PARAMETRIC DESIGN AND SIMULATION OF A SMART FAÇADE FOR

HOT AND HUMID CLIMATES USING BIOMIMETICS, PCMs AND SMAs: A

CASE STUDY IN IRAN

A THESIS SUBMITTED TO
THE GRADUATE SCHOOL OF NATURAL AND APPLIED SCIENCES
OF
MIDDLE EAST TECHNICAL UNIVERSITY

BY

NEDA GHAEILI ARDABILI

IN PARTIAL FULFILLMENT OF THE REQUIREMENTS

FOR
THE DEGREE OF MASTER OF SCIENCE
IN
BUILDING SCIENCE IN ARCHITECTURE

JANUARY 2020
 Approval of the thesis:

PARAMETRIC DESIGN AND SIMULATION OF A SMART FAÇADE FOR

HOT AND HUMID CLIMATES USING BIOMIMETICS, PCMs AND
SMAs: A CASE STUDY IN IRAN

submitted by NEDA GHAEILI ARDABILI in partial fulfillment of the

requirements for the degree of Master of Science in Building Science in
Architecture, Middle East Technical University by,

Prof. Dr. Halil Kalıpçılar

Dean, Graduate School of Natural and Applied Sciences

Prof. Dr. Fatma Cana Bilsel

Head of the Department, Architecture

Prof. Dr. Soofia Tahira Elias Ozkan

Supervisor, Architecture, METU

Examining Committee Members:

Assoc. Prof. Dr. Ayşe Tavukçuoğlu

Architecture, METU

Prof. Dr. Soofia Tahira Elias Ozkan

Architecture, METU

Prof. Dr. Gülser Çelebi

Architecture, Cankaya University

Assoc. Prof. Dr. İdil Ayçam

Architecture, Gazi University

Assist Prof. Dr. Ayşegül Tereci

Architecture, KTO Karatay University.

Date: 31.01.2020
I hereby declare that all information in this document has been obtained and
presented in accordance with academic rules and ethical conduct. I also declare
that, as required by these rules and conduct, I have fully cited and referenced
all material and results that are not original to this work.

Name, Last name : Neda Ghaeili Ardabili

Signature :

iv
 ABSTRACT

PARAMETRIC DESIGN AND SIMULATION OF A SMART FAÇADE FOR

HOT AND HUMID CLIMATES USING BIOMIMETICS, PCMs AND
SMAs: A CASE STUDY IN IRAN

Ghaeili Ardabili, Neda

Master of Science, Building Science in Architecture
Supervisor : Prof. Dr. Soofia Tahira Elias Ozkan

January 2020, 149 pages

Buildings consume a huge portion of energy for the provision of comforts indoor.
This consume is mostly related to the performance of the façade. The bio-skin, as the
emulation of nature for the design of a sustainable façade, has been considered as an
efficient solution for the related energy consume by the façade. However, some
solutions existing in nature are dynamic and require energy for their functioning,
which is a problem for using biomimicry for the design of the efficient façade. As a
consequence, the inherent feature of smart materials can be a solution for this issue,
since it can convert the bio-skin into a smart one that is capable of adapting to
outdoor variations with less consume energy.

Based on the knowledge gained from the literature review about biomimicry and
smart materials, two natural examples were selected for mimicking and two smart
materials were selected as a heat exchanger and an actuator. The biomimicry
principles were used to design a shading device whose movements were achieved by

v
the NiTi50 actuator (SMA). Then the designed device was considered for designing
the layers of smart bio-skin. The additional proposed layers have the function of
ventilation and cooling the inlet air, and to this end, the RT-35 (PCM) was selected.
Ultimately, the proposed smart bio-skin was evaluated for its impact in reducing the
cooling loads and increasing the thermal comfort of occupants in August. The impact
of the smart bio-skin was initially evaluated by simulating a test unit whose east and
west walls were designed with the smart bio-skin. Then smart bio-skin was then
applied virtually to the east and west walls of a villa in Kish Island. Results of the
simulations showed that the smart bio-skin was effective in reducing the thermal
loads by 42.75% while providing improved comfort conditions indoors, with a
temperature of 34℃ which was 6℃ cooler than the base case scenario, while the
humidity was increased an average of 50% to 70%.

Keywords: Smart Bio-Inspired Façade, Biomimicry, Smart Materials, Cooling Load,

Comfort Sensation.

vi
 ÖZ

BİYOMİMETİK, PCM VE SMA KULLANARAK SICAK VE NEMLİ

İKLİMLER İÇİN AKILLI CEPHENİN PARAMETRİK TASARIMI VE
SİMÜLASYONU: İRAN'DA BİR VAKA ÇALIŞMASI

Ghaeili Ardabili, Neda

Yüksek Lisans, Yapı Bilimleri, Mimarlık
Tez Yöneticisi: Prof. Dr. Soofia Tahira Elias Ozkan

Ocak 2020, 149 sayfa

Binalar, iç mekandaki konforun sağlanması için enerjinin büyük bir kısmını tüketir.
Bu tüketim çoğunlukla iç mekan konfor koşullarını etkileyen cephe performansı ile
ilgilidir. Biyo-deri, sürdürülebilir bir cephe tasarımı için doğaya öykünme, cephe
tarafından ilgili enerji tüketimi için etkili bir çözüm olarak kabul edilmiştir. Ancak,
doğada var olan bazı çözümler dinamiktir ve işleyişleri enerji gerektirir, verimli
cephe tasarımı için biyomimikri kullanmak için başka bir sorun olarak düşünülebilir.
Sonuç olarak, akıllı malzemelerin doğal özelliği bu sorun için bir çözüm olabilir,
çünkü daha az enerji tüketimi ile dış mekan varyasyonlarına uyum sağlayabilen akıllı
bir deriye dönüştürebilir.

Yayınden elde edilen bilgilere dayanarak, adaptasyon ilkelerini taklit etmek için iki
doğal örnek seçilmiş, ısı değiştirici ve aktüatör olarak iki akıllı malzeme seçilmiştir.
Böylece, biyomimikri prensipleri gölgeleme cihazı tasarlamak için kullanılmıştır.
Ayrıca, tasarlanan cihazların hareketleri NiTi50 (SMA) ile elde edilmiştir. Önerilen
ilave katmanlar, giriş havasını havalandırma ve soğutma işlevine sahiptir, bu amaçla,
RT-35 (PCM) ısı değiştirici olarak seçilmiştir. Daha sonra, tasarlanmış akıllı biyo-
deri değerlendirilmiştir ve değerlendirme parametreleri de iç mekanların Ağustos

vii
ayında soğutma yükünün azaltılması ve kullanıcılar tarafından konfor hissinin
artması kabul edilmiştir. Tasarlanan akıllı biyo-derinin etkisi başlangıçta doğu ve
batı duvarları tamamen tasarlanmış akıllı biyo-deri ile kaplı bir test ünitesinde
değerlendirilmiştir. Daha sonra Kiş Adası'ndaki bir villada yapılan
değerlendirmelerde aynı parametreler analiz edilmiştir. Bu analize göre, akıllı biyo-
deri villanın soğutulması için gerekli termal yükü %42,75 oranında azaltmada etkili
olmuştur ve iç mekanlarda 34°C sıcaklık ve %70 bağıl nem ile 280 saatlik sabit koşul
sağlamıştır. Bu durum temel durum senaryosundan 6°C daha soğuk ve nem ortalama
%20 daha fazladir.

Anahtar Kelimeler: Akıllı Biyo-Deri, Biyomimikri, Akıllı Malzemeler, Soğutma

Yükü, Konfor Hissi.

viii
To my family, I couldn't have done this without your encouragement.

ix
 ACKNOWLEDGMENTS

I would like to express my deep and sincere gratitude to my research supervisor,

Prof. Dr. Soofia Tahira Elias Ozkan for giving me the opportunity to do research and
providing invaluable guidance throughout this research. Her dynamism, vision,
sincerity, and motivation have deeply inspired me. She has taught me the
methodology to carry out the research. It was a great privilege and honor to work
and study under her guidance. I am extremely grateful for what she has offered me.
I would also like to thank her for her friendship and empathy.

I owe sincere thanks to my examining committee members, Prof. Dr. Gülser Çelebi,
Dr. Ayşe Tavukçuoğlu, Dr. İdil Ayçam, and Dr. Ayşegül Tereci for their insightful
comments.

Thanks to all my friends who are with me throughout the study and also special
thanks to M. Elif Akdam, and Nastran Deljavan for their limitless love and
friendship.

Finally, I am grateful to my dearest family. To the greatest woman in my life, my

lovely mother Foroughlegha, and the strongest man of my life, my father Kiyoumars,
for their endless love, understanding, encouragement, and support. I also would like
to thank my lovely siblings Yashar and Uldooz for their advice that helped me find
my way whenever I am lost. With you I am the luckiest person alive.

Thank you all very much

x
 TABLE OF CONTENTS

ABSTRACT ...............................................................................................................v

ÖZ ........................................................................................................................... vii

ACKNOWLEDGMENTS .........................................................................................x

TABLE OF CONTENTS ......................................................................................... xi

LIST OF TABLES ................................................................................................. xiv

LIST OF FIGURES ............................................................................................... xvi

LIST OF ABBREVIATIONS .............................................................................. xxiv

CHAPTERS

1 INTRODUCTION .............................................................................................1

1.1 Problem Statement ..........................................................................................1

1.2 Research Objective .........................................................................................4

1.3 Research Questions .........................................................................................4

1.4 Hypothesis .......................................................................................................5

1.5 Procedure ........................................................................................................5

1.6 Disposition ......................................................................................................6

2 LITERATURE REVIEW ..................................................................................9

2.1 Biomimicry .....................................................................................................9

2.1.1 Implementation Approaches .....................................................................10

2.1.2 Level of Biomimicry .................................................................................12

2.1.3 Scale of Mimicking and Type of Adaptation Flow in Biomimicry ..........13

2.2 Bio-Skin ........................................................................................................13

2.3 Smart Materials (SMs) ..................................................................................21

xi
2.3.1 Phase Change Materials (PCMs) .............................................................. 23

2.3.2 Shape Memory Materials (SMMs) ........................................................... 34

2.3.3 Photovoltaic (PV) ..................................................................................... 47

3 MATERIALS & METHODOLOGY .............................................................. 49

3.1 Materials ....................................................................................................... 49

3.1.1 Software .................................................................................................... 49

3.1.2 Meteorological Data ................................................................................. 50

3.1.3 Test Unit ................................................................................................... 55

3.1.4 Case Study ................................................................................................ 56

3.1.5 Smart Materials ......................................................................................... 57

3.2 Method .......................................................................................................... 59

3.2.1 Design of the Bio-Skin Prototype ............................................................. 60

3.2.2 Analysis of Bio-Skin Performance ........................................................... 62

4 RESULT AND DISCUSSION ........................................................................ 69

4.1 Design of Smart Bio-Skin ............................................................................ 69

4.2 Analysis of Test Unit .................................................................................... 74

4.2.1 Electrical Energy Produced by Photovoltaic in Test Unit ........................ 74

4.2.2 Thermal Load in Test Unit........................................................................ 79

4.2.3 Energy Flow through Walls in Test Unit .................................................. 83

4.2.4 Occupants’ Comfort Sensation in Test Unit ............................................. 85

4.3 Analysis of Study the Case Study Residential Building .............................. 89

4.3.1 Integration of Smart Bio-Skin in Case Study ........................................... 92

4.3.2 Thermal Load in Case Study Residential Building .................................. 93

4.3.3 Occupants’ Comfort Sensation in Case Study Residential Building ........ 95

xii
5 CONCLUSION ................................................................................................99

6 REFERENCES ..............................................................................................103

APPENDICES

A. Appendix A: Optimized Angles of Shading Panels' Rows in the Test-Unit..113

B. Appendix B: Thermal Load of Test Unit .......................................................121

C. Appendix C: Energy Flow Through the East and West Walls of Each Scenario
from April to October ............................................................................................131

D. Appendix D: Psychrometric Chart of Each Scenario from April to October 137

xiii
 LIST OF TABLES

TABLES

Table 2.1 Summary of Publication about Bio-Skin................................................. 15

Table 2.2 Different Subgroups of Smart Materials ................................................. 22
Table 2.3 Summary of Different Subgroups of PCMs ............................................ 26
Table 2.4 Summary of Suggested Melting Range or Temperature for PCMs ........ 28
Table 2.5 Summary of Researches about the Implementation of PCMs in the
Façade ...................................................................................................................... 29
Table 2.6 Summary of Different Subgroups of SMMs ........................................... 37
Table 2.7 Comparison of Three Groups of SMAs .................................................. 40
Table 2.8 Summary of Publication about the Integration of SMMs in the Façade . 42
Table 3.1 Details about the Weather Data and Conditions for Psychometric Chart53
Table 3.2 Temperature of Exterior Surface of Wall Covered Completely with the
Optimized Shading Devices in Each Month, Analyzed with Honeybee Plugin ..... 58
Table 3.3 Considered Materials in the Wall Construction with their Properties ..... 63
Figure 4.8 South-East View of Test Unit to Illustrate the Considered Position for
PV Panels on the Test Unit ...................................................................................... 76
Table 4.1 Amount of Monthly Produced Energy by PV Panels in the Test Unit
from April to October, Calculated with ArchSim ................................................... 77
Table 4.2 Required Electrical Energy for Actuation of a Panel for One Series of
Movement ................................................................................................................ 78
Table 4.3 Hourly Report of Required Thermal Load for Cooling the Test Unit in
Four Scenarios in August with Percentage of effectiveness of PCM, Shading, and
Smart Bio-skin Scenarios in Comparison to Base Case Scenario for Reduction of
Thermal Load .......................................................................................................... 81
Table 4.4 Limits of Psychometric Processes of Air in Four Scenario of Test Unit 88
Table 4.5 Required Cooling Load for Each Zone of Case Study and in Four
Considered Scenarios, Analyzed with Honeybee Plugin ........................................ 93

xiv
Table 4.6 Percentage of Effectiveness of PCM, Shading, and Smart Bio-Skin in
Reduction of Thermal Load of Case Study Villa .................................................... 94
Table 4.7 Limits of Psychometric Processes of Air in Four Scenarios of Case Study
Villa......................................................................................................................... 98
Table A.1 Optimum Angle of Panels' Row of Test Unit in April......................... 113
Table A.2 Optimum Angle of Panels' Row of Test Unit in May .......................... 114
Table A.3 Optimum Angle of Panels' Row of Test Unit in June .......................... 115
Table A.4 Optimum Angle of Panels' Row of Test Unit in July .......................... 116
Table A.5 Optimum Angle of Panels' Row of Test Unit in August ..................... 117
Table A.6 Optimum Angle of Panels' Row of Test Unit in September ................ 118
Table A.7 Optimum Angle of Panels' Row of Test Unit in October .................... 119
Table B.8 Hourly Report of Required Thermal Load for Cooling the Test Unit in
Four Scenarios in April ......................................................................................... 121
Table B.9 Hourly Report of Required Thermal Load for Cooling the Test Unit in
Four Scenarios in May .......................................................................................... 122
Table B.10 Hourly Report of Required Thermal Load for Cooling the Test Unit in
Four Scenarios in June .......................................................................................... 123
Table B.11 Hourly Report of Required Thermal Load for Cooling the Test Unit in
Four Scenarios in July ........................................................................................... 124
Table B.12 Hourly Report of Required Thermal Load for Cooling the Test Unit in
Four Scenarios in September ................................................................................ 125
Table B.13 Hourly Report of Required Thermal Load for Cooling the Test Unit in
Four Scenarios in October..................................................................................... 126
Table D.14 Summary of Range and Most Frequent Temperature and Relative
Humidity for Each Case (April- October)............................................................. 149

xv
 LIST OF FIGURES

FIGURES

Figure 2.1 Framework for Problem-Based Approach (Aziz & El Sheriff, 2016) ... 10
Figure 2.2 Design Process in Problem-Based Approach (Badarnah Kadri, 2012) . 11
Figure 2.3 Framework for Solution-Based Approach (Aziz & El Sheriff, 2016) ... 12
Figure 2.4 Design Process in Solution-Based Approach (Badarnah Kadri, 2012) . 12
Figure 2.5 Bio-Skin Idea Extracted from Pill Bug Shell for Design of Moveable
Space (Gruber & Gosztonyi, 2010) ......................................................................... 17
Figure 2.6 Bio-Skin Idea Extracted from Old Man Cactus for Design of Shading,
(a). Old Man Cactus, (b). Design Idea (Gruber & Gosztonyi, 2010) ...................... 17
Figure 2.7 Bio-Skin Idea Extracted from Cephalopod Skin for Design of
Chromogenic Façade, (a). Design Idea, (b). Details of Design (Gruber &
Gosztonyi, 2010) ..................................................................................................... 17
Figure 2.8 Bio-Skin Idea Extracted from Mesembryanthemums for Design of
Hydro-Sensitive Façade, (a). Mesembryanthemums, (b). Design Idea (López,
Rubio, Martín, & Croxford, 2017) .......................................................................... 18
Figure 2.9 Bio-Skin Idea Extracted from Salvia Officinlis for Design of Sunlight
Reflector, (a). Salvia Officinalis, (b). Design Idea (López, Rubio, Martín, &
Croxford, 2017) ....................................................................................................... 18
Figure 2.10 Bio-Skin Implementation the Idea is Extracted from Bird of Paradise
for Design of Hinge-less Shading Lamella, (a). Bird of Paradise, (b). Design Idea
(Schleicher, et al., 2011) .......................................................................................... 18
Figure 2.11 Bio-Skin Idea Extracted from Waterwheel Plant for Design of Elastic
Surface, (a). Waterwheel Plant, (b). Structural Behavior of Designed Surface ...... 19

