façades in northern orientations, suggesting the possibility of a glare issue and therefore the need for
some sort of shading in those areas. Additionally something that would have to be taken into account
113

and examined, would be to determine if reflections from adjacent buildings could be a cause of glare
issues as well, in which case northern orientations would be susceptible of having those problems
despite the lack of direct solar incidence. Further studies would be needed to assess that question in
detail.

Based on the simulations performed, a conclusion in regards quality of daylighting would be to say that
it has nothing to do with direct sunlight (at under the climate conditions of this study) as it will usually
provoke visual discomfort except during dawn and sunset when lighting levels are mild. Therefore, in
order to improve daylighting levels in indoor spaces, architects need to recur to ambient light for
meeting that objective.

114

Figure 5.4 - Base-cases: Shaded vs Non-shaded
115

Another aspect worth discussing, has to do with analyzing the benefits of shading devices in terms of
TA and EUI. Based on the results, shading devices manifested a more significant benefit in terms of
energy consumption than thermal comfort. This could be appreciated in all the different zones by
comparing the base-case scenarios with natural ventilation against those with natural ventilation and
shading devices (Figure 5.4), where the reductions on energy consumption reached 6 kBtu/sq/yr
(representing 33% of the overall consumption) in 51
st
floor SW corner, while the improvement in
thermal comfort only accounted for 6%. In order to understand that results though, it is important to
acknowledge that the simulations for EUI did not encompassed an hybrid system that would account for
the cooling effects of natural ventilation in the calculations, in other words, the energy calculations for
base-case scenarios with natural ventilation implemented a mechanical system for the cooling loads,
which explains why shading devices have a higher contribution in terms of energy consumption than
thermal comfort. Implementing hybrid systems to perform the calculations of energy consumption while
having natural ventilation is an aspect that could be assessed in future versions of the workflow.

Looking into the role of the impacts of shading devices over TA, shading devices did not turn out to
have drastic benefits because natural ventilation was accounted for both scenarios, shaded and not-
shaded, therefore the cooling benefits of that proved to underscore the benefits of shading. Another
evidence for that can be seen in other results where similar outcomes were obtained by design solutions
with and without shading devices, the variations between those iterations were of about 2-3%, and were
primarily in function of WWR, sill height, and number and distribution of windows along the façades
(Figure 5.5).

Natural ventilation can play a major role for improving thermal comfort in buildings compared to
shading devices. As seen through the results it unveils the possibility to think of schemes for cooling
116

based on natural ventilation and hybrid systems as effective means to improve thermal comfort while
reducing energy consumption and costs (both of energy and infrastructure) in buildings. The most
significant improvements in all simulations can be examined in Chart 4.1 in the results chapter. More
research is needed to support these notions and understand more clearly the advantages and the
disadvantages of implementing natural ventilation in commercial buildings since it also involves the risk
of affecting existing attributes in their operation, i.e., air quality, ventilation uniformity (no one would
like to see their work being blown by a unexpected gust of wind coming from the window), etc.

Figure 5.5 - Shaded Scenarios vs. Non-Shaded
117

5.2.3 OPTIMIZATION STUDIES

In order to have a comprehensive understanding of the results obtained, it is pertinent to look into how
the optimization analysis works to provide outcomes. The task of performing optimization analysis was
carried by the plugin Octopus. As mentioned in previous chapters, an advantage of implementing
Octopus, in contrast to other similar approaches, lies in its capacity to examine multiple objectives
simultaneously, providing the results that represent the best trade-offs between them.
The optimization studies consisted on running three generations (sets) with a population of 30 elements
each to determine the best correlations between sUDI and TA. In addition, EUI was also incorporated in
the visualization of results, but it was not used as part of the calculations to arrive to particular optimum
solutions. The multiple iterations performed by the optimization analysis are scattered through a three
dimensional graphic that portrays the results obtained by the analysis. After running several simulations,
the graphic gets populated with the best trade-offs between the different objectives, conforming a well-
defined arrangement of the boundaries in which all possible best trade-offs could occur. This
arrangement of results is known as the Pareto Front (Figure 5.6). Something to point out though, is that
the optimization studies performed were not exhaustive, meaning that not all design possibilities were
examined, and therefore not all best possible solutions were necessarily obtained.
The design variables (parameters) that were considered for the simulations were variables relating to the
windows: width, height, sill-height, number of units, and the activation of the shading devices. Octopus
iterates through different arrangements of the design variables to determine the best combinations. From
all the results the best six solutions were retrieved.
In the graphical representation of the optimization, one can find patterns within the multiplicity of
results, because of that, further speculations about the correlations between the different objectives can
be elaborated. For example, a direct correlation between daylighting and energy consumption was
118

found, yet a direct correlation between daylighting and thermal autonomy, or energy consumption and
thermal autonomy were not so clearly delineated.
The figure (in the “Right” view of Figure 5.6) describes how any increments in UDI are accompanied by
a linear reduction of EUI. This directly occurs because less energy is consumed for electric lighting with
any increment in daylighting. In the case of TA, there seems to be a similar correlation with UDI, yet
after a certain point where the UDI is above about 60%, the pattern blurs and several options with
similar UDI values but with different TA appear. This quasi-contradiction suggest that more complex
behaviors in terms of thermal assessment come into place once an hypothetical threshold of design
complexity is overcome. In other words, it might look that once the possible combinations of the design
variables overcome a limit in complexity (i.e. it might not make too much difference what kind of
arrangement does a WWR of 10% might have on a façade, compared to the possible arrangements of a
WWR of 50%), the possibilities to assess the objectives increase. Because TA is based on cooling
schemes that use natural ventilation, the thermal behavior of different solutions will be influenced by
how well they manage wind pressures and stack effects, which are related with the non-uniform
behaviors of wind. This helps explain why different solutions with same UDI values might have
different TA outcomes. The same might apply for the correlation between EUI and TA.
119

Figure 5.6 - Octopus Charts 51
st
Floor SW Corner

5.2.4 LIMITATIONS IN CURRENT STATE OF DEVELOPMENT

After putting the workflow into practice, the limitations in its scope and capabilities became clearer. The
current state of development of the workflow is in a “Beta” phase; some functionalities of the workflow
can be improved and some unexpected bugs might appear. For instance one of the limitations on thermal
autonomy component is the considerable amount of time it needs to calculate the results (from 25 to 50
seconds depending on hardware capabilities). The way to overcome that limitation consist of translating
the user object cluster into to code with a language such as Python, C#, or Visual Basic. In addition to
120

that, some of the capabilities of the workflow can be enhanced to achieve more comprehensive results.
For instance the daylighting analysis lacks the capability to assess glare issues, therefore no clear
correlations in that sense were able to be elaborated between the UDI results and glare. Also, the
impossibility to define operable shading devices for the daylighting analysis prevented a more
comprehensive assessment of those analysis. Further improvements will be specified in the Chapter 7 of
this document.