xvi
Figure 2.12 Bio-Skin Idea Extracted from Namib Desert Beetle for the Purpose of
Water Harvesting Wall, (a). Elevation of Designed Wall, (b). Section of Designed
Wall (Badarnah Kadri, 2012) .................................................................................. 19
Figure 2.13 Bio-Skin Implementation, the Idea is Extracted from Eye Pupil for the
Purpose of Controlling the Inlet Light by Façade, (a). Eye Pupil, (b). Designed
Façade (Jekot, 2008) ............................................................................................... 19
Figure 2.14 Bio-Skin Implementation, the Idea is Extracted from Bee Hive for
Design of Light Weight Structure and Regulation of Inlet Heat and Light (Nawal ,
Bakr, & E. Hasan , 2019) ........................................................................................ 20
Figure 2.15 Bio-Skin Implementation, the Idea is Extracted from Durian Fruit for
Purpose of Preventing the Heat and Direct Sunlight Gain, (a). Durian Fruit,
(b).Designed Façade (Nawal , Bakr, & E. Hasan , 2019) ....................................... 20
Figure 2.16 Bio-Skin Idea Extracted from Leaf for Design of Sensitive Façade to
Light, Air, and Water (a). Closed System, (b). Opened System (Yoneda, 2008) ... 20
Figure 2.17 Impact of Outdoor Temperature Range and PCM's Thickness Over the
Heat Flux ................................................................................................................. 32
Figure 2.18 Impact of PCM in the Reduction of Indoor Temperature ................... 33
Figure 2.19 Relation of PCM's Location and Thickness with Indoor Temperature 34
Figure 2.20 SMM.1, (a). Horizontal Roughness for Increment of Wind Velocity,
(b). Vertical Roughness for Decrement of Wind Velocity, (c). Required
Deformation of the Plates, (d). Examined SMP Plate with Three SMA Wires
(Lignarolo, Lelieveld , & Teuffel, 2011) ................................................................ 41
Figure 2.21 SMM.2 (a). Idea of Shading, (b). Assumed Reaction of SMA Wire, (c).
Deformation of Panel (Chamilothori, Kampitaki, & Oungrinis, 2015) .................. 41
Figure 2.22 SMM.3.1 Curtain with Cutting on it which is (a). Opened and (b).
Closed According to the Contraction and Extension of SMA Wires (Decker &
Yeadon , 2007) ........................................................................................................ 44
Figure 2.23 SMM.3.2 (a). Idea of the Blind and its Reaction at the Different
Temperature, (b). Movement of the Spring and Piston Which Rotates the Rod at
Top (Decker & Yeadon , 2007) .............................................................................. 44

xvii
Figure 2.24 SMM.4 (a). Louver and its Openings Variation According to the
Applied Electric Current, (b). Movement of the Actuator, (c). Sample Reaction's
Toward the Variation of Electric Current (Khoo, Salim, & Burry , 2012) ............. 44
Figure 2.25 SMM.5 (a). Idea of Folding Shutter, (b). Sample of the Real Sample of
Shatter, (c). Attachment of SMA Wire at the Panel (Pesenti , Masera , Fiorito, &
Sauchelli, 2015) ....................................................................................................... 45
Figure 2.26 SMM.6 (a). Idea of the Façade, (b). Attachment of Wire to the Opening
(Doumpioti, L. Greenberg , & Karatzas , 2010) ...................................................... 45
Figure 2.27 SMM.7 (a). Idea of Openable Panels at the Façade, (b). Examined
Panels, (c). Reaction of Panels According to the Variation of the Ambient
Temperature (Formrntini & Lenci , 2018) .............................................................. 45
Figure 2.28 Trends of SMMs' Integration at the Façade ......................................... 46
Figure 3.1 Hourly Average Dry and Dew Point Temperature of Kish Island During
the Year According to the Meteorological Data Retrieved from
http://climate.onebuilding.org/WMO_Region_2_Asia/IRN_Iran/HG_Hormozgan/I
RN_HG_Kish.Island.Intl.AP.408820_TMYx.zip. .................................................. 51
Figure 3.2 Hourly Average of Relative Humidity of Kish Island According to the
Meteorological Data Retrieved
fromhttp://climate.onebuilding.org/WMO_Region_2 _Asia/IRN_Iran/HG_
Hormozgan/IRN _HG_Kish.Island.Intl.AP.408820_TMYx.zip. ............................ 51
Figure 3.3 Annual Wind Rose for Kish Island, Drawn with Ladybug Plugin ........ 52
Figure 3.4 Annual Psychometric Chart of Kish Island, Drawn with Ladybug Plugin
According the Meteorological Data Retrieved from
http://climate.onebuilding.org/WMO_Region_2 _Asia/IRN_
Iran/HG_Hormozgan/IRN _HG_Kish.Island.Intl.AP.408820_TMYx.zip. ............ 53
Figure 3.5 Heat Comfort Index of Kish Island, Drawn with Ladybug Plugin ........ 54
Figure 3.6 Humidity Comfort Index of Kish Island, Drawn with Ladybug Plugin 54
Figure 3.7 Monthly Heating and Cooling Degree Hour of Kish Island, Drawn with
Ladybug Plugin ....................................................................................................... 55

xviii
Figure 3.8 Series of Villas from Damoon Saheli 2 Residential Complex, Kish
Island, Iran (‫[ مجتمع مسکونی دامون ساحلی‬Damoon Saheli Residential Complex], n.d.) 56
Figure 3.9 Floor Plans of Case Study Villa (‫[ مجتمع مسکونی دامون ساحلی‬Damoon Saheli
Residential Complex], n.d.) .................................................................................... 57
Figure 3.10 (1).Cross Section of Hydrated Cacti, (2). Cross-Section of Dehydrated
Cacti with Self-Shading Effects, (3). Hydrated Cacti, (4). Dehydrated Cacti
(Badarnah Kadri, 2012) .......................................................................................... 60
Figure 3.11 Stomata When is (1). Opened, (2). Closed (Badarnah Kadri, 2012) ... 61
Figure 3.12 Cross-Sections of the East and West Walls Showing the Constructional
Compositions for Simulation Purpose, (1). Base Case Scenario, (2). PCM Case
Scenario, (3). Shading Case Scenario, (4).Smart Bio-Skin Case Scenario ............. 63
Figure 3.13 Workflow of Analysis for Test Unit, a. Geometric Description of the
Smart Bio-Skin, b. Definition of Materials for HBzone, c. Geometric Definition of
the Test Unit, d. Definition of Simulation Condition, e. Running the Simulation and
Data Recoding ......................................................................................................... 64
Figure 3.14 Workflow for Geometric Definition of One Row of Shading Panels for
the Test Unit ............................................................................................................ 65
Figure 3.15 Framework for Creation of HBZone ................................................... 66
Figure 3.16 Definition of Materials in Honeybee ................................................... 67
Figure 3.17 Definition of PCM in Honeybee .......................................................... 67
Figure 3.18 Workflow to Run the Simulation ........................................................ 68
Figure 4.1Shading Device of Design Proposal 1 in the Various Positions of
According to the Solar Radiation ............................................................................ 70
Figure 4.2 Shading Device of Design Proposal 2 in the Various Positions
According to the Solar Radiation ............................................................................ 70
Figure 4.3 Graph of Solar Radiation Gain by Two Design Proposal Shading
Devices, on a Monthly Basis .................................................................................. 71
Figure 4.4 3D Layers of a Selected Design Proposal, (a). Shading Device
(Completely Opened), (b). Shading Device (Completely Closed), (c). Supporting

xix
Frame of Shading Device and Pores of Air Capsule When they are Closed, (d). Air
Capsule with PCM ................................................................................................... 72
Figure 4.5 3D Layers of Smart Bio-skin When Pores are Opened ......................... 73
Figure 4.6 3D Layers of Smart Bio-skin When its Pores are Closed ...................... 73
Figure 4.7 Solar Radiation for Optimized Annuals Tilt and Azimuth of PV Panels
in Kish Island, Calculated with Ladybug Plugin ..................................................... 75
Figure 4.8 South-East View of Test Unit to Illustrate the Considered Position for
PV Panels on the Test Unit ...................................................................................... 76
Figure 4.9 Comparison of Thermal Load of Four Scenarios from April to October,
Calculated with Honeybee Plugin ........................................................................... 79
Figure 4.10 Percentage of Effectiveness in Reduction of Thermal Load of PCM,
Shading, and Smart Bio-skin from April to October ............................................... 80
Figure 4.11 Effectiveness of the PCM, Shading, and Smart Bio-Skin in the
Reduction of Case's Thermal Load.......................................................................... 82
Figure 4.12 Hourly Energy Flow Through the West Wall of Base Case, PCM Case,
Shading Case, Smart Bio-Skin Case in August ....................................................... 84
Figure 4.13 Hourly Energy Flow Through the East Wall of Base Case, PCM Case,
Shading Case, Smart Bio-Skin Case in August ....................................................... 85
Figure 4.14 Psychometric Chart of Base Case According to the Hourly Indoor
Ambient Temperature and Relative Humidity at Inside the Test Unit in August,
Drawn with Ladybug Plugin ................................................................................... 86
Figure 4.15 Psychometric Chart of PCM Case According to the Hourly Indoor
Ambient Temperature and Relative Humidity at Inside the Test Unit in August,
Drawn with Ladybug Plugin ................................................................................... 87
Figure 4.16 Psychometric Chart of Shading Case According to the Hourly Indoor
Ambient Temperature and Relative Humidity at Inside the Test Unit in August,
Drawn with Ladybug Plugin ................................................................................... 87
Figure 4.17 Psychometric Chart of Smart Bio-Skin Case According to the Hourly
Indoor Ambient Temperature and Relative Humidity at Inside the Test Unit in
August, Drawn with Ladybug Plugin ...................................................................... 88

xx
Figure 4.18 Selected Section from Damoon Saheli 2 Complex in Kish Island, Iran
(‫[ مجتمع مسکونی دامون ساحلی‬Damoon Saheli Residential Complex], n.d.) .................... 90
Figure 4.19 Site Plan and Location of Villa at the Damoon Saheli 2 Complex ( ‫مجتمع‬
‫[ مسکونی دامون ساحلی‬Damoon Saheli Residential Complex], n.d.).............................. 91
Figure 4.20 Annual Thermal Load of the Villas in the Damoon Saheli 2, Analyzed
with Honeybee ........................................................................................................ 91
Figure 4.21 South-East View of the Case Study Villa, (a).Without Integration of
Smart Bio-Skin, (b). With Integration of Smart Bio-Skin on the East Wall........... 92
Figure 4.22 North-West View of the Case Study Villa, (a).Without Integration of
Smart Bio-Skin, (b). With Integration of Smart Bio-Skin on the West Wall ......... 92
Figure 4.23 Psychometric Chart of Villa without the Integration of Panels in
August, Drawn with Ladybug Plugin ..................................................................... 96
Figure 4.24 Psychometric Chart of Villa with the Integration of PCM in West and
East Walls in August, Drawn with Ladybug Plugin ............................................... 96
Figure 4.25 Psychometric Chart of Villa with the Integration of Shading in West
and East Walls in August, Drawn with Ladybug Plugin ........................................ 97
Figure 4.26 Psychometric Chart of Villa with the Integration of Smart Bio-Skin in
West and East Walls in August, Drawn with Ladybug Plugin ............................... 97
Figure B.1 Effectiveness of PCM, Shading, & Smart Bio-Skin Scenarios in
Reduction of Thermal Load of Test Unit in April ................................................ 127
Figure B.2 Effectiveness of PCM, Shading, & Smart Bio-Skin Scenarios in
Reduction of Thermal Load of Test Unit in May ................................................. 127
Figure B.3 Effectiveness of PCM, Shading, & Smart Bio-Skin Scenarios in
Reduction of Thermal Load of Test Unit in June ................................................. 128
Figure B.4 Effectiveness of PCM, Shading, & Smart Bio-Skin Scenarios in
Reduction of Thermal Load of Test Unit in July .................................................. 128
Figure B.5 Effectiveness of PCM, Shading, & Smart Bio-Skin Scenarios in
Reduction of Thermal Load of Test Unit in September........................................ 129
Figure B.6 Effectiveness of PCM, Shading, & Smart Bio-Skin Scenarios in
Reduction of Thermal Load of Test Unit in October ............................................ 129

xxi
Figure C.7 Energy Flow Through the East Wall in April ..................................... 131
Figure C.8 Energy Flow Through the West Wall in April .................................... 131
Figure C.9 Energy Flow Through the East Wall in May ...................................... 132
Figure C.10 Energy Flow Through the West Wall in May ................................... 132
Figure C.11 Energy Flow Through the East Wall in June .................................... 133
Figure C.12 Energy Flow Through the West Wall in June ................................... 133
Figure C.13 Energy Flow Through the East Wall in July ..................................... 134
Figure C.14 Energy Flow Through the West Wall in July .................................... 134
Figure C.15 Energy Flow Through the East Wall in September ........................... 135
Figure C.16 Energy Flow Through the West Wall in September ......................... 135
Figure C.17 Energy Flow Through the East Wall in October ............................... 136
Figure C.18 Energy Flow Through the West Wall in October .............................. 136
Figure D.19 Psychrometric Chart of Base Case in April, Drawn with Ladybug
Plugin ..................................................................................................................... 137
Figure D.20 Psychrometric Chart of PCM Case in April, Drawn with Ladybug
Plugin ..................................................................................................................... 137
Figure D.21 Psychrometric Chart of Shading Case in April, Drawn with Ladybug
Plugin ..................................................................................................................... 138
Figure D.22 Psychrometric Chart of Smart Bio-Skin Case in April, Drawn with
Ladybug Plugin ..................................................................................................... 138
Figure D.23 Psychrometric Chart of Base Case in May, Drawn with Ladybug
Plugin ..................................................................................................................... 139
Figure D.24 Psychrometric Chart of PCM Case in May, Drawn with Ladybug
Plugin ..................................................................................................................... 139
Figure D.25 Psychrometric Chart of Shading Case in May, Drawn with Ladybug
Plugin ..................................................................................................................... 140
Figure D.26 Psychrometric Chart of Smart Bio-skin Case in May, Drawn with
Ladybug Plugin ..................................................................................................... 140
Figure D.27 Psychrometric Chart of Base Case in June, Drawn with Ladybug
Plugin ..................................................................................................................... 141

xxii
Figure D.28 Psychrometric Chart of PCM Case in June, Drawn with Ladybug
Plugin .................................................................................................................... 141
Figure D.29 Psychrometric Chart of Shading Case in June, Drawn with Ladybug
Plugin .................................................................................................................... 142
Figure D.30 Psychrometric Chart of Smart Bio-Skin Case in June, Drawn with
Ladybug Plugin ..................................................................................................... 142
Figure D.31 Psychrometric Chart of Base Case in July, Drawn with Ladybug
Plugin .................................................................................................................... 143
Figure D.32 Psychrometric Chart of PCM Case in July, Drawn with Ladybug
Plugin .................................................................................................................... 143
Figure D.33 Psychrometric Chart of Shading Case in July, Drawn with Ladybug
Plugin .................................................................................................................... 144
Figure D.34 Psychrometric Chart of Smart Bio-Skin Case in July, Drawn with
Ladybug Plugin ..................................................................................................... 144
Figure D.35 Psychrometric Chart of Base Case in September, Drawn with Ladybug
Plugin .................................................................................................................... 145
Figure D.36 Psychrometric Chart of PCM Case in September, Drawn with
Ladybug Plugin ..................................................................................................... 145
Figure D.37 Psychrometric Chart of Shading Case in September, Drawn with
Ladybug Plugin ..................................................................................................... 146
Figure D.38 Psychrometric Chart of Smart Bio-Skin Case in September, Drawn
with Ladybug Plugin ............................................................................................. 146
Figure D.39 Psychrometric Chart of Base Case in October, Drawn with Ladybug
Plugin .................................................................................................................... 147
Figure D.40 Psychrometric Chart of PCM Case in October, Drawn with Ladybug
Plugin .................................................................................................................... 147
Figure D.41 Psychrometric Chart of Shading Case in October, Drawn with
Ladybug Plugin ..................................................................................................... 148
Figure D.42 Psychrometric Chart of Smart Bio-Skin Case in October, Drawn with
Ladybug Plugin ..................................................................................................... 148

xxiii
 LIST OF ABBREVIATIONS

ABBREVIATIONS

CDH Cooling Degree Hours

EPW EnegyPlus Weather

HB Honeybee

HDH Heating Degree Hours

LB Ladybug

PCMs Phase Change Materials

PTT Phase Transition Temperature

PV Photovoltaic

SMs Smart Materials

SME Shape Memory Effect

SMMs Shape Memory Materials

xxiv
 CHAPTER 1

1 INTRODUCTION

1.1 Problem Statement

Among the various functions of the building envelope, its roles in controlling
consumed energy by the building and providing occupants’ comfort within are more
crucial. This importance is highlighted when the building sector’s share in the total
energy consumption is considered (Hughes, Chaudhry, & Ghani, 2011).
Furthermore, there is a prediction that there will be an increment of 1.8% per year in
energy consumption by buildings until 2050 (Akeiber, 2016). One of the factors that
lead to the use of energy by the building during its occupancy period is the heat
transfer through the façade which disturbs the occupants’ comfort.

The envelope, as an intersection between two outdoor and indoor environments, can
perform a controlling role in these transfers. However, due to its conventionally fixed
feature, it cannot sufficiently respond to the variation of outdoor; consequently, the
preservation of indoor comfort leads to extra energy consumption. On the other hand,
the creatures in nature based on their adaptation ability to the constant fluctuation of
their surrounding environment are capable of sustaining efficiently (López, Rubio,
Martín, & Croxford, 2017). The emulation of these adaptation strategies in the
building could be an option for reducing the amount of energy used by buildings.
This emulation of nature has been recognized as “biomimicry” in the scientific
domain since 1982 (Radwan & Osama, 2016).

The phenomenon of biomimicry also has usage in the field of architecture. Indeed,
bio-inspired architecture has been proposed in many studies. For instance, Steffen
Reichert, Achim Menges, and David Correa have evaluated and designed a prototype

1
which is passively actuated by the air humidity, in 2015 (Reichert, Menges, &
Correa, 2015). Or the elastic deformation of the Strelitzia Reginae flower inspired
Simon Schleicher, Julian Lienhard, Simon Poppinga, Tom Masselter, Lena Müller,
and Julian Sartori to design a hinge-less flapping structure as shading for the façade
(Schleicher, et al., 2011). Another solution arising from an investigation on the
cephalopod mollusks has been called the Aero Dimm, which deals with the radiant
heat impact by its color alterations (Gruber & Gosztonyi, 2010). Another example is
the Breathing Skin that is influenced by the real skin, which has control over its
permeability of substance between the inside and outside environments (Becker,
n.d.). Finally, Lidia Badarnah Kadri in her Ph.D. research considered the various
examples from nature, whose physical, morphological, or behavioral adaptations are
used as the source of inspiration for the design of adaptive façades (Badarnah Kadri,
2012). While biomimicry as a response to a specific issue of the façade has been
studied and the bio-inspired façade are proposed by several researchers, the impact
of these bio-inspired design solution in the energy consumption of building has not
been discussed much. Also, the implementation of the biomimicry principles in the
field of architecture embodies some problems such as the selection of the proper
solution from the broad range of nature, the efficacy of solution after its scaling, and
the clashes between the integrated solutions in the design (Badarnah & Kadri, 2015).
Therefore, in this research, these issues are considered as the main parameters during
the selection, extraction, emulation, and design process until a prototype of an
appropriate façade for a cooling dominant region could be presented.

According to this emulation, the façade will be composed of one or more layers
which have moved to adapt to the changes in the surrounding. As a result, the adapted
solutions require the dynamism of the façade; for which energy would be required.
Thus, the source of the required energy is another issue that should be studied. In
this regard, the integration of smart materials in the building envelope can be
assumed as a feasible solution. Some smart materials are capable of assuming the
desired property or altering the existing properties based on one or more stimuli
(Ritter, 2007), (Samy Yousef Mohamed , 2017). Consequently, smart materials can

2
be used in the façade to provide the required dynamism, as they can be actuated by
the transient environmental factors that stimulated them. The smart materials based
on the actuated source and altered properties are categorized into various groups,
which makes the selection of the appropriate materials from this wide range to be the
initial problem. The disruptive effect of selected material at its optimum performance
while dealing with a specific issue over other factors of the comfort zone is another
problematic issue in using smart materials. Furthermore, the possibility of failure of
smart materials for presenting the desired reaction, due to the inappropriate
integration into the envelope is the other problem which should be studied.

Regarding the studies on the use of smart materials in the building, a review of
publications has been conducted in this field and two broad areas of interest have
been found: phase change materials (PCMs) and shape memory materials (SMMs).
For instance, a group of researchers is focused on the usage of the different types of
PCMs for the heat storage purpose in the building façade to reduce the heat flux
between the indoor and outdoor (Jin, A. Medina, & Zhang, 2013), (Ok Lee, A.
Medina, Raith, & Sun, 2015), (D. Zwanzig, Lian, & G. Brehob, 2013), (Chan, 2011),
(Wang, Wu, Wu, & Zhao, 2018) (Fang , 2009) (Wonorahardjo, Sutjahja, & Damiati,
2019). The use of SMMs is also considered by a series of researches, in which the
SMMs are actuated by an electric current or by temperature variations that lead to
their movement. As an example, in ‘Shape morphing wind-responsive façade
systems realized with smart materials’ the SMMs are considered as the moveable
element, which controls the wind velocity (Lignarolo, Lelieveld , & Teuffel, 2011).
Decker and Yeadon in their research for the thermally actuated blind considered the
SMM, which varies the blind opening according to the temperature and prevents the
extra heat gain through the windows (Decker & Yeadon , 2007). Chin Koi Khoo in
his research has used the SMMs as the actuator sensitive to the electric current and
based on their actuation the opening of louvers varies and controls the incoming
natural light (Khoo, Morphing Architecture with Responsive Material Systems ,
2013). According to these studies, hot climates provide suitable conditions for the

3
actuation of smart materials; for this reason, the case study area has been selected in
the hot and humid region of Kish in Iran.

1.2 Research Objective

The main concern of this thesis is assessing the effectiveness of the smart bio-
inspired façade in the reduction of energy used by the building, and increase the
occupants' comfort sensation. The study argues that the effectiveness of employing
biomimicry and smart materials in the design of a responsive façade can be
significant in providing a sustainable solution for the buildings in a cooling dominant
region, and provide comfort to its occupants. In other words, this study has three
main intentions. The first aim is the identification of examples from nature, which
live in the hot regions. Since, the living condition in the hot region requires the
specific features, consideration and study of those features help in deriving
sustainable design concepts. The application of these concepts would be gained
through the use of smart materials and their features; thus, the identification of
suitable smart materials as the sensor, actuator, and heat exchanger is the second
main aim of this thesis. Finally, the capability of the smart bio-inspired façade in the
scope of reducing the cooling load and increasing the comfort sensation by occupants
of the building is the third and last aim of this study.

1.3 Research Questions

According to these three main aims, three questions have to been addressed in this
study:

i. In the scope of sustainable design, how to design an adaptive façade

according to the principle gained from nature?
ii. Integration of which type of smart materials in the façade would be effective
for façade response according to the bio-inspired principle?

4
 iii. How much the combination of smart materials and biomimicry principles in
the façade would be accountable for the reduction of the cooling load of
buildings and the increment of occupants’ comfort sensation?

1.4 Hypothesis

The predicted outcome of this research is that the extraction of the sustainable design
ideas from nature and the combination of these ideas with the inherent features of
the smart materials will give the façade whose adaptability and smartness respond to
the transient alteration of the outdoor environment. This façade would be capable of
dealing with the uncomfortable condition of the hot-humid region and reduce the
building’s thermal load for cooling, while it also provides the comfort zone for the
occupants.

1.5 Procedure

The research, at the initial stage, focuses on the literature survey for the
determination of the smart materials and bio-inspired design idea suitable for
integration in the façade of buildings. The survey considers the definition, concept,
and researches that considered the implementation of biomimicry and smart
materials in the building façade. In the second stage, two design ideas of smart bio-
skin are presented, and according to their efficacy for controlling the solar radiation,
the most effective one is selected for further analysis. In the third stage, a test-unit is
designed to evaluate the function of designed smart bio-skin in the meteorological
condition of Kish Island. The evaluation is based on four scenarios, to analyze the
effectiveness of PCM, shading, smart bio-skin separately in terms of controlling the
thermal load and provision of the comfort zone. Finally, in the last step, the
effectiveness of PCM, shading, and smart bio-skin are measured in a case study,
which is a villa in the Damoon Saheli 2 Complex.

5
1.6 Disposition

Chapter 1: The introduction part of the thesis in which topics such as research
motivation, problems, objectives, questions, and hypotheses about the predicted
outcome of the research are discussed.

Chapter2: This section covers an overview of the biomimicry, bio-skin, smart

materials especially the phase change materials, shape-memory materials, and
photovoltaics. Based on this literature overview the appropriate type of smart
materials and biomimicry principles for the façade are clarified. The base of
clarification of the smart materials is the publications and studies in which the smart
materials are considered practically for the evaluation, and their actual results are
given.

Chapter3: This chapter deals with the materials and methods of the research. The
considered materials for this research in the initial stage were the publications about
the smart materials and biomimicry, to gain sufficient understanding about the
possible implementation of them in the façade. According to the obtained
comprehension from the initial stage, the design ideas are proposed and analyzed in
the as

sumed test-unit for the specification of their impact factor in the reduction of the
thermal load and providing the comfort condition. The list of software and plugins
used for this aim are listed, and the methods of simulation are discussed in this
chapter.

Chapter4: in this part, the result and outcomes of the simulations are presented and
discussed. Four scenarios are considered for the simulations in which the impact of
the PCM, shading and smart bio-skin are the subjection of the hourly simulation of
each month. Besides, the comparison of their effectiveness in comparison of the base
case is evaluated on both hourly and monthly time scales.

6
Chapter5: The last part is about the main highlights and the conclusion of this
research. Indeed, this section depicts the concluded result about the function of the
smart bio-skin in controlling the thermal load and comfort sensation by the occupants
and lists the recommendation for future studies.

7
8
 CHAPTER 2

2 LITERATURE REVIEW

This part focuses on the existed publications and writings about biomimicry and
smart materials. The researches in which the concept of the bio-skin and use of smart
materials in the façade are discussed from the practical aspect are also listed as the
existing case studies to obtain a holistic outlook

2.1 Biomimicry

Nature as a source of inspiration has been focused upon from the scientific aspect
since 1982, and this process of inspiration or mimicking from nature is called
“Biomimicry”. This topic gained its popularity through the influence of biologist
Janine Benyus’ book entitled "Biomimicry: Innovation Inspired by Nature", and
since then it has been used in various fields such as materials or construction.

For the literary definition of biomimicry, the word root should be identified. As
Baumeister (2014) stated, the term biomimicry stems from two roots of bios and
mimesis. The term bios means life and mimesis means to imitate. Or as Benyus
defined in her book (Biomimicry: Innovation inspired by nature, 1997) biomimicry
is ‘an emerging discipline that emulates nature's design and processes to create a
healthier, more sustainable planet’. Others (Kshirsager , Malani, & V.S.Tiware,
2017) defined it as a process during which nature is considered for examination, and
this examination covers entire models, systems, processes, and elements for either
emulation or inspiration to solve man’s problems. As mentioned, the aim of this
emulation is addressing problems properly and efficiently, since nature has been
dealing with identical issues efficiently and without any residual and active waste

9
for a long time; therefore, it can be considered as an effective source for solutions to
problems existing in the building sector.

2.1.1 Implementation Approaches

There are two main approaches to mimicking nature. Although, each author named
these groups differently their concepts and contents are identical. In this thesis, the
two terms of “Solution-based” and “Problem-Based” approach will be used for the
identification of the approaches.

i. Problem-Based Approach

At this approach, the initial step is the identification of the existed problem and
seeking the possible solution from nature. This approach which called
“Technology Pull” by Pohl & Nachtigall (2015)is defined as ‘an existing
technological product obtains new and improved qualities by the interpretation
and application of biological principles.’ Or ‘designers look to nature and
organisms for solutions, where designers must recognize exactly their design
problems and match their problems with organisms and creatures that have
solved similar problems.’ (Aziz & El Sheriff, 2016). Figure 2.1 and 2.2, show
two presented frameworks for the implementation of the Problem-based
approach.

Figure 2.1 Framework for Problem-Based Approach (Aziz & El Sheriff, 2016)

10
 Figure 2.2 Design Process in Problem-Based Approach (Badarnah Kadri, 2012)

ii. Solution-Based Approach

With this approach, nature and its knowledge are considered as the source for the
preparation of novel technology or system for human beings; therefore, at this
approach, the solution is not accountable for the specific issue. This approach is also
defined in the various publication. For instance, Pohl and Nachtigall in their book
(2015) define it as ‘the biological discoveries are the basis for the development of
new, technological products. The direction of development runs then from the
knowledge and data of biology to the formulation of an idea and development of a
technological product’. Another definition is mentioned by Al-Obaidi, Ismail,
Hussein, & Abdul Rahman (2017) as ‘Solution-based approach which refers biology
to design. This direction relies on the biologist or ecologist to adapt biological
properties into a human technology to find answers and then identify human design
problems.’ The processes for implementation of the solution based biomimicry
which is presented by Aziz et al. (2016) and Badarnah (2012) are presented in Figure
2.3 and 2.4 separately.

11
 Figure 2.3 Framework for Solution-Based Approach (Aziz & El Sheriff, 2016)

Figure 2.4 Design Process in Solution-Based Approach (Badarnah Kadri, 2012)

2.1.2 Level of Biomimicry

Through the examination of the implemented biomimicry technology, there are three
levels of emulation; 1. Organism, 2. Behavior, and 3. Ecosystem. The organism level
is related to the specified creature likes plants, animal or even a part of them. The
behavior level is emulating the behavior of the creature or even focusing on the
specific behavior of the organism that how it behaves. The ecosystem-level focuses

12
on the whole ecosystem and common roles that make them act successfully (Zari,
2010), (Al-Obaidi, Ismail, Hussein, & Abdul Rahman, 2017). Each of these levels
could be mimicked in the 5 different dimensions. The first dimension is “form”
which is based on the appearance of the creature, it answers how it looks like. The
second dimension is “material” which seeks a response to the question of what is
made of. The third dimension is the “construction” as the response to how it is made.
The fourth dimension is “process” which looks for the answer for how does it work;
and the last dimension is “function” that seeks the capability of the creature (Aziz &
El Sheriff, 2016).

2.1.3 Scale of Mimicking and Type of Adaptation Flow in Biomimicry

According to Al-Obaidi et al. (2017)defines the ratio of the scales should be

considered in the implementation. Since some functions are achievable in the
specified ratio to the whole system and in a case which this ratio does not consider
in the design and implementation process, it would not show the desired function.
The scales used in the field of biomimicry are 1. Nano, 2. Micro, 3. Meso, 4. Macro.

As Lopez et al. (2017) claim the organisms show their adaptation in the two ways of
dynamic and static. While at the dynamic mechanism the organism response to the
external stimulus through the movement, in the static strategy the creature makes
alterations in the properties to adapt itself to the environment.

2.2 Bio-Skin

Biomimicry, as a solution source, is useable in the various fields, one of these fields
is architecture, and more specifically is the façade of buildings. Consequently, a
façade that is designed according to the emulation of nature is called the bio-skin.

As the building sector is responsible for the big ratio of energy consumption and CO2
emission, and the façade as the interface layer between indoor and outdoor has the

13
potent role at the amount of consumed energy by the buildings, referring to the nature
for extraction of design idea for sustainable and efficient façade is a reasonable
solution. A list of publications has considered this topic from various perspectives,
the following table is a summary of these publications.

A list of publications has considered this topic from various perspectives, Table 2.1
is a summary of these publications, and the related figures of this table are presented
after the table through Figure 2.5 to Figure 2.16 In each of the listed research, an
issue related to the façade is addressed by mimicking nature; however, none of them
has analyzed the effectiveness of the emulated solution in the reduction of the
existing issue.

14
 Table 2.1 Summary of Publication about Bio-Skin

15
 Table 2.1 (continued)

16
 Figure 2.5 Bio-Skin Idea Extracted from Pill Bug Shell for Design of Moveable Space
(Gruber & Gosztonyi, 2010)

Figure 2.6 Bio-Skin Idea Extracted from Old Man Cactus for Design of Shading, (a). Old
Man Cactus, (b). Design Idea (Gruber & Gosztonyi, 2010)

Figure 2.7 Bio-Skin Idea Extracted from Cephalopod Skin for Design of Chromogenic
Façade, (a). Design Idea, (b). Details of Design (Gruber & Gosztonyi, 2010)

17
 Figure 2.8 Bio-Skin Idea Extracted from Mesembryanthemums for Design of Hydro-
Sensitive Façade, (a). Mesembryanthemums, (b). Design Idea (López, Rubio, Martín, &
Croxford, 2017)

Figure 2.9 Bio-Skin Idea Extracted from Salvia Officinlis for Design of Sunlight Reflector,
(a). Salvia Officinalis, (b). Design Idea (López, Rubio, Martín, & Croxford, 2017)

Figure 2.10 Bio-Skin Implementation the Idea is Extracted from Bird of Paradise for
Design of Hinge-less Shading Lamella, (a). Bird of Paradise, (b). Design Idea (Schleicher,
et al., 2011)

18
Figure 2.11 Bio-Skin Idea Extracted from Waterwheel Plant for Design of Elastic Surface,
(a). Waterwheel Plant, (b). Structural Behavior of Designed Surface

Figure 2.12 Bio-Skin Idea Extracted from Namib Desert Beetle for the Purpose of Water
Harvesting Wall, (a). Elevation of Designed Wall, (b). Section of Designed Wall
(Badarnah Kadri, 2012)

Figure 2.13 Bio-Skin Implementation, the Idea is Extracted from Eye Pupil for the Purpose
of Controlling the Inlet Light by Façade, (a). Eye Pupil, (b). Designed Façade (Jekot, 2008)

19
 Figure 2.14 Bio-Skin Implementation, the Idea is Extracted from Bee Hive for Design of
Light Weight Structure and Regulation of Inlet Heat and Light (Nawal , Bakr, & E. Hasan ,
2019)

Figure 2.15 Bio-Skin Implementation, the Idea is Extracted from Durian Fruit for
Purpose of Preventing the Heat and Direct Sunlight Gain, (a). Durian Fruit,
(b).Designed Façade (Nawal , Bakr, & E. Hasan , 2019)

Figure 2.16 Bio-Skin Idea Extracted from Leaf for Design of Sensitive Façade to Light,
Air, and Water (a). Closed System, (b). Opened System (Yoneda, 2008)

20
2.3 Smart Materials (SMs)

With the enhancement of technology novel materials with unique features have been
introduced and the smart materials are one of them, which possesses different
consumptions in different fields. A common definition of the SMs is the alteration
of one or more properties of the material in the reply to one or more stimuli (Samy
Yousef Mohamed , 2017). Four features separate the smart materials from others,
which are a. the capability of property change, b. the ability of energy exchange, c.
discrete size or location, and d. reversibility and bi-directionality (Schwartz, 2002).
The feature of discrete size or location and reversibility and bi-directionality are
common among the whole SMs. Indeed, the SMs do not require the extra or
complicated component or system for its alteration; besides, during their lifecycle,
they are capable of presenting the action and reverse action cyclically. However, the
shift of energy or the property is the factor that categorizes the SMs in two
distinguished groups of Type I and Type II materials.

i. Type I: Within the input energy, the materials present the property change.
The groups based on the type of input energy and presented property change is
classified into the different subgroups.

ii. Type II: This category converts the input energy to another form of energy;
therefore, the experienced alteration is the type of energy. This group is also
according to the type of input and shifted energy is categorized in the various
subgroups (Kienzl, 2002). Table 2.2 lists the subgroups of Type I and Type II smart
materials:

21
 Table 2.2 Different Subgroups of Smart Materials

Type Input Output Name of subgroup

Type I Mechanical Color Mechanchormic

Shape Shape memory material

Thermal Color Thermochromics- Thermotrope

State of matter Phase change materials

Electrical/Magn Color Electrochromic,

etic field Liquid crystals,
suspended particle

Viscosity Electrotheological,
Magnetorheological

Chemical Color Chemochromic

Radiation Color Photochromic

Energy

Type II Heat Electricity Thermoelectric

Electricity Heat Thermo-photovoltaic

Optical Electro-luminescent

Optical Electricity Photovoltaic

Chemical Chemo-luminescent

Optical Photo luminescent, fluorescent

The use of SMs in the building sector is not as conventional as is in other sectors;
however, Mel Schwartz (Schwartz, 2002)mapped a table of appropriate SMs that can
be used in the architecture. According to this table the three categories of phase
change materials (PCMs), shape-memory materials (SMMs), and chromogenic are
effective in the building façade. In this thesis, two groups of PCMs and SMMs are
considered for the design of the smart bio-skin. For this purpose, in the following
paragraphs, the written literature about the PCMs and SMMs will be discussed.

22
2.3.1 Phase Change Materials (PCMs)

The PCMs are capable of storing and releasing a large amount of heat as the latent
heat with precise alteration of temperature and volume (Bianco, et al., 2018). Indeed,
“PCMs utilize the latent heat of phase change to control the temperature within a
specific range. When the temperature rises above a certain point, the chemical bonds
in the materials will start to break up and the material will absorb the heat in an
endothermic process where it changes state from solid to liquid. As the temperature
drops, the material will give off energy and return to solid-state” (Kalnæs & Jelle,
2015, p. 152) . The storage of heat occurs with either the fusion or the vaporization
of PCMs. The amount of stored heat and volume and pressure change at the
vaporized PCMs is higher than the melted PCMs; however, since the required
temperature range of vaporization of PCMs is higher than the ambient temperature,
it is not the accountable candidate for the building envelope (Huang , Alva, Jia, &
Fang, 2017).