5.3 CONCLUSION

As previously depicted, there were different findings in regards to the different areas involved in the
workflow. The areas of the workflow that were discussed in relation to the results obtained, and the
summary of findings for each of them are the following:

1) Assessment of Complex urban scenarios and orientation
The most relevant findings for this area, were that overshadowing of adjacent buildings modified the
outcomes by around 6% in terms of daylighting, 20% in terms of thermal comfort, and 3 kBtu/sf/yr in
terms of energy consumption. Also, the fact that better overall results were reported from zones oriented
towards North-East compared to those oriented to the South-West.

2) Correlation of daylight and thermal comfort
For this area, the findings were that daylighting can be more easily correlated with energy consumption
due uniformity and predictability on the usage of HVAC systems. In addition, correlating daylighting
with thermal autonomy was more complex due natural ventilation, and complexity in combinations
between design variables. In the case of daylighting, a conclusion was that ambient light is the best way
to ensure a good quality of illumination for the climatic conditions on which the study took place.
121

3) Optimization studies
A primary finding in relation to the performing multi-objective optimization studies, was that drastically
different solutions in terms of design can provide very similar overall results. Another important finding
from this area was that the most dramatic improvements were caused by natural ventilation, increasing
the thermal comfort within a range of 13-48% in all zones.

122

CHAPTER 6: CONCLUSIONS

The first stage of development of the workflow and the implementation of a set of novel capabilities to
model façade solutions was satisfactorily completed. The intended goal was to support decision-making
for early stages of design and retrofit scenarios towards environmentally responsive buildings as
corroborated and explained in chapters 4 and 5. The set of novel capabilities that were implemented
included the assessment of complex urban settings; the implementation of cooling schemes based on
natural ventilation, making use of the emerging metric thermal autonomy; the creation of a component
for modeling shading devices based on climate data; the creation of a component for visualizing
daylighting results; and the implementation of a multi-objective optimization analysis to correlate
daylighting and thermal performance to determine the best trade-offs.

The conclusions are divided in two different categories: completion of the workflow and the final
conclusions obtained from the results.

6.1 WORKFLOW FUNCTIONALITY

In order to test the capabilities of the workflow, a hypothetical multi-story commercial building located
in downtown Los Angeles was used as a case-study. Four different zones in the building were modeled
to investigate the variations in daylighting and thermal performance encountered in different orientations
of the building and on different floors despite having the same geometrical attributes. The zones were
located in the south-west corner of the 7
th
floor, the north-east corner of the 7
th
floor, the south-west
corner of the 51
st
floor, and in the north-east corner of the 51
st
floor.

123

An important contribution was not only the elaboration of the workflow, which consisted on connecting
a set of existing components and third party utilities to satisfy some of the needs, but the creation of a set
of custom components, known as “User Objects,” to satisfy specific needs that were not solved with the
available library of functions in Rhino3d and Grasshopper. In this case, they were created to model the
geometry of the building, windows, and shading devices, and to visualize the results of the daylighting
simulations, thermal autonomy, and the conversion of energy consumption into energy use intensity.
Automating repetitive tasks with the user objects was a way to simplify the repetitive procedures that
have to be followed in any project, and they represented the foundation over which the simulations and
optimization studies were performed.

6.2 FINDINGS FROM THE SIMULATIONS RESULTS

By implementing a case-study to test the workflow, different sets of results pertaining to each analyzed
zone were gathered. These sets of results allowed the observation of improvements over the base-case
and trends in regards to daylighting, thermal comfort, natural ventilation, energy consumption,
fenestration patterns, and shading strategies. The conclusions obtained from the observation of those
results are discussed in chapter 5; the main highlights are presented here:

 The optimization results portrayed the capacity to arrive to similar outcomes through various
contrasting design solutions. Shading devices, window-wall ratio, arrangement of windows
through the façade are all indeed aspects that affect the final outcome, yet based on the results,
none of them posed a constraint that cannot be avoided to maximize the outcomes of the
objectives defined.
 The results in terms of thermal comfort were the most significant, having a range of
improvement of 10 to 57% between the best scenarios against the base-cases in each zone. The
124

improvements were considerable because base-cases were simulated as sealed buildings, where
the rest of the cases studied included a scheme of cooling based on natural ventilation. That
demonstrated the potential of cooling buildings passively. With this workflow, the user would
have a tool to asses that.
 Useful daylight illuminance was increased (and reduced in some cases) in a range between -12 %
to 27% against base-cases. In some instances the percentages of daylight went lower than the
base-cases because they were fully glazed, making the task of improving the daylight
performance hard, like in north-east orientations for instance, where daylight performance was
commonly satisfactory due the lack of direct sunlight to overcome the upper threshold of 2000
lux of UDI.
 In regards to energy use intensity, the improvements accounted for a range of energy reduction
between 7 and 14 kBtu/sf/yr. The adjustments implemented to achieve these results encompassed
improved fenestration designs, implementation of shading devices, incorporation of natural
ventilation, and improved controls in the systems.
 There was a direct correlation between daylighting and energy consumption. There was not a
direct correlation between thermal autonomy and energy consumption, and thermal autonomy
and daylighting. This is partially due to the non-uniform behavior of natural ventilation, which
turn the assignment of defining predictable fenestration patterns difficult (for further explanation
see Chapter 5.2.2).
 Implementing systems based on natural ventilation leverage interdependence with the objective
of daylighting since their correlation scales in parallel through the increase/decrease of the
window area; higher window-wall ratios mean more potential to cool through natural ventilation
and improve daylighting, and vice-versa. This goes in contrast with the existing paradigm of
125

cooling based on mechanical systems, which poses an opposite correlation between daylighting
and energy consumption (the more window are, the more heat there is to remove and therefore
more energy). Figure 6.1 present a graphic expression of that, the size of the elements and their
location in the graphic represent their magnitude, small sizes signify low magnitudes, and bigger
sizes mean higher magnitudes. Based on that, we have for instance that for a design paradigm
based on mechanical systems, when reducing the window-wall ratio, the artificial lighting
increases while daylighting and energy for cooling decrease.

Figure 6.1 – Daylighting, Energy, Natural Ventilation Correlation

126

6.3 FUTURE WORK

In order to enhance the capabilities and scope of the current state of development of the workflow,
improvements can be incorporated. There are three main areas in which this can occur: outreach
enhancements, functionality enhancements, and research objectives. Outreach enhancements make the
workflow accessible and useful for people interested in it. This encompasses both having information to
help the user understand and make the best use of the application, while also satisfying the need to
incorporate the models developed into further stages of design by connecting the workflow with BIM
applications and more advanced energy modeling software. Functionality enhancements are
improvements that can be performed to enhance the existing functionalities of the workflow. In this
category there are many considerations that could broaden its scope of application to satisfy objectives
that go beyond those that were defined at this stage. One example would be the incorporation of
modeling inner patios within the capabilities of the tool. Research objectives point some areas that could
be studied to improve the scope of use of the workflow, in themselves they demand for the specific
assessment of particular areas that need to be examined in certain depth and which are do not affect the
current usability of the workflow.

Outreach enhancements

 Translation of User Objects into Python or C# code to improve speed and to share them
 Connectivity with BIM applications, exporting models in IFC format
 Creation of a manual
 Creation of video tutorials
 gbXML export for further development in other more sophisticated energy modeling tools
 Calibrate results with other tools such as Open Studio, Vasari, Design Builder, IES, etc.
127

 Creation of more user friendly components to perform the simulations

Functionality enhancements

 Ability to create and simulate more complex buildings
 Implement daylight analysis including operable shading devices and radiation maps
 Incorporate capabilities to model and analyze light shelves, skylights, and inner patios
 Implement vents in the palette of strategies for natural ventilation
 Implement hybrid systems that account for natural ventilation in combination of an additional
cooling system to obtain a more precise energy assessment
 Implementation of a capability to assess glare issues
 Separating window for daylighting with windows for ventilation
 Incorporate different types of glazing and different materials for walls, roofs, floors
 Incorporate the capability analyze multiple zones in the simulations

Research Objectives

 Examine optimization outcomes in other climates
 Examine in what ways altitude would affect wind speed and therefore natural ventilation
compared with lower altitudes

6.4 Conclusion

The research objective behind this project was to develop a workflow to support decision-making during
early stages of design and retrofit scenarios towards environmentally responsive goals through the
implementation of a set of novel capabilities. These included the assessment of complex urban settings;
the implementation of cooling schemes based on natural ventilation; making use of the emerging metrics
128

of thermal autonomy and useful daylight illuminance; automating the modeling of shading devices
based on climate data; and performing multi-objective optimization analysis to correlate and determine
best trade-offs between daylighting and thermal performance.
The intended goals of this workflow were to analyze the daylight and thermal response of buildings in
complex urban settings by implementing certain customized components and making use of already
existing state of the art functionalities within the Grasshopper environment. This final workflow as
demonstrated in the case study achieved many of its objectives in that it was able to provide:
 A strong connection between daylight and thermal studies.
 Multi-objective optimization of thermal versus daylight parameters.
 The ability to iterate through several design options in short periods of time (+-100/workday).
 A method to calculate the thermal behavior of buildings using thermal autonomy as a metric.
 The capacity to automate the process of generating shading devices based on user defined constraints
of peak days and hours.

An important contribution in this sense was not only the elaboration of the workflow, which consisted
on connecting a set of existing plugins that satisfied a particular need, but the creation of a set of custom
components, also known as “User Objects”, to generate the geometry of the building, the windows, the
shading devices, and to visualize the results of the daylighting simulations, thermal autonomy, and the
translation of energy consumption into EUI. These customized functionalities allow the adequate
elaboration and representation of design solutions and results.

129

BIBLIOGRAPHY
A. Nabil and J. Mardaljevic, “Useful Daylight Illuminance: A New Paradigm for Assessing Daylight in
Buildings,” Lighting Research and Technology 37, no. 1 (March 1, 2005): 41–57,
doi:10.1191/1365782805li128oa.
“Archsim,” accessed July 1, 2014, http://archsim.com/.
“Amherst College Science Center - Behnisch Architekten,” accessed September 11, 2013,
http://behnisch.com/projects/761.
Anjali Joseph, Ph.D., “The Impact of Light on Outcomes in Healthcare Settings,” August 2006.
“ANSI/ASHRAE Standard 55-2013, Thermal Environmental Conditions for Human Occupancy,” n.d.
Anthea Court, Alan Pearson, and Derek Frewin, “The Effects of Exposure to Natural Light in the
Workplace on the Health and Productivity of Office Workers: A Systematic Review,” The JBI
Database of Systematic Reviews and Implementation Reports 8, no. 16 Suppl (July 29, 2010):
S72–90.
Antony Wood and Ruba Salib, Guide To Natural Ventilation in High Rise Office Buildings (New York:
Routledge, 2012).
Atelier Ten and Jessica Zofchak, “Daylight Case Studies-Radiance Workshop,” 2013,
http://www.radiance-online.org/community/workshops/2013-golden-
co/ZofchakA10_Radiance%202013_Daylighting%20Case%20Studies.pdf.
Azadeh Omidfar, “Design Optimization of a Contemporary High Performance Shading Screen-
Integration of ‘Form’ and Simulation Tools,” in Proceedings of Building Simulation 2011
(presented at the 12th Conference of International Building Performance Simulation Association,
Sydney, Australia, 2011).
Brendon Levit, Susan Ubbelohde, George Loisos, Nathan Brown, “Thermal Autonomy as Metric and
Design Process,” in Pushing the Boundary-Net Positive Buildings (presented at the CaGBC
National Conference and Expo 2013, Vancouver, 2013).
130