2.3.1.1 Classification of Phase Change Materials

PCMs according to their chemical properties are classified into three main groups.
(Fang , 2009, p. 6)

i. Organic

This group is grouped into two subgroups of Alkane Family (paraffin) with the
formula CnH(2n.+2) and Fatty Acids with the formula CH3(CH2)2nCOOH (Su,
2015, p. 375). The melting at this category occurs in the range of temperature instead
of the specific point of temperature, this range is named mushy zone and in general
is between the 20-70℃ (Memon, 2014, p. 873). The disadvantage of this category is
its flammability, indeed, the organic PCMs due to owning the Hydrocarbons in their
components are flammable whose flash point is 200 ℃ (Su, 2015, p. 375). The
second drawback of this category is its low thermal conductivity which is around 0.2
W/m.K (Akeiber, 2016, p. 1473); furthermore, this category is incompatible with

23
plastic, which leads to the issues with their capsulation. In other words, this category
of PCMs cannot be carried in plastic containers (Kalnæs & Jelle, 2015, p. 153). The
advantage of this category of PCMs is its stability in physical and chemical aspects.
These PCMs with this feature can experience a series of melting and solidification
without phase separation and decomposition, which is suitable for the long term
usage of PCM (Zhou, 2012, p. 596). Also, the small amount of this category is
capable to store the huge amount of heat due to its high latent heat capability
(Memon, 2014, p. 873); consequently, this category is a suitable candidate for the
compact places. In the comparison of other subgroups of PCMs, the organic PCMs
are cheaper, safe, with a reliable and predictable reaction (Su, 2015, p. 375).

ii. Inorganic

This category possesses two subgroups named Salt Hydrate saltnH2O and Metallic
(Kalnæs & Jelle, 2015, p. 153). Unlike the organic PCMs, the melting at this category
happens in one specific temperature and the melting range of this category covers
the border range from 10-900℃ that increases their usage area (Memon, 2014, p.
873). The drawback of this category is its high density, this should be considered in
the system in which the weight of the system is crucial (Mehling & Cabeza, 2008, p.
240). Besides, unlike the organic PCMs, they are not stable in the repetitive cycle of
melting and solidification, and there is the segregation of components after some
cycles of phase alteration, so not a suitable option for use in long-duration (Fang ,
2009, p. 6); furthermore, they show supercooling effect. The positive point of this
category is its high density, indeed, if the amount of stored heat is crucial and the
system in which this PCM is planning to be used can support its heavyweight, the
inorganic PCMs are a suitable option (Mehling & Cabeza, 2008, p. 240). This
category has higher conductivity in comparison of organic PCMs and it is close to
0.5 W/m.K (Akeiber, 2016, p. 1473)and they are non-flammable which makes them
suitable for usage in the industrial uses

24
 iii. Eutectic

This category is the combination of two above mentioned subgroups; consequently,

their function and feature are related to the combined components and their
combined ratio. While this category is expensive, it has e high thermal conductivity
and its thermal behavior is adjustable (Mehling & Cabeza, 2008, p. 240)

A summary of the subdivision of PCMs is listed in Table 2.3.

25
 Table 2.3 Summary of Different Subgroups of PCMs

26
2.3.1.2 Usage of Phase Change Materials in the Façade

In conformity with the inherent feature of PCMs, they are accountable for the heat
regulation in buildings. The regulation occurs in two ways:

Thermal control: As a thermal controller the PCMs adjust the air temperature instead
of conveying the heat (Mehling & Cabeza, 2008). Therefore, PCMs reduce the effect
of the outdoor wide temperature fluctuation at indoor temperature; consequently, the
indoor regardless of outdoor variation, experiences a narrow temperature fluctuation
(Fleischer, 2015).

Thermal storage: PCMs as the thermal storage has been used since the 1980s (Faraji,
2017, p. 105). In this category, the heat is stored by PCMs when there is extra supply
and released when there is demand

2.3.1.3 Selection Criteria of Phase Change Materials

To narrow down the broad range of existed PCMs for selecting one for the desired
function, a list of criteria should be considered; these benchmarks are listed as below:

i. Physical Aspect- Melting Point: this factor should be identified based on

the mean of days and night temperate and climate conditions (Akeiber,
2016, p. 1474)This property is defined differently in various publication,
Table 2.4, is a summary of these definitions

27
 Table 2.4 Summary of Suggested Melting Range or Temperature for PCMs

Definer (Source) Area of Usage Melting Range

Zhou (2012, p. 596) For comfort zone 18-30°C
For optimum heat storage Tr*=Ta**+ (1~3) °C
Kalnæs et al. (Kalnæs & Cooling purpose Up to 21°C
Jelle, 2015, p. 155) Human comfort zone 29-60°C
Hot water application 19-24°C
Waqas et al. (2013, p. 615) Free cooling 19-24°C
Warm climate Tp***=Ta**+2K
Chan (2011, p. 2949) Hong Kong 28-30°C
*Room temperature
**Average ambient temperature
***Peak melting temperature

ii. Thermodynamic: the factor measures the heat conductivity of PCMs a

higher conductivity represents the fast response of the materials
(Akeiber, 2016, p. 1474).
iii. Chemical Aspect: the other factor is the chemical property of the PCMs
in which the stability of materials with no or low supercooling;
furthermore, the compatibility of PCMs with their containers or other
construction materials should be considered (Fleischer, 2015, p. 38).

iv. Environmental Aspect: In this factor, the no-flammability, nontoxicity,

and safety of the PCMs for the environment are considered for the
selection (Fleischer, 2015, p. 38).

2.3.1.4 Case Studies of Phase Change Materials’ Implementation in the

Façade

In this section, 15 publications that evaluated the usage of PCM at the façade are
objected for the evaluation. Table 2.5 is a concise description of these studies:

28
 Table 2.5 Summary of Researches about the Implementation of PCMs in the Façade

29
 Table 2.5 (continued)

30
 Table 2.5 (continued)

31
*at this research the PCM is capsulated in the macro-scale balls; therefore, the mentioned thickness is assumptive. For the
calculation of assumed thickness, the mentioned total mass at the paper (110Kg) is divided by the coconut oil density
916Kg/m3. Then the volume is divided into the areas of two considered panels.
For providing a comprehensive insight into the effective use of PCM in the façade,
the above-listed researches are considered for evaluation. For this sake, a sample that
extracts and aligns with most of the considered research cases in the papers is
assumed as a base case. In a case in which the research condition does not match
with the assumed base case, with a few interferences, the examination condition is
converted to the base case. Therefore, in the succeeding paragraphs, the comparison
of uniformed researches is discussed.

i. Impact of PCM thickness and outdoor temperature over the heat flux: The
first considered option is the impact of the outdoor temperature and PCM
thickness over the heat flux reduction. Figure 2.17 represents this
consequence. The potent factor is the outdoor temperature range, by
consideration of stable indoor temperature, the increment of outdoor
temperature increases the heat flux. However, the accretion of the PCM
thickness has a reverse reaction over the heat flux.

Figure 2.17 Impact of Outdoor Temperature Range and PCM's Thickness Over the Heat
Flux

32
ii. Effectiveness of PCMs’ type on indoor temperature: Another assessment
which is considered is the effect of PCM type on the indoor temperature
reduction. According to the five papers that cover the reduction of the
indoor temperature by the implementation of PCM on both façade and
blind, the type of PCM affects the reduction of indoor temperature. Figure
2.18 illustrates the organic PCMs are not as effective as the inorganic
PCM. As presented in the graph.

Figure 2.18 Impact of PCM in the Reduction of Indoor Temperature

iii. Optimum place of PCMs according to their thickness: The last

assessment is the explanation of the optimum location of the PCM at the
wall section. Accordingly, 7 studies that discuss the location of PCMs are
considered for this assessment. Based on the data presented in these
articles, the best location for PCM depends on two variables of PCMs
thickness and indoor temperature. In general, for thickness under the
10mm, the optimized position for the PCM is from the middle of the wall
toward the inside. On the other hand, as the thickness of PCM increases

33
 (between 10 to 20 mm), the PCM should have the same distance from the
melting and freezing sources. Figure 2.19, depicts the relationship of the
PCM thickness and temperature of the indoor environment on the best
location of PCM.

Figure 2.19 Relation of PCM's Location and Thickness with Indoor Temperature

2.3.2 Shape Memory Materials (SMMs)

Shape Memory Materials, as one of the subgroups of the shape-changing smart

materials, possess the ability to retain the secondary phase which gained after severe
and quasi-plastically distortion until the required stimulus has been applied and
material recovers its primary stage (L. Sun, 2012). This phase alteration takes place
because of the Shape Memory Effect (SME). SME is ‘the ability of a material to be
deformed from one shape to another and then to return to its original shape after a
change in its surrounding stimulus environment’ (Schodek, 2005, p. 235).

34
2.3.2.1 Classification of Shape Memory Materials

There are three main subdivisions of SMMs:

i. Shape Memory Alloys (SMAs)

The first subgroup gained emphasis in both scientific and industrial fields is the
Shape Memory Alloys. SMAs as one of the SMMs go through severe deformation
and call up their first phase because of the SME. In this subgroup, the stimuli are
either thermal or mechanical force, and the initial phase is remembered only with
the heating up the SMAs (Lagoudas, 2008). During the transformation of SMMs,
there is no phase alteration, and all transformation occurs in the solid-solid base.
Therefore, the transformation does not cause the chemical variation, and the change
is in the atomic structure as the sheer lattice distortion (Ritter, 2007). There are two
types of SME for SMAs, the first SME is pseudo-elasticity in which the stimuli is
mechanical force, by loading the force the SMAs are strained and they could regain
their initial phase by unloading the stress (Lagoudas, 2008, pp. 13-14). The amount
of the required force for the deformation of the alloys depends on the temperature,
by raising the temperature of the alloy, the required stress increases too. The value
of elongation of the alloys by the strain is concerning the alloy’s elements, but in
general, the average elongation is 8% of the first length of the alloy (Czechowicz &
Langbein, 2015). The second type SME is Pseudoplasticity in which the thermal
force is the actuation source and divided into two subgroups of one-way effect and
two-way effect. (Ziółkowski, 2015). SMAs are also according to their primary
alloying elements are subdivided into three groups:

 Iron-based alloys
 Copper-based materials
 NiTi-based category (L. Sun, 2012)
ii. Shape Memory Polymer (SMPs)

‘Shape memory polymers (SMPs) are a class of smart materials that offer mechanical
action triggered by an external stimulus. More specifically, SMPs can remember one

35
or more shapes, each determined by network elasticity, but can be stored in
temporary shape by material immobilization, commonly by vitrification or
crystallization’ (Patrick T. Mather, 2009).

In the comparison of the SMAs the SMPs could be manufacturing easier and
cheaper, their rate of the recovery is higher, they are biocompatible, biodegradable,
and lighter (Jinsong Leng, 2011) (Pretsch, 2010). Another difference of the SMAs
and SMPs is the types of the stimulus, while in the SMAs are actuatd with the thermal
and mechanical load, the SMPs are responsible to various types of stimulus such as
the ‘light (UV and infrared light) and chemical (moisture, solvent and PH change)’
(Jinsong Leng, 2011, p. 1079). As mentioned in the article of “Shape-memory
polymers and their composites: Stimulus methods and applications” (Jinsong Leng,
2011) the SMPs, due to the low modulus possess the lower recovery stress-forces ,
have the response period and not point which has the long duration, possess lower
cycle life with the weak material stability. Besides, the shape memory effect is one-
way and not two-way which makes them less proper for the actuation and sensor
action.

iii. Shape Memory Hybrids (SMHs)

This subdivision of the Shape memory materials is completely novel and there is the
requirement of further research for clarifying its function. However, as mentioned
in the Shape Morphing Solar Shading (Francesco, 2016), the production of this type
of the SMMs does not require the sophisticated tools and proliferated knowledge
about the SMMs, and it could be produced with the ordinary elements which do not
own the shape memory effect. Besides, it possesses the narrow hysteresis in
comparisons of the other subdivisions of the SMMs.

The SMHs Consists of two parts of the matrix and inclusions, which shows the
transition and elastic functions, there is no chemical interaction between these parts
which causes the maintenance of the properties of each component (C.C. Wang,
2012). A summary of the subdivision of SMMs is listed in Table 2.6.

36
 Table 2.6 Summary of Different Subgroups of SMMs

37
 Table 2.6 (continued)

38
 Table 2.6 (continued)

39
2.3.2.2 Usage of Shape Memory Materials in the Façade

Albeit there is a lack of industrial fabrication of SMMs for the building and
construction sector (Ritter, 2007), at the list of publications, the SMAs are considered
as actuators. The actuation of SMAs causes the movements of panels at the façade.
These movements either control the air movement or the shading. The reason for
selecting the SMAs as the actuator is the large extent of work done by the small
scope of SMA (Soylemez , 2009).

2.3.2.3 Selection Criteria of Shape Memory Materials

At publications listed in the 2.3.2.4 “Case studies of SMMs’ Integration in the

Façade” section the NiTi based alloy is considered as the effective actuator in the
façade. Table 2.7, is a brief comparison of three types of SMAs which indicates the
reason for NiTi based alloy’s popularity.

Table 2.7 Comparison of Three Groups of SMAs

Type of SMAs Iron-based alloys Copper-based materials NiTi-based SMA

Advantages Cheaper Biocompatibility
Better electrical Corrosion resistance
conductivity Strong shape memory
Better thermal effect
conductivity PTT is less susceptible
Smaller hysteresis to components (C.
(C. Lagoudas , 2008) Lagoudas , 2008)
Disadvantages High thermal PTT is intensively Expensive (C.
hysteresis dependent to Lagoudas , 2008).
(150℃) (C. components
Lagoudas , PTT range beyond
2008) the ambient
temperature range
(Lexcellent, 2013)

40
2.3.2.4 Case Studies of Shape Memory Materials Integration in the Façade

Table 2.8 covers researches mentioned to the idea of SMMs’ usage or real integration
of the SMMs as the actuator at building façade. Factors such as the usage purpose,
type of SMMs’ transformation, the quantity of the SMMs, type of deformation at
façade, actuation force of SMMs, and type of SMMs are considered for evaluation
and comparison. Figures of the listed researches in Table 2.8 are also presented from
Figure 2.20, to 2.27.

Figure 2.20 SMM.1, (a). Horizontal Roughness for Increment of Wind Velocity, (b).
Vertical Roughness for Decrement of Wind Velocity, (c). Required Deformation of the
Plates, (d). Examined SMP Plate with Three SMA Wires (Lignarolo, Lelieveld , & Teuffel,
2011)

Figure 2.21 SMM.2 (a). Idea of Shading, (b). Assumed Reaction of SMA Wire, (c).
Deformation of Panel (Chamilothori, Kampitaki, & Oungrinis, 2015)

41
 Table 2.8 Summary of Publication about the Integration of SMMs in the Façade

42
 Table 2.8 (continued)

43
 Figure 2.22 SMM.3.1 Curtain with Cutting on it which is (a). Opened and (b). Closed
According to the Contraction and Extension of SMA Wires (Decker & Yeadon , 2007)

Figure 2.23 SMM.3.2 (a). Idea of the Blind and its Reaction at the Different Temperature,
(b). Movement of the Spring and Piston Which Rotates the Rod at Top (Decker & Yeadon
, 2007)

Figure 2.24 SMM.4 (a). Louver and its Openings Variation According to the Applied
Electric Current, (b). Movement of the Actuator, (c). Sample Reaction's Toward the
Variation of Electric Current (Khoo, Salim, & Burry , 2012)

44
Figure 2.25 SMM.5 (a). Idea of Folding Shutter, (b). Sample of the Real Sample of Shatter,
(c). Attachment of SMA Wire at the Panel (Pesenti , Masera , Fiorito, & Sauchelli, 2015)

Figure 2.26 SMM.6 (a). Idea of the Façade, (b). Attachment of Wire to the Opening
(Doumpioti, L. Greenberg , & Karatzas , 2010)

Figure 2.27 SMM.7 (a). Idea of Openable Panels at the Façade, (b). Examined Panels, (c).
Reaction of Panels According to the Variation of the Ambient Temperature (Formrntini &
Lenci , 2018)

45
In conformity with Table 2.8, the trend of SMMs usage at the façade has been depicted in
Figure 2.28.
ion SMMs on Ms SMMs
Caused Quanti sfor e of Type

SMA Spring
Usage Deformat ty of mati SM of
Actu

Tran Forc

Joule Heating
SM atio
Ms' n

Fold
several
1
Fold
2D Size Change
Purpose

C. Inlet Light
C. Aire Movement

0 1 2 3 4 5 6 7 8

SMM. 1 SMM. 2 SMM. 3.1 SMM. 3.2 SMM. 4 SMM. 5 SMM. 6 SMM. 7

Figure 2.28 Trends of SMMs' Integration at the Façade

Based on Figure 2.28, the SMA wire has the most usage, the joule and thermal
heating have the same values; however, if the energy consumption is considered as
the fundament of the design, the thermal heating will be the optimum option. On the
other hand, if the ranges of SMMs do not match with the ambient temperature, the
joule heating also could be considered as an actuator. For the SMMs movement, the
linear movement is the most considered type of movement at the designs. The
quantity of the SMMs completely depends on the design and the attachment of
SMMs; in general, the 2 SMMs are mostly consumed in these seven studies. The
caused deformation also depends on the attachment method of SMMs; overall, in the
case of the direct implementation of SMMs, the panels follow the same deformation
that occurred in the SMMs. On the other hand, by consideration of the mechanical
system, the linear movement of the actuator can be converted to another type of
movement as it happens in SMM.3.2.

The usage purpose of controlling the sunlight and heat gain has popularity among
the studies.

46
2.3.3 Photovoltaic (PV)

Photovoltaic as a subgroup of type II smart material is capable of converting one

form of energy into another form. In other words, with the stroked solar radiation,
the photon energy is absorbed by the atoms of PV, and this excessed energy leads to
the movement of the atom to the higher energy level, in which the atom cannot
maintain, and has to release that extra energy to get back to its previous energy level.
In this condition, with the usage of semi-conductor materials, PVs catch the released
energy and produced electricity (Addington & Schodek, 2005). As this category of
the material can produce a useful type of energy, it is grouped as the generator.

2.3.3.1 Classification of Photovoltaic

The PV cells can be classified into three categories:

Monocrystalline: Since the production of this category requires the involvement of

high energy and labor work, it is expensive. The maximum theoretical efficiency
defined for this category is 33%, which in the market scope it drops to 19.4%
(Alfughi, 2015). Indeed, the monocrystalline PV is capable of the conversion of
19.4% of the photon energy into the electricity. based on this efficiency rate, the
payback period of the cell from the energy scope is approximate 1-2 years (Sick &
Erge, 1998).

Polycrystalline: The high cost of the monocrystalline cell causes the production of
the cheaper polycrystalline cells, the production procedure of this cell is easier than
the monocrystalline cell; therefore, the finished cost of the product is cheaper in
comparison of the monocrystalline. While this category is not as efficient as the
monocrystalline, its low-cost leads to its popularity in the market (Alfughi, 2015).

Thin-film solar cells: To reduce the crystalline cell’s cost, the faster production
system and consumption of fewer materials in the production of the cell are
considered as methods for manufacturing of the thin-film solar cells. The considered

47
method for the reduction of the cell’s cost declines the efficacy of the cell and makes
it about one-third of the monocrystalline. Indeed, this category presents an efficiency
of around 8~11% during its early working period, later this value decreases with
30%. Because of the low manufacturing cost, the payback period of this category is
fewer than 1-2 years assumed for the monocrystalline (Sick & Erge, 1998).

2.3.3.2 Challanges of Photovoltaic’s Usage

The function of PV depends on the series of factors, the factor which alteration can
negatively impact the PV function. Next paragraphs cover these effective factors and
challenges caused by their sift.