“Building Envelope Design Guide - Wall Systems | Whole Building Design Guide,” accessed February
21, 2014, http://www.wbdg.org/design/env_wall.php.
California Energy Commission, “Revised Zero Net Energy (ZNE) Definition,” July 20, 2011.
California Public Utilities Comission, “Achieving Maximum Energy Savings in California for 2009 and
Beyond,” September 2008, www.californiaenergyefficiency.com.
Christoph F. Reinhart and Oliver Walkenhorst, “Validation of Dynamic RADIANCE-Based Daylight
Simulations for a Test Office with External Blinds,” Energy and Buildings 33, no. 7 (September
2001): 683–97, doi:10.1016/S0378-7788(01)00058-5.
“Dhour: A Bioclimatic Information Design Prototyping Toolkit | Grasshopper,” accessed June 6, 2014,
http://www.food4rhino.com/project/dhour.
Drury Crawley, Pless Shanti, and Paul Torcellini, “Getting to Net Zero,” September 2009,
http://sullivanarchitect.com/wp-content/uploads/2013/08/NREL-Getting-to-Net-Zero.pdf.
“Elk | Grasshopper,” accessed June 5, 2014, http://www.food4rhino.com/project/elk.
EPA (United States Environmental Protection Agency), “Portfolio Manager Technical Reference: U.S.
Energy Use Intensity by Property Type,” July 2013.
“Fanger, P Ole (1970). Thermal Comfort: Analysis and Applications in Environmental Engineering.
McGraw-Hill.,” n.d.
Francisco Valdes and Yuming Sun, “Parametric Natural Ventilation Simulation with Real-Time
Geometric Feedback (Nat-Vent),” 2012.
Gagne, J.M.L and Marilyne Andersen, “A Daylighting Knowledge-Base for Performance-Driven Facade
Design Exploration,” LEUKOS - The Journal of the Illuminating Engineering Society of North
America, 2011.
“Grasshopper - Algorithmic Modeling for Rhino,” accessed July 1, 2014,
http://www.grasshopper3d.com/.
131

“Heliotrope - Solar | Grasshopper,” accessed June 5, 2014,
http://www.food4rhino.com/project/heliotropegh.
J. Alstan Jakubiec and Christoph F Reinhart, “DIVA 2.0: Integrating Daylight and Thermal Simulations
Using Rhinoceros 3D, Daysim, and Energy Plus” (presented at the 12th Conference of
International Building Performance Simulation Association, Sydney, Australia, 2011), 8.
Kalyanmoy Deb, “Multi-Objective Optimization,” in Search Methodologies, ed. Edmund K. Burke and
Graham Kendall (Springer US, 2014), 403–49, http://link.springer.com/chapter/10.1007/978-1-
4614-6940-7_15.
Katie Fehrenbacher, “Google X Spin-out Flux Is Harnessing Data to Make Designing Buildings Better,”
May 6, 2014, http://gigaom.com/2014/05/06/google-x-spin-out-flux-is-harnessing-data-to-make-
designing-buildings-better/.
Kera Lagios, Jeffery Niemasz, and Christoph F Reinhart, “Animated Building Performance Simulation
(ABPS)_Linking Rhinoceros/Grasshopper with Radiance/Daysim,” in Fourth National
Conference of IBPSA-USA (presented at the SimBuild 2010, New York City, New York, 2010).
“Ladybug + Honeybee | Grasshopper,” accessed June 5, 2014,
http://www.food4rhino.com/project/ladybug-honeybee.
Luisa Caldas, “Generation of Energy-Efficient Architecture Solutions Applying GENE_ARCH: An
Evolution-Based Generative Design System,” Advanced Engineering Informatics 22, no. 1 (enero
2008): 59–70, doi:10.1016/j.aei.2007.08.012.
M. Stavric, “Parametric Modeling for Advanced Architecture” 5, no. 1 (n.d.).
Mahmoud Gadelhak, “Integrating Computational and Building Performance Simulation Techniques for
Optimized Facade Designs,” in Computation and Performance, vol. 2 (presented at the eCAADe,
Delft, The Netherlands, 2013), 261–70.
Mohamed Mansour El Sheikh, “INTELLIGENT BUILDING SKINS: Parametric Based Alogrithm for
Kinetic Façades Design and Daylighting Performance Integration” (University of Southern
California, 2011).
132

“Nicol, Fergus; Humphreys, Michael (2002). ‘Adaptive Thermal Comfort and Sustainable Thermal
Standards for Buildings’. Energy and Buildings 34 (6): 563–572. doi:10.1016/S0378-
7788(02)00006-3.,” n.d.
Pablo C. Herrera, “Rhinoscripting Resources: Rhino Python 101 Primer by Skylar Tibbits, Arthur van
Der Harlen and Steve Baer. (2011),” Rhinoscripting Resources, June 29, 2011,
http://rhinoscriptingresources.blogspot.com/2011/06/rhino-python-101-primer-by-skylar.html.
Pablo M. La Roche, Carbon-Neutral Architectural Design, 1 edition (Boca Raton, FL: CRC Press,
2011).
Patricia Plympton, Susan Conway, and Kyra Epstein, “Daylighting in Schools: Improving Student
Performance and Health at a Price Schools Can Afford,” August 2000.
“PanelingTools for Grasshopper with Rhino 5.0 | Grasshopper,” accessed July 1, 2014,
http://www.food4rhino.com/project/pt-gh.
“Rhinoceros,” accessed July 1, 2014, http://www.rhino3d.com/.
Richard de Dear and G. S. Brager, “Developing an Adaptive Model of Thermal Comfort and
Preference,” Center for the Built Environment, January 1, 1998,
http://escholarship.org/uc/item/4qq2p9c6.
Rober Vierlinger, “Octopus - Grasshopper,” Grasshopper-Algorithmic Modeling for Rhino, accessed
March 13, 2014, http://www.grasshopper3d.com/group/octopus.
“Selectable Preview | Grasshopper,” accessed July 1, 2014,
http://www.food4rhino.com/project/selectablepreview.
Simos Yannas, “Towards Environmentally-Responsive Architecture,” in The 20th Conference on
Passive and Low Energy Architecture (presented at the PLEA 2003, Santiago, Chile, 2003), 6.
“Solemma,” accessed July 1, 2014, http://www.solemma.net/index.html.
133

Svend Svendsen Christian Anker Hviid, “A Method for Evaluating the Problem Complex of Choosing
the Ventilation System for a New Building,” n.d.
Tyler Tucker, “PERFORMATIVE SHADING DESIGN: Parametric Based Measurement of Shading
System Configuration Effectiveness and Trends” (University of Southern California, 2013).
Walter T. Grondzik, Alison G. Kwok, Benjamin Stein, John S. Reynolds, Mechanical and Electrical
Equipment for Buildings, 11th ed. (Wiley, 2009).
Yun Kyu Yi and Ali M. Malkawi, “Optimizing Building Form for Energy Performance Based on
Hierarchical Geometry Relation,” Automation in Construction 18, no. 6 (October 2009): 825–33,
doi:10.1016/j.autcon.2009.03.006.
A-1

A3. THIRD CHAPTER APPENDIX
A3.3 WORKFLOW GRASSHOPPER DEFINITION

Figure A3.1 - Workflow Diagram
A-2

A3.3 WORKFLOW GRASSHOPPER DEFINITION

Figure A3.2 - Grasshopper Definition
A-3

A3.3 PEAK ANNUAL HOT DAY (PAHD), AND LOWEST ANNUAL COLD DAY
(LACD)

Figure A3.3 - Peak Annual Hot Day (PAHD), And Lowest Annual Cold Day (LACD)

For this analysis the plug-in Dhour was used. The analysis consist of importing an energy
plus (epw) weather file directly into grasshopper (Figure A3.3-A). Once imported, the data
contained in the file can be retrieved by a dhour component (B), the data includes all the
different measures collected by the weather station, in this case the data that is relevant is
the daily dry bulb temperature which can be defined by right clicking the component and
checking that option from the list of measures showed. From there, the next step is to filter
A-4

the data to determine the days in which the temperature exceeds a threshold of temperature
beyond which it would be considered hot (C), as well as the days when goes under a lower
threshold to determine what are the days when the temperature is cold (D). The thresholds
of temperature can be defined by the user (E). After filtering the days where the
temperature goes out of range of comfort, it is necessary to visualize the data to have a clear
understanding of the thermal behavior during the year. That is done within grasshopper
with the dhour component “Quick Heatmap” (F), which displays a chart with the hourly
temperatures during the year. To complement the process of visualizing the data of the days
in which temperature exceeds or is lower to the thresholds, a component to actually look
into those periods can be inserted, the functionality of this component consist of listing the
hours outside of the comfort zone (G), this is helpful to corroborate the trends of thermal
behavior during the year and will facilitate more informed decisions for defining shading
yearly periods. The component that is used to do that is called the “Hour Binning”. The
final part of performing this analysis consist of retrieving the Peak Annual Hot Day
(PAHD) (H), and the Lowest Annual Cold Day (LACD) (I), from the sets that were
previously obtained. This is done with the “Extreme Periods” component of dhour.

A-5

A4. FOURTH CHAPTER APPENDIX

A4.1 INDIVIDUAL RESULTS (Start in next page)

A-6

7
th
Floor SW Fragment
“Base-Case”
Window-Wall Ratio: 93%

Figure A4.1.1 - Building Zone

Figure A4.1.2 - Useful Daylight Illuminance

Figure A4.1.3 - Thermal Autonomy

Figure A4.1.4 - Energy Use Intensity
0
5
10
15
20
25
EUI (kBtu/sf/yr)
Heating Cooling Lights Total
A-7

7
th
Floor SW Fragment
“Base-Case with
Natural Ventilation”
Window-Wall Ratio: 93%

Figure A4.2.1 - Building Zone

Figure A4.2.2 - Useful Daylight Illuminance

Figure A4.2.3 - Thermal Autonomy

Figure A4.2.4 - Energy Use Intensity
0
5
10
15
20
25
EUI (kBtu/sf/yr)
Heating Cooling Lights Total
A-8

7
th
Floor SW Fragment
“Base-Case with
Natural Ventilation and
Shading Devices”
Window-Wall Ratio: 93%

Figure A4.3.1 - Building Zone

F
i
Figure A4.3.2 - Useful Daylight Illuminance

Figure A4.3.3 - Thermal Autonomy

Figure A4.3.4 - Energy Use Intensity
0
5
10
15
20
25
EUI (kBtu/sf/yr)
Heating Cooling Lights Total
A-9

7
th
Floor SW Fragment
“Optimization 1”
Window-Wall Ratio: S-83%, W-39%

Figure A4.4.1 - Building Zone

F
i

F
i
Figure A4.4.2 - Useful Daylight Illuminance

Figure A4.4.3 - Thermal Autonomy

Figure A4.4.4 - Energy Use Intensity
0
5
10
15
20
25
EUI (kBtu/sf/yr)
Heating Cooling Lights Total
A-10

7
th
Floor SW Fragment
“Optimization 2”
Window-Wall Ratio: S-19%, W-88%

Figure A4.5.1 - Building Zone

F
i

F
i
Figure A4.5.2 - Useful Daylight Illuminance

Figure A4.5.3 - Thermal Autonomy

Figure A4.5.4 - Energy Use Intensity
0
5
10
15
20
25
EUI kBtu/sf/yr)
Heating Cooling Lights Total
A-11

7
th
Floor SW Fragment
“Optimization 3”
Window-Wall Ratio: S-14%, W-79%

Figure A4.6.1 - Building Zone

F
i

F
i
Figure A4.6.2 - Useful Daylight Illuminance

Figure A4.6.3 - Thermal Autonomy

Figure A4.6.4 - Energy Use Intensity
0
5
10
15
20
25
EUI kBtu/sf/yr
Heating Cooling Lights Total
A-12

7
th
Floor SW Fragment
“Optimization 4”
Window-Wall Ratio: S-85%, W-35%

Figure A4.7.1 - Building Zone

F
i

F
i
Figure A4.7.2 - Useful Daylight Illuminance

Figure A4.7.3 - Thermal Autonomy

Figure A4.7.4 - Energy Use Intensity
0
5
10
15
20
25
EUI kBtu/sf/yr
Heating Cooling Lights Total
A-13

7
th
Floor SW Fragment
“Optimization 5”
Window-Wall Ratio: S-29%, W-84%

Figure A4.8.1 - Building Zone

F
i

F
i
Figure A4.8.2 - Useful Daylight Illuminance

Figure A4.8.3 - Thermal Autonomy

Figure A4.8.4 - Energy Use Intensity
0
5
10
15
20
25
EUI (kBtu/sf/yr)
Heating Cooling Lights Total
A-14

7
th
Floor SW Fragment
“Optimization 6”
Window-Wall Ratio: S-19%, W-67%

Figure A4.9.1 - Building Zone

F
i

F
i
Figure A4.9.2 - Useful Daylight Illuminance

Figure A4.9.3 - Thermal Autonomy

Figure A4.9.4 - Energy Use Intensity
0
5
10
15
20
25
EUI (kBtu/sf/yr)
Heating Cooling Lights Total
A-15

51
st
Floor SW Fragment
“Base-Case”
Window-Wall Ratio: 93%

Figure A4.10.1 - Building Zone

F
i

F
i
Figure A4.10.2 - Useful Daylight Illuminance

Figure A4.10.3 - Thermal Autonomy

Figure A4.10.4 - Energy Use Intensity
0
5
10
15
20
25
EUI (kBtu/sf/yr)
Heating Cooling Lights Total
A-16

51
st
Floor SW Fragment
“Base-Case with
Natural Ventilation”
Window-Wall Ratio: 93%

Figure A4.11.1 - Building Zone

F
i

i
g
u
r
Figure A4.11.2 - Useful Daylight Illuminance

Figure A4.11.3 - Thermal Autonomy

Figure A4.11.4 - Energy Use Intensity
0
5
10
15
20
25
EUI (kBtu/sf/yr)
Heating Cooling Lights Total
A-17