Orientation and tilt: the maximum solar radiation opts in the case that the module
has tilted to an angle equal the (latitude -10°) and facing the right direction. The
underestimation of this fact can reduce the expected efficiency of the module.

Shading of fixed components: another distractive factor for the function of the PV is
the shading caused by the fixed components such s the neighborhood buildings. This
issue is tolerable in the case the neighborhood possesses a low-rise context. In this
case, by consideration of the distance between the buildings, the optimum position
for the module without the shadow could be calculated, However, in the high density
and mixed context, where both high-rise and low-rise buildings compactly are
located next to each other, the high-rise building provides a constant shading on the
PV and reduces the efficiency of the modules.

Shading of temporary components: this type of shading mostly related to the adjacent
trees. The shading caused by trees varies either with the seasonal shift or the growth
of the trees. To coping with this issue the possible preventions are 1. planting trees
on the north side, b. growing small-sized tree with a maximum height of 6 meters,
and c. cropping trees for avoiding their excessive growth (Luque & Hegedus, 2003).

48
 CHAPTER 3

3 MATERIALS & METHODOLOGY

This section focusses on the materials and method of data gathering and analysis of
this research. The details about the publications, accountable software, suitable smart
materials and pinnacles from nature, assumed test-unit, and the selected case study
is presented as the materials of this research. Furthermore, the method of selection
and framework for the simulation of cases are discussed as the methodology.

3.1 Materials

In this research, the effectiveness of the biomimicry principle and smart materials
are investigated on a test-unit by the computer-based energy simulation program,
then according to the effectiveness of smart bio-skin in the test-unit, the degree of its
effectiveness in an existing case is calculated. For this reason, the considered
research materials are as below:

3.1.1 Software

3D modeling Rhino interface and parametric visual scripting interface of

Grasshopper were used for the parametric design of the façade. The environmental
design analysis plugin of Ladybug (LB) was used for importing the weather data in
EnegyPlus Weather (EPW) format and analyzing them for pre-design
comprehension of the site and its climatic conditions. The environmental design
analysis plugin of Honeybee (HB) was used for connecting the defined script in the
Grasshopper to the open-source energy modeling engine EnergyPlus for simulating
the panels. Besides, the Plugin Diva was used for the calculation of provided energy
by photovoltaic panels. The reason for the selection of these software and plugins

49
are the possibility of defining the phase change material’s property and availability
of software’s working manual.

3.1.2 Meteorological Data

Both the case study and test-unit are located in Kish Island, Iran, whose coordinates
are 26.56°N, 53.99°E and 3.5-5m height above sea level (Google Earth, n.d.).
According to Ghobadian (2015, p. 68), Kish Island has a hot and humid climate zone,
where has a long period of hot summer, with two months of mild winter during
January and February. The volume of precipitation is low; however, for the sake of
adjacency with the sea, the level of air humidity is high in the entire year.
Furthermore, because of the salinity soil of Island, the vegetation on the island is
scanty, and this condition leads to the flood even with the few amounts of
precipitation during the year. The weather data used in the analysis is for the period
between 2003-2017 retrieved from http://climate.onebuilding.org/WMO_Region_2
_Asia/IRN_Iran/HG_Hormozgan/IRN_HG_Kish.Island.Intl.AP.408820_TMYx.zip

The hottest month of the year is August with an average hourly temperature range of
32.-36℃ and the coldest month of the year is January with an average hourly
temperature range of 16.-23 ℃. The range of average hourly temperatures of Kish
Island during the year is presented in Figure 3.1.

50
 50

40
Temperature (℃)

30

20

10

0
January

February

March

April

May

June

July

August

September

October

November

December
-10
Month
Dry bulb tem. averaged of min for each hour Dry bulb tem. averaged for each hour
Dry bulb tem. averaged of max for each hour Dew point tem. averaged of min for each hour
Dew point tem. averaged for each hour Dew point tem. averaged of max for each hour

Figure 3.1 Hourly Average Dry and Dew Point Temperature of Kish Island During the
Year According to the Meteorological Data Retrieved from
http://climate.onebuilding.org/WMO_Region_2_Asia/IRN_Iran/HG_Hormozgan/IRN_HG
_Kish.Island.Intl.AP.408820_TMYx.zip.

The average relative humidity during the year is approximately the constant value of
67% the lowest value belongs to May with the monthly average of 61.16% and the
highest value belongs to October with the monthly average of 73.33%. Figure 3.2,
presents the minimum, maximum and average hourly relative humidity in Kish.

120
Relative Humidity (%)

100
80
60
40
20
0
January

February

March

April

May

June

July

August

September

October

November

December

Month
Min averaged for each hour Averaged for each hour

Figure 3.2 Hourly Average of Relative Humidity of Kish Island According to the
Meteorological Data Retrieved fromhttp://climate.onebuilding.org/WMO_Region_2
_Asia/IRN_Iran/HG_ Hormozgan/IRN _HG_Kish.Island.Intl.AP.408820_TMYx.zip.

51
The dominant wind is from the west and northwest direction. The maximum wind
speed is during April with a speed of 4.97 m/s and the minimum speed is during
December with a speed of 2.92 m/s. Figure 3.3, presents the annual wind rose drawn
with Ladybug plugin.

Figure 3.3 Annual Wind Rose for Kish Island, Drawn with Ladybug Plugin

The annual psychometric chart of the island obtained from Ladybug plugin is
presented in Figure 3.4. The inputs of this chart are the relative humidity and the dry-
blub temperature of the island, and the output is the frequency of the climatic
condition on the island. The light purple frame in the chart is the comfort zone during
the winter, and the dark purple color is for the comfort region during the summer.
The more details about the minimum and maximum temperature and relative
humidity, wind speed and direction, type of activity, type clothing and the comfort
percentage are presented in Table 3.1.

52
 Figure 3.4 Annual Psychometric Chart of Kish Island, Drawn with Ladybug Plugin
According the Meteorological Data Retrieved from
http://climate.onebuilding.org/WMO_Region_2 _Asia/IRN_ Iran/HG_Hormozgan/IRN
_HG_Kish.Island.Intl.AP.408820_TMYx.zip.

Table 3.1 Details about the Weather Data and Conditions for Psychometric Chart

Month Temperature Relative Wind Type of Clothing Comfort

(℃) Humidity Activity (clo) Percentage
(%) (%)
Min Max Min Max Speed Direction NS*
(m/s) (°)
January 16 22.7 36 86.1 4.3 229.6 2 54.84
February 17.25 25.3 35 85.2 3.67 208.85 2 36.6
March 20 27 39.8 89.3 4.05 215.94 0.5 25.94
April 22.5 31.5 37.2 84.7 4.97 248.76 0.5 30.28
May 27 35.5 23.5 82.75 4.5 239.79 Sitting 0.5 10.48
June 29.1 35.4 38 87.2 3.48 219.87 =1mph 0.5 1.94
July 32 35.6 52.1 83.6 3.6 203.06 (metabolic 0.5 0
August 32.4 36.4 43.4 83.2 2.94 174.66 rate) 0.5 0
September 30.6 35.25 46.4 88.4 3.14 213.6 0.5 0
October 27.3 33 45.6 94.7 3.74 186.145 0.5 8.06
November 22 28.6 38.1 84.1 3.12 203.11 0.5 35.98
December 16.25 25.7 32.5 97.25 2.92 203.41 2 31.58

53
In Figure 3.5, the red region shows the discomfort region in the scope of temperature.
In this region, the outdoor temperature is above the 28 ℃. And this condition exists
during the 7 months from April until October. The blue region indicates the comfort
region from November to March in which the temperature is between the 17 to 28.
Figure 3.6 presents the annual comfort and discomfort in the scope of relative
humidity. The red color represents the discomfort in which the relative humidity is
above 70% and exists during the April until November, the blue region presents
comfort in which the relative humidity is between 20 to 70%.

Figure 3.5 Heat Comfort Index of Kish Island, Drawn with Ladybug Plugin

Figure 3.6 Humidity Comfort Index of Kish Island, Drawn with Ladybug Plugin

54
According to Figure 3.5, the period of April to October as the thermal discomfort
period of Kish Island is the analysis duration of this research.

Figure 3.7 presents the heating and cooling degree hours of Kish Island. The red
color shows the heating degree hours (HDH) and blue is the indicator of cooling
degree hours (CDH). As previously mentioned, the city is cooling load dominant,
and during August, the cooling load is maximum.

Figure 3.7 Monthly Heating and Cooling Degree Hour of Kish Island, Drawn with
Ladybug Plugin

3.1.3 Test Unit

The considered test unit is a cubic space having dimension of 4×4×4m3. It has a
window measuring 1.8m×2m in its south side and a door with a dimension of
0.9m×2.1m at its north side. The building materials and construction type, for the
sake of utility in the existing case, is assumed to be the same as the case study
building.

55
3.1.4 Case Study

The case study is conducted virtually on an existing two-floor house in the Damoon
Saheli 2 complex on Kish Island, in the south of Iran. The island has a hot and humid
climate, which contributes to high cooling loads in buildings. The houses in the
complex have a rectangular plan which is oriented from north to south. Figure 3.8
depicts this complex and its villas among which the most problematic case in the
scope of requiored cooling load is considered for evaluation in the research.

Figure 3.8 Series of Villas from Damoon Saheli 2 Residential Complex, Kish Island, Iran
(‫[ مجتمع مسکونی دامون ساحلی‬Damoon Saheli Residential Complex], n.d.)

The net area of the case study villa is 205m2, and the typical plan layout of the
villas is presented in Figure 3.9.

56
 Figure 3.9 Floor Plans of Case Study Villa (‫[ مجتمع مسکونی دامون ساحلی‬Damoon Saheli
Residential Complex], n.d.)

3.1.5 Smart Materials

Two types of smart materials for the purpose of cooling and actuation force have
been used in the design of the façade: phase change materials (PCM) and shape
memory alloys (SMA). For the selection of appropriate types of smart materials, the
publications in which the implementation of smart materials are assessed practically
are considered as a guideline.

i. PCM

As discussed in the literature review section 2.3.1.3, “Selection Criteria of PCM”

four criteria should be considered for the selection of the appropriate type of PCM.
In line with the four listed criteria in this section and considered usage area of PCM
as the heat exchanger in the wall section of the building at this research, the organic
PCM is a suitable type of PCM. To specify the type of organic PCM for this research,
the melting range of the PCM was considered as the indicator. As listed in Table 2.4
of the literature review according to Waqas et al. (2013, 615), the suitable melting

57
range for PCMs for the warm climate is the average ambient temperature plus 2℃.
Since in this research, the PCM is considered as the heat exchanger for reduces the
outdoor high temperature’s impact on indoors. For this purpose, the average outdoor
ambient temperature should be added by 2℃ to show the required PCM’s melting
range. However, since in this research, the PCM is embedded in the wall section
which is covered with optimized shading, the conducted heat through the wall will
be some degrees fewer than the outdoor ambient temperature; consequently, the
temperature of the exterior surface of the wall is considered as an indicator of melting
range. According to the calculated values, by the Honeybee plugin, the temperature
of the exterior surface of the wall for each is listed in Table 3.2.

Table 3.2 Temperature of Exterior Surface of Wall Covered Completely with the
Optimized Shading Devices in Each Month, Analyzed with Honeybee Plugin

Month Jan. Feb. Mar. Apr. May June July Aug Sep. Oct. Nov Dec.
. .
Tempe 19.8 21.2 23.2 27 30.6 32 33.8 34 32.4 29.7 25.7 21.6
rature
(℃)

As presented in Figure 3.5 cooling is required during 7 months from April to

October, the exterior surface of the wall during this period is between 27 to 34℃;
therefore, the melting range for suitable PCM would be:

Tp=Ta+2℃ (Waqas, 2013, p. 615),

Where Tp is Melting Range of PCM (℃) and Ta is Average of Outdoor Ambient

Temperature(℃)

Tp=(27~34) ℃+2℃

Tp= 29~36℃

58
In line with the assumed temperature range, PCM RT35 can be selected for the
research. RT 35 is an organic PCM which possesses the melting range of 29~36℃
℃ (PCM RT-LINE) and matches with the exterior surface temperature of the wall
from April to October of the case study villa.

ii. SMA

The SMA works as the actuation source for the movement of the shading panels in
the façade, and since the rotations of panels are defined according to the
minimization of the infiltrated solar radiation, the rotations do not follow the air
temperature variation. Therefore, the considered actuation source of SMA could not
be the ambient temperature. Thus, the actuation source is joule heating which is
provided by the photovoltaic panels attached to the case’s roof. Based on the
literature review about the SMA section 2.3.2.3, 2.3.2.3, “Selection Criteria of
SMMs” the most popular type of the SMA for the façade is Ni50Ti50, so regardless
of the phase transition temperature of the SMA, Ni50Ti50 is considered for the panels.

3.2 Method

The research is based on the four following steps:

i. Design of a suitable system for the façade according to the bio-inspired

principle
ii. Selection of the appropriate types of smart materials for the façade; namely
SMA and PCM
iii. Testing and analyzing the design in a test unit by simulating its performance
with the energy modeling engine
iv. Simulation of the thermal performance in the case study villa before and after
the integration of the bio-skin wall.

Each above-listed step is described in details in the following sections

59
3.2.1 Design of the Bio-Skin Prototype

The Problem-based approach of biomimicry is considered for applying the bio-

inspired design. For this purpose, the problems related to the test unit are defined in
the first step of the study, according to the climatic issues discussed in section 3.1.2,
“Meteorological Data” in Material and Methodology Chapter, the high air
temperature with the high level of relative humidity causes issues which interrupt the
occupants comfort sensation and leads to the extra cooling load.

By reframing these issues within the natural context and the pinnacles from nature,
which tackle the same issues, are identified through the relevant literature. Therefore,
the publications about the organisms, which sustain in the hot region are studied and,
the effective strategies for inspiration are extracted accordingly. This study has
emulated the toothpick cactus for its self-shading ability and the stomata for
permeability properties. Figures 3.10 and 3.11 illustrate the selected pinnacles for
the bio-inspired design. The reason for the selection of these pinnacles from nature
is the availability of their biological data in different publications that eases the
mimicking process.

Figure 3.10 (1).Cross Section of Hydrated Cacti, (2). Cross-Section of Dehydrated Cacti
with Self-Shading Effects, (3). Hydrated Cacti, (4). Dehydrated Cacti (Badarnah Kadri,
2012)

60
 Figure 3.11 Stomata When is (1). Opened, (2). Closed (Badarnah Kadri, 2012)

These natural strategies are converted into the design strategy for the Bio-Skin
prototype. The bio-skin has four sections, the first one which is the outermost part of
the prototype and integrated on the exterior surface of the wall is the smart shading
panel which is responsible for the reduction of solar radiation by responding to
changes in the solar position, using SMA as an actuator. This section has inspired
from the self-shading effect of tooth-pick cacti that prevents the cacti’s extra heat
gain during the hot season. The second and fourth sections of the prototype are pores
on the exterior and interior surface of the wall. These sections have been emulated
from stomata which has control over the inlet and outlet gasses in the plants. The
third part is the air capsule with PCM that possesses the ventilation function and
controls the inlet air and provides a reduction in indoor temperature.

Based on this approach, two prototypes of bio-skin panels were defined and designed
by using the parametric visual scripting interface of Grasshopper and 3D modeling
Rhino interface. These panels were simulated by using the Honeybee plugin, and
tested for their effectiveness as shading devices; the panel that provided more
shading was selected for further analysis with the integration of the PCM capsule.

61
3.2.2 Analysis of Bio-Skin Performance

The selected prototype is evaluated according to its efficiency for the reduction of
the cooling load of the test unit and increment of comfort sensation by the occupants.
In this regard, the Ladybug and Honeybee plugins are used for the connection of the
grasshopper interface to the open-source energy modeling engine for simulation. The
entire analyses are run based on the hourly optimized position of the shading panels
in each row on the façade.

The analysis is considered in four different scenarios:

i. The initial scenario is the base case where the test unit is simulated without
the implementation of any bio-skin or smart materials.
ii. The second scenario is the case in which only the PCM is embedded in the
east and west wall section of the test unit and called the PCM case
iii. In the third scenario, merely the impact of the shading in the bio-skin is
evaluated; therefore, the considered scenario is the case in which the shading
devices of smart bio-skin are integrated on the west and east wall of the test
unit and is called Shading case
iv. Finally, the impact of the smart bio-skin in the test unit is considered for the
evaluation and named as Smart Bio-skin case in the simulation. In this
scenario just like the previous scenarios, the smart bio-skins are considered
to integrate into the west and east direction of the test unit.

For a valid comparison of the four scenarios, the conditions and the wall’s
construction are considered identical in all cases. Indeed, the wall construction is
defined according to the selected bio-skin, which are panels of concrete holding the
air capsules with PCM. Since the cross-section of the smart bio-skin is complex and
layers of materials do not possess a uniform thickness throughout the entire wall, for
the simulation, the volume of each section in the bio-skin is extracted in the
grasshopper interface and based on the fixed surface area of 16m2, the depth of each
section is calculated. This calculated value was assumed to be fixed for the entire

62
wall section. Figure 3.12 depicts four horizontal cross-sections from the walls of the
above-mentioned scenarios that are converted version of the designed smart bio-skin
system for running the simulation and analysis. Table 3.3, also lists the layers of the
selected materials in the wall section in detail.

Figure 3.12 Cross-Sections of the East and West Walls Showing the Constructional
Compositions for Simulation Purpose, (1). Base Case Scenario, (2). PCM Case
Scenario, (3). Shading Case Scenario, (4).Smart Bio-Skin Case Scenario

Table 3.3 Considered Materials in the Wall Construction with their Properties

Material Thickness (cm) Thermal Density (kg/m3) Specific Heat

Conductivity (J/kg.K)
(W/m.K)
Concrete Panel 4 1.73 2243 837
Air Wall 10 0.60 800 1000
RT35 2 0.20 860 2000
Concrete Panel 4 1.73 2243 837

63
Note: the properties about the concrete panel and air wall are obtained from
Honeybee plugin EP Construction Library, the property of RT35 is obtained from
(PCM RT-LINE)

The workflow of the simulation can be categorized into 7 steps:

1. Geometric definition of the case module in Rhino interface

2. Geometric description of the smart bio-skin in the Grasshopper interface
3. Importing the test unit and shading device which are defined in step 1 and 2
through Honeybee to EnergyPlus
4. Defining the boundary conditions for simulation
5. Running the simulations
6. Recording the data
7. Categorizing the data and deducing the impact of the design
Figure 3.13, presents the workflow of abovementioned steps in Grasshopper

Figure 3.13 Workflow of Analysis for Test Unit, a. Geometric Description of the Smart
Bio-Skin, b. Definition of Materials for HBzone, c. Geometric Definition of the Test Unit,
d. Definition of Simulation Condition, e. Running the Simulation and Data Recoding

64
The initial step is the definition of the case module which is done in the Rhino
interface, the module with the properties listed in sections 3.1.3 “Test Unit” and 3.1.4
“Case Study” are defined in Rhino and with the Grasshopper interface is converted
to the “Brep” usable geometry for simulation. The next step is the description of the
smart bio-skin via the Grasshopper. As the shading panels are defined in
grasshopper, their impacts on the wall are exactly simulated during the analysis. The
shading panels are capable of 90-degree rotation, and the best tilt indicator is the
most minimized solar radiation gain by the walls. For this aim, the genetic algorithm
of the “Galapagos” component was defined in which the fitness function is the
minimized amount of hourly solar radiation gain. In both test unit and the case study,
the positions of the shadings are optimized according to their location in rows and
the orientation of the wall in hourly based. In the test unit, 11 rows of shading are
considered on both the east and west-facing walls, separately, therefore, in total, the
22 shading tilts are defined for the test unit for each hour. Appendix A lists the
optimized angles which are used in the simulation. Figure 3.14, depicts the workflow
for the definition of one row of shading panels in Grasshopper.