51
st
Floor SW Fragment
“Base-Case with
Natural Ventilation and
Shading Devices”
Window-Wall Ratio: 93%

Figure A4.12.1 - Building Zone

F
i
Figure A4.12.2 - Useful Daylight Illuminance

Figure A4.12.3 - Thermal Autonomy

Figure A4.12.4 - Energy Use Intensity
0
5
10
15
20
25
EUI (kBtu/sf/yr)
Heating Cooling Lights Total
A-18

51
st
Floor SW Fragment
“Optimization 1”
Window-Wall Ratio: S-30%, W-41%

Figure A4.13.1 - Building Zone

F
i

F
i
Figure A4.13.2 - Useful Daylight Illuminance

Figure A4.13.3 - Thermal Autonomy

Figure A4.13.4 - Energy Use Intensity
0
5
10
15
20
25
EUI (kBtu/sf/yr)
Heating Cooling Lights Total
A-19

51
st
Floor SW Fragment
“Optimization 2”
Window-Wall Ratio: S-21%, W-44%

Figure A4.14.1 - Building Zone

F
i

F
i
Figure A4.14.2 - Useful Daylight Illuminance

Figure A4.14.3 - Thermal Autonomy

Figure A4.14.4 - Energy Use Intensity
0
5
10
15
20
25
EUI (kBtu/sf/yr)
Heating Cooling Lights Total
A-20

51
st
Floor SW Fragment
“Optimizations 3”
Window-Wall Ratio: S-45%, W-5%

Figure A4.15.1 - Building Zone

F
i

F
i
Figure A4.15.2 - Useful Daylight Illuminance

Figure A4.15.3 - Thermal Autonomy

Figure A4.15.4 - Energy Use Intensity
0
5
10
15
20
25
EUI (kBtu/sf/yr)
Heating Cooling Lights Total
A-21

51
st
Floor SW Fragment
“Optimization 4”
Window-Wall Ratio: S-16%, W-49%

Figure A4.16.1 - Building Zone

F
i

F
i
Figure A4.16.2 - Useful Daylight Illuminance

Figure A4.16.3 - Thermal Autonomy

Figure A4.16.4 - Energy Use Intensity
0
5
10
15
20
25
EUI (kBtu/sf/yr)
Heating Cooling Lights Total
A-22

51
st
Floor SW Fragment
“Optimization 5”
Window-Wall Ratio: S-34%, W-53%

Figure A4.17.1 - Building Zone

F
i

F
i
Figure A4.17.2 - Useful Daylight Illuminance

Figure A4.17.3 - Thermal Autonomy

Figure A4.17.4 - Energy Use Intensity
0
5
10
15
20
25
EUI (kBtu/sf/yr)
Heating Cooling Lights Total
A-23

51
st
Floor SW Fragment
“Optimization 6”
Window-Wall Ratio: S-49%, W-33%

Figure A4.18.1 - Building Zone

F
i

F
i
Figure A4.18.2 - Useful Daylight Illuminance

Figure A4.18.3 - Thermal Autonomy

Figure A4.18.4 - Energy Use Intensity
0
5
10
15
20
25
EUI (kBtu/sf/yr)
Heating Cooling Lights Total
A-24

7
th
Floor NE Fragment
“Base-Case”
Window-Wall Ratio: 93%

Figure A4.19.1 - Building Zone

F
i

F
i
Figure A4.19.2 - Useful Daylight Illuminance

Figure A4.19.3 - Thermal Autonomy

Figure A4.19.4 - Energy Use Intensity
0
5
10
15
20
25
EUI (kBtu/sf/yr)
Heating Cooling Lights Total
A-25

7
th
Floor NE Fragment
“Base-Case with
Natural Ventilation”
Window-Wall Ratio: 93%
Figure A4.20.1 - Building Zone

F
i

F

F
i
g
u
r
e

F
Figure A4.20.2 - Useful Daylight Illuminance

Figure A4.20.3 - Thermal Autonomy

Figure A4.20.4 - Energy Use Intensity
0
5
10
15
20
25
EUI (kBtu/sf/yr)
Heating Cooling Lights Total
A-26

7
th
Floor NE Fragment
“Base-Case with
Natural Ventilation and
Shading Devices”
Window-Wall Ratio: 93%

Figure A4.21.1 - Building Zone

F

Figure A4.21.2 - Useful Daylight Illuminance

Figure A4.21.3 - Thermal Autonomy

Figure A4.21.4 - Energy Use Intensity
0
5
10
15
20
25
EUI (kBtu/sf/yr)
Heating Cooling Lights Total
A-27

7
th
Floor NE Fragment
“Optimization 1”
Window-Wall Ratio: N-32%, E-89%

Figure A4.22.1 - Building Zone

F
i

F
i
Figure A4.22.2 - Useful Daylight Illuminance

Figure A4.22.3 - Thermal Autonomy

Figure A4.22.4 - Energy Use Intensity
0
5
10
15
20
25
EUI (kBtu/sf/yr)
Heating Cooling Lights Total
A-28

7
th
Floor NE Fragment
“Optimization 2”
Window-Wall Ratio: N-14%, E-50%

Figure A4.23.1 - Building Zone

F
i

F
i
Figure A4.23.2 - Useful Daylight Illuminance

Figure A4.23.3 - Thermal Autonomy

Figure A4.23.4 - Energy Use Intensity
0
5
10
15
20
25
EUI (kBtu/sf/yr)
Heating Cooling Lights Total
A-29

7
th
Floor NE Fragment
“Optimization 3”
Window-Wall Ratio: N-85%, E-22%

Figure A4.24.1 - Building Zone

F
i

F
i
Figure A4.24.2 - Useful Daylight Illuminance

Figure A4.24.3 - Thermal Autonomy

Figure A4.24.4 - Energy Use Intensity
0
5
10
15
20
25
EUI (kBtu/sf/yr)
Heating Cooling Lights Total
A-30

7
th
Floor NE Fragment
“Optimization 4”
Window-Wall Ratio: N-58%, E-50%

Figure A4.25.1 - Building Zone

F
i

F
i
Figure A4.25.2 - Useful Daylight Illuminance

Figure A4.25.3 - Thermal Autonomy

Figure A4.25.4 - Energy Use Intensity
0
5
10
15
20
25
EUI (kBtu/sf/yr)
Heating Cooling Lights Total
A-31

7
th
Floor NE Fragment
“Optimization 5”
Window-Wall Ratio: N-15%, E-54%

Figure A4.26.1 - Building Zone

F
i

F
i
Figure A4.26.2 - Useful Daylight Illuminance

Figure A4.26.3 - Thermal Autonomy

Figure A4.26.4 - Energy Use Intensity
0
5
10
15
20
25
EUI kBtu/sf/yr)
Heating Cooling Lights Total
A-32

7
th
Floor NE Fragment
“Optimization 6”
Window-Wall Ratio: N-63%, E-13%

Figure A4.27.1 - Building Zone

F
i

F
i
Figure A4.27.2 - Useful Daylight Illuminance

Figure A4.27.3 - Thermal Autonomy

Figure A4.27.4 - Energy Use Intensity
0
5
10
15
20
25
EUI (kBtu/sf/ye)
Heating Cooling Lights Total
A-33

51
st
Floor NE Fragment
“Base-Case”
Window-Wall Ratio: 93%

Figure A4.28.1 - Building Zone

F
i

F
i
Figure A4.28.2 - Useful Daylight Illuminance

Figure A4.28.3 - Thermal Autonomy

Figure A4.28.4 - Energy Use Intensity
0
5
10
15
20
25
EUI (kBtu/sf/yr)
Heating Cooling Lights Total
A-34

51
st
Floor NE Fragment
“Base-Case with
Natural Ventilation”
Window-Wall Ratio: 93%

Figure A4.29.1 - Building Zone

Figure A4.29.2 - Useful Daylight Illuminance

Figure A4.29.3 - Thermal Autonomy

Figure A4.29.4 - Energy Use Intensity
0
5
10
15
20
25
EUI (kBtu/sf/yr)
Heating Cooling Lights Total
A-35

51
st
Floor NE Fragment
“Base-Case with
Natural Ventilation and
Shading Devices”
Window-Wall Ratio: 93%

Figure A4.30.1 - Building Zone

Figure A4.30.2 - Useful Daylight Illuminance

Figure A4.30.3 - Thermal Autonomy

Figure A4.30.4 - Energy Use Intensity
0
5
10
15
20
25
EUI (kBtu/sf/yr)
Heating Cooling Lights Total
A-36

51
st
Floor NE Fragment
“Optimization 1”
Window-Wall Ratio: N-30%, E-13%

Figure A4.31.1 - Building Zone

Figure A4.31.2 - Useful Daylight Illuminance

Figure A4.31.3 - Thermal Autonomy

Figure 4.31.4 - Energy Use Intensity
0
5
10
15
20
25
EUI (kBtu/sf/yr)
Heating Cooling Lights Total
A-37

51
st
Floor NE Fragment
“Optimization 2”
Window-Wall Ratio: N-10%, E-67%

Figure 4.32.1 - Building Zone

Figure 4.32.2 - Useful Daylight Illuminance

Figure 4.32.3 - Thermal Autonomy

Figure 4.32.4 - Energy Use Intensity
0
5
10
15
20
25
EUI /kBtu/sf/yr)
Heating Cooling Lights Total
A-38

51
st
Floor NE Fragment
“Optimization 3”
Window-Wall Ratio: N-32%, E-50%

Figure A4.33.1 - Building Zone

Figure A4.33.2 - Useful Daylight Illuminance

Figure A4.33.3 - Thermal Autonomy

Figure A4.33.4 - Energy Use Intensity
0
5
10
15
20
25
EUI (kBtu/sf/yr)
Heating Cooling Lights Total
A-39

51
st
Floor NE Fragment
“Optimization 4”
Window-Wall Ratio: N-20%, E-22%

Figure A4.34.1 - Building Zone

Figure A4.34.2 - Useful Daylight Illuminance

Figure A4.34.3 - Thermal Autonomy

Figure A4.34.4 - Energy Use Intensity
0
5
10
15
20
25
EUI (kBtu/sf/yr)
Heating Cooling Lights Total
A-40

51
st
Floor NE Fragment
“Optimization 5”
Window-Wall Ratio: N-60%, E-41%

Figure A4.35.1 - Building Zone

F
i
g
u
r
Figure A4.35.2 - Useful Daylight Illuminance

Figure A4.35.3 - Thermal Autonomy

Figure A4.35.4 - Energy Use Intensity
0
5
10
15
20
25
EUI (kBtu/sf/yr)
Heating Cooling Lights Total
A-41

51
st
Floor NE Fragment
“Optimization 6”
Window-Wall Ratio: N-49%, E-33%

Figure A4.36.1 - Building Zone

Figure A4.36.2 - Useful Daylight Illuminance

Figure A4.36.3 - Thermal Autonomy

Figure A4.36.4 - Energy Use Intensity
0
5
10
15
20
25
EUI (kBtu/sf/yr)
Heating Cooling Lights Total
A-42

A4.2 GROUP 2: MATRIX OF RESULTS FOR EACH ZONE

Figure A4.37 - 7
th
Floor, South-West Corner Comparison
A-43

Figure A4.38 - 51
st
Floor, South-West Corner Comparison
A-44

Figure A4.39 - 7
th
Floor, North-East Corner Comparison

A-45

Figure A4.40 - 51
st
Floor, North-East Corner Comparison
A-46

A4.3 GROUP 3: MATRIX OF RESULTS OF ALL ZONES TOGETHER

Figure A4.41 - Matrix of Results

A-47

A4.4 GROUP 4: IMPROVEMENT CHARTS

BEST-CASES
EUI
(kBtu/sf/yr)
EUI
Improvement
Over Base-Case
(kBtu/sf/yr)
sUDI
sUDI %
Improvement
Over Base-Case
TA
TA %
Improvement
Over Base-Case
7th Floor SW Opt1 13 -11 71% 4% 70% 48%
51st Floor SW Opt5 11 14 76% 16% 86% 45%
7th Floor NE Opt3 10 12 79% 5% 78% 29%
51st Floor NE Opt5 11 12 79% 6% 85% 13%
Chart A4.1 - Best-Cases, improvements over base-cases

7th Floor SW
EUI
(kBtu/sf/yr)
EUI
Improvement
Over Base-Case
(kBtu/sf/yr)
sUDI
sUDI %
Improvement
Over Base-Case
TA
TA %
Improvement
Over Base-Case
Base-Case 24 N/A 66% N/A 22% N/A
Base-Case, NV, LC 15 8 66% 0% 66% 44%
Base-Case, NV, LC, SD 11 12 72% 6% 79% 57%
Opt1 13 11 71% 4% 70% 48%
Opt2 13 10 64% -3% 71% 49%
Opt3 12 11 65% -1% 71% 49%
Opt4 13 10 65% -2% 70% 48%
Opt5 13 10 63% -3% 70% 48%
Opt6 13 10 55% -11% 73% 51%
Chart A4.2 - 7
th
Floor South-West corner improvements over base-cases