Figure 3.14 Workflow for Geometric Definition of One Row of Shading Panels for the
Test Unit

65
In the next stage, for the definition of the model for Honeybee, the “createHBZones”
component was used. The required inputs for this component are “name” the
“zoneProgram” (which is defined as residential)and “HBSurfaces” which are
extracted from the Rhino model. The last step is to specify whether the case module
is conditioned or not and during this simulation, based on whether the analysis is
under room conditions or with space cooling.

At this stage also the materials’ properties that are planned to be used in the cross-
section of the wall that is given in Table 3.3, are defined as new materials and this
definition is linked to the “HB-EP construction” component to prepare the proposed
wall sections for the simulation. Figure 3.15, shows the process required for the
definition of “HBZone”, and Figures 3.16, and 3.17, present the workflow used for
the definition of Materials’ property.

Figure 3.15 Framework for Creation of HBZone

66
 Figure 3.16 Definition of Materials in Honeybee

Considering the definition of the PCM in Honeybee, the variation of the enthalpy of
the RT35 which is based on the temperature variations is defined as strings and
connected to the “additionalStrings” of the Run Energy Simulation component.

Figure 3.17 Definition of PCM in Honeybee

To run the simulations, the defined “HBZone”, weather data as an “epw” file, and
shading as the “HBContext” are connected to “Run Energy Simulation”.
Furthermore, in line with the aimed outcome from the simulation the items of

67
“zoneEnergyUse”, “surfaceTempAnalysis”, and “surfaceEnergyAnalysis” are set as
true from the “EPOutput” component. The considered analysis period is hourly
based, which are recorded and categorized for further analysis and comparison. The
explained process is illustrated in Figure 3.18.

Figure 3.18 Workflow to Run the Simulation

68
 CHAPTER 4

4 RESULT AND DISCUSSION

This chapter covers the outcomes of the bio-inspired design for the façade and
method for the selection of the final prototype for smart bio-skin. Then the result of
the simulation of selected smart bio-skin in the scope of thermal load and comfort
sensation for four scenarios of the test unit is presented. Finally, the effectiveness of
the smart bio-skin in the case study is discussed in the last part of this chapter.

4.1 Design of Smart Bio-Skin

Two different bio-skins were designed according to the inspirations that are gained
from the pinnacles in nature and explained in section 3.2.1., “Design of the Bio-Skin
Prototype”. The function of self-shading of toothpick cactus and permeability of
stomata were the mimicking source for the design. Indeed, the proposed bio-skins
are considered as a series of layers each of which is responsible for specific functions.
For instance, its outer layer is composed of kinetic shading devices that move
according to the solar radiation with SMA wires in their supporting frame. After the
shading devices, the second most outer layer possesses openings which also open
and close with expansion and compression of the SMA wires thus controlling the
inlet air. In the middle of the wall, the air capsule with PCMs is considered for the
extraction of heat from the incoming air to cool it before it enters the indoor space;
consequently, this capsule has the ventilation function. Finally, the inner surface also
with some pores leads the cooler air indoor. The initial step of the design was the
design of shading devices which are named as design proposal 1 and design proposal
2, while in the design proposal 1 merely the function of self-shading of the cacti is
considered as the inspiration source, in the design proposal 2, both form and function
of the cacti are inspiration source for design. Figure 4.1, represents the shading

69
device designed for design proposal 1. Additionally, the shading devices of the
design proposal 2 is presented in Figure 4.2.

Figure 4.1Shading Device of Design Proposal 1 in the Various Positions of According to

the Solar Radiation

Figure 4.2 Shading Device of Design Proposal 2 in the Various Positions According to the
Solar Radiation

70
These designed shading devices are assumed to attach the west and east wall of the
test unit. Besides, the hourly tilt of each row of panels was optimized in the response
to the reduction of solar radiation gain. According to these hourly optimized
positions for the shading device, the amount of solar radiation gain by the wall is
considered as the benchmark for the selection between two design proposals. Indeed,
since the solar heat gain is one of the main causes of overheating in the buildings in
this region, the case that averts the solar heat gain more is selected for further
analysis. Figure 4.3, represents the amount of solar radiation gain in two cases,
monthly.

Nov.
Sep.
Month

July
May
March
Jan.
0 10000 20000 30000 40000 50000 60000 70000 80000
Total Radiation (kWh/m2)

Total Radiation Design Proposal.2 Total Radiation Design Proposal.1

Figure 4.3 Graph of Solar Radiation Gain by Two Design Proposal Shading Devices, on a
Monthly Basis

With the hourly optimum position of the shading devices in the design proposal 1,
the wall beneath the shading gets 683740.2 kWh/m2 solar radiation. However, in
design proposal 2 this value is equal to 635243.5 kWh/m2. Since the main concern
of the design is the reduction of solar heat gain, the design proposal 2 is selected for
further analysis in the selected residential building model.

Details about the layers of the selected design proposal are presented in Figure 4.4.
The shading device having a dimension of 35cm ×70cm and its material is canvas
which is water-proof and elastic enough to tolerate hourly varied position of the

71
panel. This shading device is supporting with a supporting frame whose rotation is
occurred with four NiTi50 wires. In line with the shading device dimension, air
capsules with a dimension of 44cm ×65.5cm were considered beneath the supporting
frame. In the middle of this capsule another capsule for PCM RT-35 was considered.

Figure 4.4 3D Layers of a Selected Design Proposal, (a). Shading Device (Completely
Opened), (b). Shading Device (Completely Closed), (c). Supporting Frame of Shading
Device and Pores of Air Capsule When they are Closed, (d). Air Capsule with PCM

Figure 4.5 and 4.6, shows the arrangement of the panels on the wall in two
situations in which pores of the air capsule are opened and closed.

72
 Figure 4.5 3D Layers of Smart Bio-skin When Pores are Opened

Figure 4.6 3D Layers of Smart Bio-skin When its Pores are Closed

73
4.2 Analysis of Test Unit

As the initial step, the simulation was conducted on the test unit discussed in section
3.1.3 for the evaluation of the effectiveness of the smart bio-skin. It was explained
in section 3.1.5, the SMA wires Ni50Ti50 as the kinetic wires are actuated with joule
heating, whose electrical energy requirements are proposed to be supplied by the PV
panels on the test unit’s roof. Consequently, in the initial step of the analysis, the
effectiveness of the PV panels for meeting the required energy demand for the wires'
bending are considered for evaluation. Furthermore, as mentioned in section 3.2.2,
four simulation scenarios, namely: base case, PCM case, shading case, and smart
bio-skin case, were considered for the simulation and analysis of the impacts of PCM
cooling, kinetic shading devices, and smart bio-skin on the thermal loads, energy
flow, and comfort sensation in the test unit and the case study building. The
following paragraphs cover each of these items separately.

4.2.1 Electrical Energy Produced by Photovoltaic in Test Unit

As explained in Section 3.1.5, the selected SMA wire for the shading device of the
smart bio-skin reacts in response to the changes in solar radiation, so the required
transformation of the wire cannot match with the air temperature shifts; as a result,
the air temperature cannot be an actuation force for required movement of the panels.
For this reason, the considered actuation force for the SMA wires is joule heating.
The required electricity for this heating is assumed to be provided by the photovoltaic
panels integrated into the test unit’s roof. Although the outcome of the test unit's
analysis is not applicable for the case study, due to the incongruity between shade
and shadows in their neighborhood and the shading factor, in this section the
possibility of reliance on the photovoltaics (PV) for actuation of the SMA is
evaluated.

74
The proposed PVs are assumed to be installed on the roof of the test unit. For
optimization of the PV’s efficiency, the optimum tilt angle and altitude of a panel
are calculated in the Ladybug plugin with the “LB-Tilt and Orientation Factor
(TOF)” component. According to this calculation, the optimum tilt and altitude
during the year are 27ᵒ and 180ᵒ respectively for Kish Island. The chart of the solar
radiation as a function of the panel is presented in Figure 4.7.

Figure 4.7 Solar Radiation for Optimized Annuals Tilt and Azimuth of PV Panels in Kish
Island, Calculated with Ladybug Plugin

In line with the calculated values of the tilt and azimuth, a series of PV panels having
dimensions of 35×400cm are assumed to be installed on the roof. At this stage, to
eliminate overshadowing of panels on each other the “Radiation Map” component
of the Diva plugin was used for visualization the incident radiation and obtaining the
quantitative evaluation for the specification of a suitable location for the PV panels
without the overshadowing themselves. According to this analysis, each 27ᵒ tilted
panel should be placed at a distance of 60cm from the next one to gain the maximum
solar radiation of 2208 kWh/m2.

75
In total, based on the required space for each panel, six panels can be attached to
the roof of the test unit. Figure 4.8 presents the tilt and position of PV panels in the
roof of the test unit.

Figure 4.8 South-East View of Test Unit to Illustrate the Considered Position for PV
Panels on the Test Unit

To calculate the amount of electricity generated by the panels, PVs with 15%
efficiency was defined in the energy modeling plugin “ArchSim”. Furthermore,
since all simulations were conducted for the hottest period, i.e. from April to August,
the same duration is considered for the calculation of the produced electrical energy
in joule. Conforming to the calculation, the monthly energy produced by the panels
is presented in Table 4.1 below.

76
Table 4.1 Amount of Monthly Produced Energy by PV Panels in the Test Unit from April
to October, Calculated with ArchSim

Month Produced Energy in Joule

April 8.97E+08
May 8.71E+08
June 8.20E+08
July 8.68E+08
August 8.86E+08
September 8.31E+08
October 8.24E+08

As noted in the literature review (Pesenti , Masera , Fiorito, & Sauchelli, 2015), a
Ni50Ti50 wire with the length of 360mm is actuated in one second with 8 volts and
0.54-ampere current. In the proposed smart bio-skin panels, four Ni50Ti50 wires are
assumed to lead the movement of the shading device of each panel and the length
of each wire is 220mms. Therefore, in line with the values given in the paper ‘Kinetic
solar skin: a responsive folding technique’ (Pesenti , Masera , Fiorito, & Sauchelli,
2015), the resistance of each 220 mm of wire with the consideration of the constant
cross-sectional area of the wire with the same value of electrical current will be:

𝑉 = 𝑅×𝐼

Where V is voltage (Volts-V), R is Resistance (Ohm- 𝜴), and I is Electric Current

(Ampere)

8
8𝑉 = 𝑅 × 0.54𝑎𝑚𝑝𝑒𝑟𝑒 → 𝑅 = = 14.815𝜴
0.54

𝜌𝐿
𝑅=
𝐴

Where R is Resistance (Ohm- 𝜴), 𝜌 is Resistivity coefficient (ohm meters), L is

Length of wire (meter), and A is area (m2)

77
Since the wire type and cross-section are considered constant the resistivity
coefficient (𝜌) with the cross-sectional area of wire (A) is eliminated in the
proportionality for evaluation of new wire’s resistance:

𝑅2 𝐿2 𝑅2[𝛺] 220𝑚𝑚
= → = → 𝑅2 = 9.05𝛺
𝑅1 𝐿1 14.815𝛺 360𝑚𝑚

The equation for the calculation of electrical energy in joules is:

𝑃 = 𝐼2 × 𝑅

Where P is enrgy (joules), I is Electric Current (Ampere), and Resistance (Ohm- 𝜴),

Since the resistance of the wire, based on the considered type of connection between
them, varies; the required electrical energy for both serial and parallel types of array
connections in one panel are given in Table 4.2.

Table 4.2 Required Electrical Energy for Actuation of a Panel for One Series of Movement

Serial Connection Parallel Connection

Resistance of 4𝑤𝑖𝑟𝑒𝑠 × 9.05𝛺 = 36.2𝛺 9.05𝛺
each panel
Energy for 0.542 𝐴 × 36.2𝛺 = 10.55𝐽 0.542 𝐴 × 9.05𝛺 = 2.64𝐽
each panel
Total energy 254 × 10.55𝐽 = 2679.7𝐽 254 × 2.64 = 670.56𝐽
for 254 panels
Number of 5.9966𝑒 + 9𝐽 5.9966𝑒 + 9𝐽
⁄2679.7𝐽 =2.2378e+6 ⁄670.56𝐽 =8.9427e+6
actuation
Required 100
number of
actuation

According to this analysis, the optimized tilted PV panels in the test unit can fulfill
the required actuation force for the rotation of shading devices of smart bio-skin.

78
4.2.2 Thermal Load in Test Unit

As mentioned in 3.2.2, the proposed bio-skin was assumed to have the PCM placed
within the air void between the concrete layers of the wall; and for the sake of
simplification in running the simulations, the total volume of the PCM was assumed
to be distributed evenly within the wall section. This was because the amount of heat
absorbed by the PCM depends on the volume of the material and not the shape. The
simulation of the thermal load was conducted assuming that the test unit is
conditioned to provide the comfort zone for the occupant. For this purpose, during
the simulation, the indoor temperature was kept constant at 25℃. and based on the
outdoor temperature fluctuation the amount of required thermal load for cooling the
indoor and keeping its temperature at the constant value of 25℃ was calculated.
Figure 4.9 presents the impacts of different scenarios on the thermal load
calculations, from April to October.

Oct
Sep
Time (Month)

Aug
July
June
May
April

0.00 10.00 20.00 30.00 40.00 50.00 60.00

Cooling Load (kWh)

Smart Bio-skin Shading PCM Base Case

Figure 4.9 Comparison of Thermal Load of Four Scenarios from April to October,
Calculated with Honeybee Plugin

Based on Figure 4.9, the smart bio-skin is the most successful scenario for the
reduction of the thermal load for cooling the test unit. In general, while the

79
implementation of PCM only in the wall section reduces the thermal load by 8.2%,
adding shading devices results in the reduction of the thermal load by 15%. The
amount of reduction reaches the highest value i.e 21.9%, in the smart bio-skin
scenario, which is based on the integration of kinetic shading devices, and PCM
capsules.

For a better understanding of the scenarios’ effectiveness month by month, a bar

chart and data table for the percentage of effectiveness are depicted in Figure 4.10.

Smart Bio-skin
Scenario

Shading

PCM

0.00 5.00 10.00 15.00 20.00 25.00 30.00

PCM Shading Smart Bio-skin
Oct 7.01 13.08 18.89
Sep 7.92 13.30 20.17
Aug 8.38 13.46 20.82
July 8.47 13.74 21.44
June 8.43 15.78 22.83
May 8.34 17.09 23.83
April 8.99 22.51 28.42

Percentage of effectiveness (%)

Oct Sep Aug July June May April

Figure 4.10 Percentage of Effectiveness in Reduction of Thermal Load of PCM, Shading,

and Smart Bio-skin from April to October

Based on Figure 4.10 and in the precise scale, the shading is most effective during
the spring months (April, May, June); however, PCM shows a slight increment of

80
effectiveness during the summer months (July, August, September). The smart bio-
skin follows the effectiveness trend of the shading scenario, with a difference during
the summer (July, August, September), where the impact of the PCM layer at the
façade leads to a greater reduction of thermal loads in comparison to the merely
shading the façade.

As determined in Figure 3.7, the most number of Cooling Degree Hours of the year
are required in August; and since this month is seen to be the most problematic month
of the year, it is selected for the presentation of the hourly report for the 4 scenarios
concerning their effectiveness for reducing the thermal loads. The rest of the months
are presented in Appendix B.

Table 4.3 Hourly Report of Required Thermal Load for Cooling the Test Unit in Four
Scenarios in August with Percentage of effectiveness of PCM, Shading, and Smart Bio-
skin Scenarios in Comparison to Base Case Scenario for Reduction of Thermal Load

Time - Thermal Thermal Thermal Thermal Efficacy Efficacy Efficacy

Hour Load in Load in Load in Load in of PCM- of of Smart
Base Case PCM Shading Smart % Shading- Bio-skin-
(kWh) Case Case Bio-skin % %
(kWh) (kWh) Case
(kWh)
1 1.91 1.87 1.71 1.64 2.16 10.37 14.19
2 1.78 1.76 1.62 1.58 1.12 8.99 11.58
3 1.68 1.68 1.55 1.53 0.08 7.71 9.07
4 1.58 1.60 1.48 1.48 -1.14 6.59 6.66
5 1.52 1.55 1.43 1.45 -2.43 5.49 4.30
6 1.48 1.54 1.42 1.45 -3.65 4.46 2.19
7 1.45 1.51 1.40 1.44 -4.51 3.71 0.67
8 1.45 1.49 1.39 1.42 -3.26 3.99 1.41
9 1.47 1.45 1.37 1.38 1.30 6.42 6.03
10 1.63 1.50 1.46 1.41 7.57 10.16 13.29
11 1.85 1.62 1.60 1.48 12.56 13.68 20.44
12 2.10 1.77 1.76 1.56 15.54 16.01 25.91
13 2.32 1.93 1.92 1.64 16.73 16.95 29.14
14 2.49 2.08 2.07 1.73 16.57 17.01 30.52
15 2.64 2.21 2.19 1.81 16.20 17.11 31.18
16 2.79 2.34 2.30 1.91 15.91 17.57 31.56

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 Table 4 3. (continued)

17 2.94 2.49 2.41 2.02 15.37 18.13 31.46

18 3.03 2.59 2.47 2.09 14.36 18.61 30.90
19 3.04 2.65 2.48 2.14 12.65 18.36 29.42
20 2.94 2.63 2.43 2.14 10.42 17.31 27.08
21 2.77 2.54 2.33 2.10 8.06 15.79 24.18
22 2.52 2.37 2.16 1.98 6.06 14.38 21.49
23 2.28 2.17 1.98 1.84 4.49 13.03 18.98
24 2.07 2.00 1.82 1.72 3.21 11.68 16.53

Table 4.3. lists hourly required cooling load of the test unit during August. Based on
the base cooling load of the base case scenario, the effectiveness of three scenarios
of PCM, shading, and smart bio-skin were calculated. In general, during August, the
effectiveness of PCM, Shading, and Smart Bio-skin in the reduction of thermal load
of the test unit are 8.4%, 13.5%, and 20.8% respectively.

35 40
EFFECTIVENESS PERCENTAGE (%)

AMBIENT TEMPERATURE (℃)

30
35
25

20 30
15

10 25

5
20
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
-5 15
TIME (HOUR)

PCM Shading Smart Bio-skin Ambient Temperautre

Figure 4.11 Effectiveness of the PCM, Shading, and Smart Bio-Skin in the Reduction of
Case's Thermal Load

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Depending on the ambient temperature, the daily melting and solidification cycle of
the PCM influences the thermal load of the cases in the PCM and smart bio-skin
scenarios. As can be seen in Figure 4.11, the effectiveness of the PCM case for
reduction of the thermal load becomes negative between the 3 to 8 a.m., a duration
in which the ambient temperature dropped below 30℃; consequently, the PCM can
release its latent heat and become solid. This released heat during the night,
negatively affect the thermal load of the test unit, and even in comparison to the base
case, the case with PCM consumes more energy for cooling the environment during
the night. The same negative effect is seen in the smart bio-skin scenario also.
However, because of the shading effect on the surface temperature, the released heat
by the PCM does not negatively impact the cooling loads as does in the PCM case.
During the rest of the day, the smart bio-skin has the most impact on the reduction
of the thermal load, this effectiveness is highest at 4 p.m. when the ambient
temperature is 35.8℃ and PCM is completely melted. As a result, the façade is
capable of reducing the thermal load by 31.56%.

4.2.3 Energy Flow through Walls in Test Unit

In this section, August being the most problematic month of the year is selected for
providing the report about the function of different scenarios of energy flow through
the west and east walls, where the interventions were proposed, and the rest months
are presented in Appendix C. Besides, like the thermal load simulation, at this
simulation also space is considered as conditioned with a constant indoor
temperature of 25℃, to calculate the impact of the outdoor temperature over the
energy flow through the east and west walls.

83
 Figure 4.12 Hourly Energy Flow Through the West Wall of Base Case, PCM Case,
Shading Case, Smart Bio-Skin Case in August

According to Figure 4.12, energy flow in the smart bio-skin in the west direction is
more moderate and is up to 0.04 kWh/m2. Its trend of variation aligns with the
ambient temperature alteration, and in line with the wall orientation, the highest
value of the flow occurs at 3 pm. The effectiveness of the shading in controlling the
energy flow is close to the smart bio-skin. However, after 3 pm until sunrise, the
existence of PCM in the smart bio-skin reduces the energy flow by 14.09% in
comparison to the only shading case. In general, the PCM case, shading case, and
smart bio-skin case reduce the energy flow by almost 18%, 42%, and 50%
respectively.

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Figure 4.13 Hourly Energy Flow Through the East Wall of Base Case, PCM Case, Shading
Case, Smart Bio-Skin Case in August

According to Figure 4.13, regarding energy flow through the east wall, the maximum
energy flow occurs at 9 a.m. due to its orientation. Similar to the west wall, the smart
bio-skin is most effective in controlling the energy flow with variation in the narrow
range of 0- 0.04 kWh/m2. Altogether, the reduction of energy flow with 16.8%,
41.4%, and 49% occurs with the usage of the PCM wall, shading wall, and smart
bio-skin wall separately.

4.2.4 Occupants’ Comfort Sensation in Test Unit

Since the main target of this analysis is the comparison of the façade’s impact in
providing comfort zones, the simulated HB-Zone is assumed unconditioned. Indeed,
the main purpose of this analysis is presenting the effectiveness of the different types
of scenarios in providing the comfort zone without using the mechanical cooling
strategy. Consequently, the effect of the façade type on the indoor ambient

85
temperature and relative humidity are gained from this simulation, and the input
Ladybug add-on is used to prepare the Psychometric Charts of calculated data to
compare how much these scenarios are effective in providing the comfort zone

As in the previous sections, August being the most problematic month of the year is
also selected for presentation in this section, and the rest of the months are listed in
Appendix D. Figure 4.14, 4.15, 4.16 and 4.17 present the psychometric charts of 4
scenarios of the basic case, PCM case, shading case, and smart bio-skin case
respectively.

Figure 4.14 Psychometric Chart of Base Case According to the Hourly Indoor Ambient
Temperature and Relative Humidity at Inside the Test Unit in August, Drawn with
Ladybug Plugin

86
 Figure 4.15 Psychometric Chart of PCM Case According to the Hourly Indoor Ambient
Temperature and Relative Humidity at Inside the Test Unit in August, Drawn with
Ladybug Plugin

Figure 4.16 Psychometric Chart of Shading Case According to the Hourly Indoor Ambient
Temperature and Relative Humidity at Inside the Test Unit in August, Drawn with
Ladybug Plugin

87
 Figure 4.17 Psychometric Chart of Smart Bio-Skin Case According to the Hourly Indoor
Ambient Temperature and Relative Humidity at Inside the Test Unit in August, Drawn
with Ladybug Plugin

According to the psychometric charts presented in Figure 4.14, 4.15, 4.16, and
4.17, Table 4.4 is arranged:

Table 4.4 Limits of Psychometric Processes of Air in Four Scenario of Test Unit

Case Temperature Relative Period Frequent Frequent RH

Range (℃) Humidity Frequency Temp. (℃) (%)
(%) (hr)
Base Case 34-46 30-85 62.4 44 43
62.4 37 60-70
PCM 35-45 30-80 78 39 65
Shading 33-43 35-90 78 36 65
Smart Bio- 34-42 35-85 156 37 60-70
skin

88
Although none of the cases fall within the comfort zone; the boundaries of the
psychometric zones shift for the different façade interventions.

The psychosomatic processes boundary of the ordinary case is 30-85% of relative

humidity and 34-46℃ of operative temperature. This boundary in comparison to the
other scenarios is wider, which means the occupants experience a wider fluctuation.
The two most frequent conditions with the frequency distribution of 62.4 hours in
each case are 44℃ operative temperature with 43% relative humidity and 37℃ with
60-70% relative humidity.

With the use of a PCM layer only in the façade 78 out of 744 hours of August have
the most frequent situation in which relative humidity is 65% and the operative
temperature is equal to 39℃. Therefore, in this scenario, occupants experience a
longer period in a constant situation with a narrower fluctuation.

In contrast, the implementation of shading in the façade restricts the boundary with
relative humidity in the range of 35-90% and the operative temperature between 33-
43℃ which is narrower than previous scenarios. The mode of this scenario has an
operative temperature of 36℃, which is 3℃ cooler than the PCM-Case, with the
relative humidity of 65%.

Finally, the impact of the smart bio-skin in occupants’ sense of comfort is a

psychometric process with a temperature range of 34-42℃ and relative humidity of
35-85%. The most frequent condition that has the 37℃ and 65% of relative humidity
occurs for 156 hours the month.

4.3 Analysis of Study the Case Study Residential Building

As discussed in Section 3.1.4, the considered case study is Damoon Saheli 2 complex
in Kish Island, Iran. The considered section of this complex has 12 villas with a tall
building in the south direction of the villas. Figure 4.18 presents the selected section
for analysis.

89
 Figure 4.18 Selected Section from Damoon Saheli 2 Complex in Kish Island, Iran ( ‫مجتمع‬
‫[ مسکونی دامون ساحلی‬Damoon Saheli Residential Complex], n.d.)

As depicted in Figure 4.19 Damoon Saheli 2 has a total of 12 villas in two rows,
among these twelve cases, the most problematic case based on the villa’s thermal
load is selected for the simulation of the smart bio-skin’s effect. For analyzing the
thermal load of each, the existing neighborhood and the tall building were considered
as the surrounding context.

90
 Figure 4.19 Site Plan and Location of Villa at the Damoon Saheli 2 Complex ( ‫مجتمع مسکونی‬
‫[ دامون ساحلی‬Damoon Saheli Residential Complex], n.d.)

Figure 4.20 shows the most problematic case in the scope of the required thermal
load for the case is Villa 1 with 82.7 kWh of thermal load during a year. Therefore,
the simulation and outcome that will be presented in the rest of this thesis are related
to this case.

84.00
82.00
Thermal Load (kWh)

80.00
78.00
76.00
74.00
72.00
70.00
68.00
Villa Villa Villa Villa Villa Villa Villa Villa Villa Villa Villa Villa
1 2 3 4 5 6 7 8 9 10 11 12
Thermal Load 82.70 75.04 76.22 76.88 74.34 81.95 80.66 74.11 78.96 75.32 75.13 80.20
Case

Figure 4.20 Annual Thermal Load of the Villas in the Damoon Saheli 2, Analyzed with
Honeybee

91
4.3.1 Integration of Smart Bio-Skin in Case Study

In contrast to the test unit, the smart bio-skin panels do not cover the entire façade
of the case. Indeed, the panels are implemented from a height of 1.6m from the floor
reaching up to the beam level at each floor, on the east and west façades of the villa.
Furthermore, the large floor to roof window in the west wall of the villa, which
provides light and air to the staircase, is proposed as completely covered with the
smart bio-skin panels. Figure 4.21 and 4.22 present the considered location of the
panels in the east and west façades of the building separately.

Figure 4.21 South-East View of the Case Study Villa, (a).Without Integration of Smart
Bio-Skin, (b). With Integration of Smart Bio-Skin on the East Wall

Figure 4.22 North-West View of the Case Study Villa, (a).Without Integration of Smart
Bio-Skin, (b). With Integration of Smart Bio-Skin on the West Wall

92
4.3.2 Thermal Load in Case Study Residential Building

According to Figure 3.9, the villa is two stories high, and each storey of the villa is
considered as the separated zone; the data are categorized based on these zones.
Furthermore, like the test unit, the building during the simulation is considered as a
conditioned zone with a constant temperature of 25 ℃. Table 4.5 lists the thermal
load required for the cooling of each zone in every four scenarios separately.

Table 4.5 Required Cooling Load for Each Zone of Case Study and in Four Considered
Scenarios, Analyzed with Honeybee Plugin

Hour GF*- GF- GF- GF- FF**- FF- FF- FF-

Base PCM Shading Smart Base PCM Shading Smart
Case Bio-skin Case Bio-skin
1 5.44 5.36 4.81 4.60 5.38 4.45 3.99 3.82
2 5.37 5.17 4.71 4.58 5.25 4.25 3.87 3.76
3 5.30 5.03 4.65 4.58 5.14 4.09 3.78 3.72
4 5.18 4.88 4.56 4.56 4.97 3.93 3.67 3.67
5 5.03 4.90 4.63 4.69 4.78 3.94 3.72 3.77
6 5.00 5.07 4.84 4.96 4.69 4.10 3.91 4.01
7 8.40 5.08 4.89 5.04 7.55 4.10 3.95 4.07
8 7.53 5.08 4.87 5.00 6.54 4.14 3.97 4.08
9 8.40 4.78 4.48 4.50 7.41 3.97 3.72 3.73
10 8.37 4.85 4.62 4.20 7.71 4.23 4.03 3.67
11 8.45 4.96 4.29 3.95 8.21 4.59 3.96 3.65
12 8.63 5.12 4.30 3.80 8.78 4.98 4.19 3.69
13 8.81 5.24 4.35 3.71 9.25 5.30 4.40 3.75
14 9.35 5.42 4.50 3.77 10.02 5.61 4.65 3.89
15 9.96 5.70 4.73 3.92 10.75 5.92 4.90 4.07
16 10.65 6.11 5.04 4.18 11.31 6.23 5.14 4.26
17 10.87 6.81 5.57 4.67 11.42 6.71 5.49 4.60
18 10.24 7.28 5.93 5.03 10.68 6.94 5.65 4.79
19 9.04 7.59 6.20 5.36 9.32 6.99 5.71 4.93
20 8.84 7.73 6.39 5.64 8.91 6.93 5.73 5.06
21 8.29 7.58 6.39 5.75 8.24 6.69 5.63 5.07
22 7.96 6.95 5.95 5.45 7.80 6.03 5.16 4.73
23 4.94 6.27 5.45 5.08 5.05 5.36 4.66 4.34
24 5.49 6.27 5.53 5.23 5.47 5.36 4.73 4.47

93
*GF: Ground Floor, **FF: First Floor

According to Table 4.5 all three cases of PCM, shading, and smart bio-skin are most
effective for the reduction of the thermal loads during the day from sunrise to sunset,
which can be related to the coverage of the big window in the west side of the villa
that overcomes the negative impact of the window on the heat transfer. The
percentage of effectiveness of the PCM, shading, and smart bio-skin scenarios in the
reduction of the thermal load is presented in Table.4.6. Like the test unit, the required
thermal load for cooling the case study is considered as the base for calculation of
the effectiveness of the other three different scenarios.

Table 4.6 Percentage of Effectiveness of PCM, Shading, and Smart Bio-Skin in Reduction
of Thermal Load of Case Study Villa

Hou PCM- PCM- Shading- Shading- Smart Bio-skin, Smart Bio-skin,

r GF* FF** GF FF GF FF

1 1.41 17.31 11.64 25.88 15.4 29.04

2 3.61 19.16 12.28 26.42 14.77 28.52
3 4.97 20.38 12.29 26.51 13.59 27.6
4 5.7 20.87 11.92 26.09 11.98 26.15
5 2.7 17.61 8.04 22.13 6.88 21.15
6 -1.42 12.69 3.11 16.58 0.8 14.6
7 39.59 45.69 41.83 47.7 39.99 46.05
8 32.6 36.73 35.28 39.26 33.54 37.62
9 43.04 46.35 46.69 49.79 46.47 49.59
10 42.13 45.08 44.87 47.68 49.82 52.38
11 41.23 44.05 49.28 51.71 53.24 55.49
12 40.64 43.25 50.14 52.34 56.02 57.96
13 40.51 42.76 50.6 52.47 57.85 59.44
14 42.03 44.05 51.9 53.57 59.72 61.13
15 42.74 44.97 52.54 54.38 60.6 62.13
16 42.58 44.91 52.67 54.58 60.71 62.3
17 37.37 41.24 48.73 51.9 57.07 59.73
18 28.88 35.04 42.11 47.13 50.86 55.12
19 16.03 25.03 31.44 38.79 40.73 47.08

94
 Table 4 6. (continued)

20 12.55 22.16 27.69 35.64 36.23 43.24

21 8.51 18.8 22.96 31.62 30.63 38.43
22 12.8 22.67 25.34 33.79 31.54 39.29
23 -26.97 -6.06 -10.43 7.76 -2.87 14.08
24 -14.04 2.14 -0.73 13.57 4.81 18.32
*GF: Ground Floor, **FF: First Floor

Table 4.5 shows that all three cases are most effective for the upper floor, and in
general, the PCM is effective for the reduction of the thermal loads by 24.95% and
32.4% downstairs and upstairs respectively. Besides, shading reduces the thermal
load on the upper floor by 41.2% and this decrease in the lower floor is equal to
34.4%. Finally, the impact of the smart bio-skin for reduction of the thermal loads
downstairs and upstairs is 39.5% and 46. %, respectively.

4.3.3 Occupants’ Comfort Sensation in Case Study Residential Building

Similar to the test unit analysis, the case study was not conditioned during the
simulation. Consequently, the impact of different scenarios in the thermal
conductivity and fluctuation of indoor temperature in response to outdoor
temperature is subjected to analysis. The following graphs (Figure 4.23 to 4.26)
present the indoor conditions according to the ambient temperature and relative
humidity at the different scenarios separately.

95
Figure 4.23 Psychometric Chart of Villa without the Integration of Panels in August,
Drawn with Ladybug Plugin

Figure 4.24 Psychometric Chart of Villa with the Integration of PCM in West and East
Walls in August, Drawn with Ladybug Plugin

96
Figure 4.25 Psychometric Chart of Villa with the Integration of Shading in West and East
Walls in August, Drawn with Ladybug Plugin

Figure 4.26 Psychometric Chart of Villa with the Integration of Smart Bio-Skin in West
and East Walls in August, Drawn with Ladybug Plugin

97
Similar to the test unit none of the scenarios provided comfort conditions indoor.
However, smart bio-skin is successful in providing longer and more bearable
conditions. In accordance with the psychometric charts (Figure 4.23 to 4.26), Table
4.7 is listed to present more details about the effectiveness of the different scenarios
in controlling the indoor temperature and relative humidity.

Table 4.7 Limits of Psychometric Processes of Air in Four Scenarios of Case Study Villa

Case Temperature Relative Period Frequent Frequent

Range (℃) Humidity (%) Frequency (hr) Temp. (℃) RH (%)
NI 37-49 35-75 140 40 50
140 43 55
PCM 36-46 25-70 140 38 60
Shading 31-42 40-90 126 33 75
Smart Bio- 31-42 40-90 280 34 65-75
skin

The villa without the integration of the specific type of panels, experiences the two
most frequents conditions, with the frequency distribution of 140 hours in each, in
which the temperatures are of 40℃ and 43℃ and the relative humidity is 50% and
55% respectively. The implementation of PCM in the wall causes this trend to
decline by 2℃ and the frequent condition has a temperature of 38℃ with 60%
relative humidity. The shading panels are more effective than the PCMs and reduce
the ambient temperature by 5℃; however, the frequency of this condition is fewer
and is equal to 126 hours in August. Finally, the smart bio-skin panels in comparison
to the shading panels, provide the most common condition inside, in which the
frequency of the condition is 280 hours in the month with 34 ℃ temperature and
relative humidity of 65-75%.

98
 CHAPTER 5

5 CONCLUSION

Two broad fields of smart materials and biomimicry have been studied in the various
scientific fields in the last few decades, and the field of architecture is one of them.
Indeed, biomimicry is mostly considered as the inspiration or mimicking natural
phenomena in architecture for dealing with the existing problem in the building
sectors; as an example, biomimicry is used for the design of building façade to
enhance the function of façade in buildings. Besides, some type of smart materials
based on their inherent features are suitable for usage in the building and handling
the variation occurred in the environment for tolerating their impact on the
occupants’ comfort. In this thesis, the main concern was studying the possibility of
the integration of these two fields together for the design of a smart bio-skin as the
building façade. Therefore, the publications in which results of experiments or
research for bio-skin and use of smart materials in the façade were considered as the
design and simulation reference.

For the design of bio-skin, the self-shading effect of the cactus for reduction of solar
heat gain, and stomata’s permeability were selected for the design of bio-skin in the
hot-humid region of the Kish Island, Iran; to control the heat gain and infiltrated air
through the wall. Furthermore, based on the outcomes of the various researches about
the different types of phase change materials and shape-memory materials, PCM-
RT35 and SMA-Ni50Ti50 that matched the requirement of the design circumstance
are selected for integration with the bio-inspired design ideas to enhance the function
of the designed bio-skin from the point of view of energy consumption and comfort
provision.

The impact of smart materials and the bio-inspired design were considered for
evaluation in three different scenarios. One of them is the evaluation of the RT-35

99
impact only. Another scenario only considered the shading impact, and the last
scenario dealt with the impact of the two together in the proposed smart bio-skin. All
listed scenarios were compared with the ordinary condition with no shading or
integration of PCM in the wall to show the effectiveness of each scenario.

In general, since the selection criteria for RT-35 were defined based on the conditions
provided by the designed smart bio-skin, which is 5 to 6 degrees below the outdoor
ambient temperature range, the presented function by the PCM scenario in the
simulation is less the reaction presented by the other organic PCMs in experimental
researches. Also, the close difference between the day and night temperature in Kish
Island causes the stored heat during the day to provide extra load for the building
during the night. All in all, the implementation of the RT-35 in the façade for this
region is effective during daytimes; however, during the night hours, the function of
the RT-35 is not as same as is assumed. Indeed, this simulation emphasizes the
importance of the accurate selection of the PCM and the impact of the region’s
climate, as the incorrect selection of the PCM in the scope of its melting temperature
according to its implemented place or the place with a close-range of temperature
during a day leads to the reduction of expected efficiency of the PCM.

As mentioned in Chapter 4, the most effective type of façade for controlling the
thermal load and provision of indoor comfort in the hot and humid climate of Kish
Island is the smart bio-skin by reducing the thermal load in the case study villa by
43%. The second most effective type of the façade is with the shading devices and
the last one is the integration of PCM in the wall section. The shading device as the
adapted solution from nature is considerably effective in the reduction of the thermal
load, energy flow and provision of the comfort zone. Besides, although the the PCM
wall does not present the optimum function of RT-35, it is effective during the
daytime.

Also, due to the close day and night temperature values in this region, the PCM
cannot react optimally during the night hours. For this reason, the hourly
effectiveness of the cases contains the PCM in the façade in comparison to the only

100
shading scenario, does not present the constant optimum function. Indeed, in the
PCM only case due to the cyclic reaction of heat gain and loss by the PCM, heat
gained during the daytime is released to indoor that causes the effectiveness of the
PCM for the reduction of heat gain to become negative during the night. The same
logic is applicable to the performance of the smart bio-skin for the reduction of
thermal load in comparison to the shaded façade also. While during the day there are
significant differences between the reduced thermal load of the spaces with smart
bio-skin and the shaded walls, this difference gets close during the night hours, which
is also related to the cyclic heat released by the PCM in the façade. This result is
specified for this region and RT-35. If the difference of day and night temperature
was higher than the existing condition, during the night the smart bio-skin and PCM
would perform as effective as they do during the day. Furthermore, there is the
assumption that by using a PCM with a narrower phase transition temperature, the
duration in which the indoor gets the heat released by the PCM would be longer, and
there would be the extra thermal load for the provision of indoor comfort condition.

In the provision of the comfort zone, none of the scenarios can be effective alone,
and the additional cooling source is required. However, the effectiveness of the smart
bio-skin in the reduction of the unbearable condition of indoor is also higher than the
other types of façade. This higher performance is shown either by the longer period
of constant conditions or with lower temperature. Indeed, in April and October in
which the outdoor temperature is not as high as June, July, May, August, and
September, there is no considerable reduction of the temperature with the integration
of smart bio-skin in the façade. However, the occupants experience a longer constant
condition with fewer fluctuations. During the summer in which the outdoor
temperature is high, the smart bio-skin effectively reduced the indoor temperature
by 3℃,4℃, 4℃ respectively in July, August, and September, and the indoor
condition gets less severe.

A point that should be highlighted is in the analysis and evaluation of the smart bio-
skin's effectiveness, the impact of ventilation considered by designed pores on the
exterior and interior surface of the wall is neglected; therefore, there is the

101
assumption that the simulated performance is below the possible real impact of the
smart bio-skin.

From the point of view of the appropriateness of Ni50Ti50 for movement of the
panels, since the required variation was defined in response to the solar radiation, the
joule heating with the photovoltaics was considered as the actuation force of the
wires. The low required force for the actuation of the SMA wire during the analysis
period was supported by the PV panels of the test unit; however, the efficacy of the
same system should be evaluated according to the required energy by SMA and
installation location of the PV panels.

102
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 APPENDICES

A. Appendix A: Optimized Angles of Shading Panels' Rows in the Test-Unit

Table A.1 Optimum Angle of Panels' Row of Test Unit in April

Hour Side R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11

East 36 28 27 27 35 13 27 29 36 31 36
5
West 26 15 29 41 28 16 33 17 19 20 25
East 36 28 27 27 35 13 27 25 27 22 29
6
West 26 15 29 41 28 16 33 17 19 20 25
East 37 26 26 25 32 13 29 14 19 22 29
7
West 26 21 29 48 26 16 33 25 19 25 30
East 37 22 22 25 27 13 29 14 19 22 29
8
West 25 21 29 48 26 18 33 25 27 32 40
East 30 22 22 35 27 13 29 14 19 49 20
9
West 25 21 29 32 26 18 33 12 27 37 42
East 30 22 22 46 27 15 26 14 26 49 20
10
West 25 21 29 32 26 18 33 12 27 37 33
East 36 27 17 31 23 15 26 13 25 51 37
11
West 35 13 29 32 26 15 34 12 36 37 33
East 36 27 17 31 23 15 26 13 25 51 37
12
West 35 13 29 32 26 15 34 12 36 37 33
East 36 27 18 32 25 12 26 13 25 52 33
13
West 33 13 22 28 30 15 29 12 39 35 30
East 36 27 18 32 25 12 26 13 25 34 33
14
West 33 13 22 28 30 15 29 12 39 35 30
East 35 30 25 32 18 16 28 15 27 34 33
15
West 33 13 29 25 34 15 31 15 36 29 29
East 35 29 25 30 18 16 28 17 30 34 33
16
West 18 13 29 25 34 15 31 15 36 29 29
East 35 29 25 30 18 16 28 17 30 34 33
17
West 18 13 29 25 34 15 31 15 36 29 29
East 31 29 28 30 18 16 28 17 30 34 33
18
West 18 13 29 13 34 10 29 18 32 26 27

113
 Table A.2 Optimum Angle of Panels' Row of Test Unit in May

May Row
Side
Hour R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11
East 29 28 29 14 21 14 27 18 29 37 17
5
West 34 37 12 37 38 15 25 30 30 33 33
East 29 22 21 16 25 14 26 18 29 30 24
6
West 38 37 28 37 29 27 27 35 30 29 33
East 29 22 21 16 26 14 20 13 29 30 24
7
West 27 37 28 37 29 27 27 35 30 29 33
East 30 21 21 16 26 14 20 13 29 30 24
8
West 27 37 28 37 29 27 28 35 30 29 33
East 36 45 24 30 34 14 33 13 29 30 31
9
West 27 37 22 42 29 27 31 28 34 35 44
East 36 45 38 33 34 14 33 13 29 30 31
10
West 34 37 22 42 16 27 33 14 34 20 35
East 28 23 38 47 34 22 32 13 36 23 31
11
West 34 42 37 36 16 10 33 14 17 20 35
East 28 23 38 47 34 22 32 20 36 23 31
12
West 34 42 37 36 16 10 33 14 17 20 35
East 28 23 38 47 34 29 40 28 36 23 31
13
West 34 42 37 36 16 10 33 14 17 20 35
East 33 23 38 47 34 32 40 28 36 31 26
14
West 34 22 30 36 16 13 31 17 22 30 26
East 33 27 38 40 34 32 40 28 36 31 26
15
West 34 22 30 29 10 13 31 17 25 13 26
East 38 31 39 26 28 8 36 22 32 34 37
16
West 18 13 30 13 32 12 27 14 32 13 26
East 38 31 39 26 28 8 36 22 32 34 37
17
West 18 13 30 13 32 12 27 14 32 13 26
East 35 31 39 26 28 8 36 22 32 34 37
18
West 18 13 30 13 35 12 27 16 32 13 26
East 30 30 39 26 19 8 28 17 32 32 31
19
West 18 13 29 13 35 12 27 18 32 22 26

114
 Table A.3 Optimum Angle of Panels' Row of Test Unit in June

June Row
Side
Hour R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11
East 29 20 23 23 18 14 23 19 29 31 25
5
West 38 32 23 40 35 14 27 31 22 33 31
East 31 20 26 19 17 10 23 17 25 41 24
6
West 38 32 23 40 31 14 27 31 29 33 24
East 31 20 26 19 17 10 23 17 25 41 24
7
West 32 41 23 40 31 14 27 31 29 33 24
East 33 20 33 19 26 10 29 23 25 31 24
8
West 32 41 23 42 31 32 27 31 35 33 24
East 33 13 35 29 29 15 29 23 32 31 33
9
West 32 40 23 46 31 32 28 37 40 29 42
East 33 13 35 29 29 15 29 23 32 31 33
10
West 34 40 23 46 23 32 28 37 40 29 42
East 33 13 35 29 29 15 29 24 28 26 33
11
West 34 37 11 46 23 32 28 37 40 29 42
East 36 15 35 29 28 15 32 24 39 26 34
12
West 34 37 11 46 23 32 28 37 40 26 36
East 47 29 30 30 27 16 36 22 39 26 31
13
West 32 30 14 39 35 14 28 35 35 26 30
East 47 26 30 30 38 16 39 22 38 27 31
14
West 32 30 14 39 35 14 29 30 35 26 30
East 47 26 30 30 38 31 40 27 38 38 31
15
West 32 22 25 49 37 14 29 14 35 29 30
East 37 26 30 30 38 31 40 30 38 38 31
16
West 36 22 32 49 37 14 29 14 35 29 30
East 37 22 22 28 32 28 38 43 39 34 24
17
West 36 22 32 39 23 14 29 14 23 15 33
East 37 22 32 26 27 27 36 41 40 29 26
18
West 36 22 26 39 23 14 34 14 23 15 33
East 37 22 32 26 27 26 36 41 40 29 26
19
West 36 29 26 39 23 14 34 14 23 15 33

115
 Table A.4 Optimum Angle of Panels' Row of Test Unit in July

July Row
Side
Hour R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11
East 35 27 41 28 36 14 33 12 27 15 34
5
West 27 27 31 28 34 20 33 34 27 19 36
East 34 20 30 15 42 15 28 12 27 15 34
6
West 27 27 31 28 34 26 19 27 37 18 32
East 29 20 30 15 42 16 28 12 28 15 35
7
West 27 27 31 28 34 26 14 25 37 18 30
East 29 20 30 15 42 12 29 12 28 21 37
8
West 27 27 36 28 29 26 14 12 37 18 28
East 32 30 34 32 22 12 32 19 36 32 27
9
West 37 15 36 35 27 16 33 12 20 30 28
East 32 30 34 32 22 12 32 19 36 32 27
10
West 37 15 36 35 27 16 33 12 20 30 28
East 32 40 26 32 21 14 34 23 36 28 27
11
West 37 15 36 35 27 16 33 12 20 30 28
East 32 40 26 32 20 14 31 33 33 19 27
12
West 33 12 37 31 35 15 34 13 20 22 30
East 32 39 27 32 20 14 31 33 33 19 23
13
West 33 12 37 31 35 15 34 13 20 22 30
East 32 39 27 32 20 14 31 33 33 19 23
14
West 33 12 37 31 35 15 34 13 20 22 30
East 35 39 31 35 24 21 35 33 33 19 26
15
West 33 12 37 31 32 15 35 13 20 19 26
East 35 31 31 37 27 21 36 33 33 19 28
16
West 34 12 36 27 32 15 35 13 20 13 26
East 35 24 31 37 27 23 36 47 33 19 28
17
West 34 12 36 27 32 15 35 13 20 13 26
East 37 22 31 26 27 26 36 49 33 22 27
18
West 34 26 30 41 23 14 34 9 20 13 33
East 37 22 31 26 27 26 36 49 40 29 26
19
West 34 26 30 41 23 14 34 9 20 13 33

116
 Table A.5 Optimum Angle of Panels' Row of Test Unit in August

August Row
Side
Hour R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11
East 36 15 30 34 33 13 33 14 30 31 34
5
West 27 26 35 39 23 27 37 34 21 19 30
East 36 15 30 31 33 13 33 20 28 31 34
6
West 16 25 22 39 23 27 37 34 21 18 30
East 36 15 29 27 33 13 33 20 27 31 33
7
West 17 25 22 35 23 27 37 34 21 15 31
East 40 15 29 15 37 13 37 18 27 33 33
8
West 17 25 22 35 26 30 37 25 21 14 31
East 38 15 29 15 37 14 37 18 27 33 33
9
West 17 25 33 35 26 13 37 25 20 14 31
East 37 15 28 15 36 14 36 18 27 31 32
10
West 29 26 33 35 26 13 37 25 20 14 31
East 37 15 28 15 36 14 36 18 27 31 32
11
West 29 26 33 30 21 14 31 27 20 15 28
East 37 15 28 15 36 14 36 18 27 31 32
12
West 29 26 33 30 21 14 31 27 20 15 28
East 37 15 28 15 36 14 36 16 30 33 34
13
West 34 25 33 29 21 14 30 27 20 15 28
East 37 15 28 15 36 14 36 16 30 33 34
14
West 34 25 33 29 21 14 30 27 20 15 28
East 37 15 25 15 36 14 36 16 30 33 34
15
West 34 25 33 29 21 14 30 26 20 15 28
East 39 14 25 15 36 14 36 16 30 33 31
16
West 37 21 36 29 21 13 28 23 20 18 24
East 39 14 25 15 36 14 36 16 30 33 31
17
West 37 21 36 29 21 13 28 23 20 18 24
East 36 13 25 15 36 15 36 16 29 32 31
18
West 37 21 36 29 21 15 24 21 20 18 24
East 36 13 25 15 35 15 36 15 28 32 31
19
West 30 13 37 23 25 15 22 21 19 18 24

117
 Table A.6 Optimum Angle of Panels' Row of Test Unit in September

September Row
Side
Hour R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11
East 36 28 17 26 24 14 36 27 30 26 28
6
West 33 26 20 26 27 42 21 30 33 35 26
East 19 26 17 29 33 13 36 27 24 28 35
7 West 33 13 20 41 29 37 21 37 35 29 26
East 19 13 17 42 33 13 36 27 24 46 35
8
West 33 13 19 41 30 37 24 37 35 29 26
East 19 13 17 47 34 13 29 28 24 46 35
9
West 33 13 19 41 30 37 24 37 35 29 28
East 19 11 17 50 35 12 29 28 24 46 35
10
West 34 13 19 41 30 37 26 39 35 29 30
East 19 9 17 56 35 11 29 32 20 43 37
11
West 34 13 24 41 35 37 26 39 40 34 30
East 30 44 32 23 52 11 47 28 38 34 31
12
West 32 37 34 40 35 14 34 39 11 39 36
East 30 45 32 23 52 11 47 28 38 34 31
13
West 29 37 34 40 30 14 34 35 11 40 36
East 30 45 32 23 52 11 47 28 38 34 31
14
West 29 37 34 40 30 14 34 35 11 40 37
East 34 14 25 15 35 15 36 15 28 33 31
15
West 29 13 36 22 27 14 20 20 21 15 23
East 34 14 25 15 35 15 36 15 28 33 31
16
West 29 13 36 22 27 15 20 20 21 15 23
East 36 13 25 15 35 15 36 15 28 32 31
17
West 29 13 36 22 27 15 22 21 19 18 24
East 36 13 25 15 35 15 36 15 28 32 31
18
West 29 13 36 22 27 15 22 21 19 18 24

118
 Table A.7 Optimum Angle of Panels' Row of Test Unit in October

October Row
Side
Hour R1 R2 R3 R4 R5 R6 R7 R8 R9 R10 R11
East 36 28 17 13 21 10 37 8 24 10 24
6
West 32 24 20 26 25 42 21 30 31 35 26
East 36 28 18 13 21 10 37 8 24 10 24
7
West 32 32 28 32 25 43 26 30 31 35 26
East 34 13 25 13 24 10 37 8 24 28 27
8
West 32 32 28 32 25 32 26 28 33 39 49
East 32 13 30 13 24 10 36 8 24 41 27
9
West 37 32 28 32 36 32 26 28 35 39 32
East 32 13 30 13 24 10 36 8 24 41 27
10
West 37 42 30 37 36 32 34 28 35 39 32
East 32 21 30 30 24 14 33 13 25 38 33
11
West 29 42 30 37 36 26 33 28 29 39 32
East 32 19 25 31 21 14 33 13 25 38 33
12
West 29 42 35 37 36 26 33 28 29 43 32
East 34 15 15 43 21 28 33 29 25 35 33
13
West 35 42 35 36 24 16 36 28 29 43 41
East 34 15 10 40 19 28 28 28 25 30 30
14
West 35 42 32 35 22 15 35 28 24 40 34
East 34 15 10 40 19 28 28 28 25 30 30
15
West 35 42 32 35 22 15 35 28 24 40 34
East 32 15 10 40 20 27 28 28 25 30 30
16
West 35 32 32 20 17 15 32 36 24 40 34
East 32 22 12 40 20 27 25 25 25 26 25
17
West 35 32 32 20 17 15 32 36 20 22 31
East 32 25 14 40 17 27 25 25 25 26 25
18
West 23 32 32 40 30 13 28 36 20 22 31

119
120
B. Appendix B: Thermal Load of Test Unit

Table B.8 Hourly Report of Required Thermal Load for Cooling the Test Unit in Four
Scenarios in April

Time Base Case PCM Shading Smart Bio-skin

1 0.70 0.80 0.58 0.62
2 0.59 0.71 0.50 0.56
3 0.51 0.62 0.44 0.50
4 0.45 0.55 0.40 0.45
5 0.43 0.53 0.39 0.44
6 0.42 0.51 0.38 0.44
7 0.38 0.47 0.36 0.41
8 0.38 0.45 0.36 0.40
9 0.47 0.49 0.40 0.43
10 0.60 0.57 0.47 0.48
11 0.81 0.71 0.60 0.58
12 1.01 0.83 0.73 0.67
13 1.20 0.97 0.87 0.76
14 1.36 1.08 1.00 0.84
15 1.50 1.17 1.11 0.90
16 1.64 1.26 1.21 0.96
17 1.78 1.37 1.31 1.04
18 1.87 1.47 1.38 1.11
19 1.87 1.54 1.39 1.16
20 1.76 1.53 1.33 1.15
21 1.59 1.46 1.23 1.12
22 1.35 1.30 1.06 1.00
23 1.10 1.12 0.88 0.87
24 0.90 0.97 0.73 0.76

121
Table B.9 Hourly Report of Required Thermal Load for Cooling the Test Unit in Four
Scenarios in May

Time Base Case PCM Shading Smart Bio-skin

1 1.28 1.34 1.11 1.15
2 1.15 1.25 1.02 1.09
3 1.05 1.19 0.94 1.04
4 0.94 1.12 0.86 0.98
5 0.87 1.05 0.81 0.93
6 0.86 1.03 0.81 0.93
7 0.84 0.99 0.79 0.90
8 0.86 0.96 0.81 0.88
9 0.91 0.94 0.82 0.85
10 1.06 0.98 0.90 0.88
11 1.28 1.09 1.04 0.96
12 1.53 1.24 1.21