51st Floor SW
EUI
(kBtu/sf/yr)
EUI
Improvement
Over Base-Case
(kBtu/sf/yr)
sUDI
sUDI %
Improvement
Over Base-Case
TA
TA %
Improvement
Over Base-Case
Base-Case 26 N/A 60% N/A 41% N/A
Base-Case, NV, LC 18 7 60% 0% 75% 34%
Base-Case, NV, LC, SD 12 13 87% 27% 80% 39%
Opt1 12 14 72% 12% 88% 47%
Opt2 12 14 66% 7% 88% 46%
Opt3 11 14 67% 7% 90% 49%
Opt4 12 14 71% 11% 88% 47%
Opt5 11 14 76% 16% 86% 45%
Opt6 13 12 72% 12% 87% 46%
Chart A4.3 - 51
st
Floor South-West corner, improvements over base-cases
A-48

7th Floor NE
EUI
(kBtu/sf/yr)
EUI
Improvement
Over Base-Case
(kBtu/sf/yr)
sUDI
sUDI %
Improvement
Over Base-Case
TA
TA %
Improvement
Over Base-Case
Base-Case 22 N/A 75% N/A 49% N/A
Base-Case, NV 13 9 75% 0% 73% 24%
Base-Case, NV, SD 11 11 83% 9% 75% 26%
Opt1 10 12 80% 5% 76% 27%
Opt2 9 12 76% 1% 77% 28%
Opt3 10 12 79% 5% 78% 29%
Opt4 11 11 74% -1% 76% 27%
Opt5 11 11 63% -11% 78% 29%
Opt6 10 12 75% 0% 79% 30%
Chart A4.4 - 7
th
Floor North-East corner, improvements over base-cases

51st Floor NE
EUI
(kBtu/sf/yr)
EUI
Improvement
Over Base-Case
(kBtu/sf/yr)
sUDI
sUDI %
Improvement
Over Base-Case
TA
TA %
Improvement
Over Base-Case
Base-Case 23 N/A 73% N/A 72% N/A
Base-Case, NV, LC 14 9 73% 0% 82% 10%
Base-Case, NV, LC, SD 13 10 83% 10% 82% 10%
Opt1 11 12 69% -4% 89% 18%
Opt2 11 12 73% 0% 87% 15%
Opt3 11 12 73% 0% 87% 15%
Opt4 11 12 65% -8% 89% 18%
Opt5 11 12 79% 6% 85% 13%
Opt6 11 12 80% 7% 84% 12%
Chart A4.5 - 51
st
Floor North-East corner, improvements over base-cases

Abstract (if available)

Abstract Creating buildings that are responsive to the immediate environmental conditions, require the building envelope to become a primary component for the mediation between outdoors and indoors. Fenestration patterns and shading devices for interior daylighting and thermal comfort are critical elements to enhance the capacity in which the building envelopes can be improved. However, despite a range of “rules of thumb” and design “best practices” are available to guide fenestration design decision-making, how to best apply them is often unclear when overlapped with urban constraints such as orientation, overshadowing of adjacent buildings, and local climate. ❧ A parametric workflow can facilitate the simulation of annual climate-based daylight and thermal performances in early stages of design and retrofit scenarios within the environment of Grasshopper and Rhino3D. The workflow includes a set of built-in components that encompass a conceptual model builder, a window placer, and an automated shading calculator based on peak temperature climate data. It implements a preliminary approach to determine and visualize the Thermal Autonomy (TA) of buildings using the Adaptive Comfort Standard (ACS), building upon other partial frameworks it also provides visualizations of yearly Useful Daylight Illuminance (UDI), and can perform multi-objective optimizations of daylight versus thermal calculations with the plugin Octopus. By determining optimal geometry for daylight aperture configurations and exterior shading elements across the façade, it acts as a design tool for students, architects and engineers. The approach and its novel features are described in the context of a hypothetical commercial building façade retrofit scenario located in downtown Los Angeles, where the best improvements reached a total of 16% for daylighting, 48% for the thermal comfort.

Linked assets

University of Southern California Dissertations and Theses

Conceptually similar

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

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

PDF

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

PDF

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

PDF

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

PDF

Double skin façades performance: effects on daylight and visual comfort in office spaces

PDF

Dynamic shading and glazing technologies: improve energy, visual, and thermal performance

PDF

ctrl+z: exploring the effects of undoing retrofits to pre-war buildings in Los Angeles

PDF

Mitigating thermal bridging in ventilated rainscreen envelope construction: Methods to reduce thermal transfer in net-zero envelope optimization

PDF

Effective light shelf and form finding: development of a light shelf design assistant tool using parametric methods

PDF

Daylight and health: exploring the relationship between established daylighting metrics for green building compliance and new metrics for human health

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

Evaluation and development of solar control screens: using daylight simulation to improve the performance of facade solar control screens

PDF

Visualizing architectural lighting: creating and reviewing workflows based on virtual reality platforms

PDF

Learning personal thermal comfort and integrating personal comfort requirements into HVAC system control loop

PDF

CFD visualization: a case study for using a building information modeling with virtual reality

PDF

Daylight prediction: an evaluation of daylighting simulation software for four cases

PDF

Impacts of indoor environmental quality on occupants environmental comfort: a post occupancy evaluation study

PDF

Modular shading: using design to mitigate bus rider thermal heat stress

PDF

Developing environmental controls using a data-driven approach for enhancing environmental comfort and energy performance

Asset Metadata

Creator Gamas Villamil, Alejandro Alberto (author)

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

School School of Architecture

Degree Master of Building Science

Degree Program Building Science

Publication Date 06/30/2016

Defense Date 06/16/2016

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

Tag daylighting,energy efficiency,OAI-PMH Harvest,optimization,parametric,thermal comfort,workflow

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Konis, Kyle (committee chair), Kensek, Karen (committee member), Noble, Douglas (committee member)

Creator Email agamas@hotmail.com,algamvill@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c40-259388

Unique identifier UC11280526

Identifier etd-GamasVilla-4488.pdf (filename),usctheses-c40-259388 (legacy record id)

Legacy Identifier etd-GamasVilla-4488.pdf

Dmrecord 259388

Document Type Thesis

Format application/pdf (imt)

Rights Gamas Villamil, Alejandro Alberto

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA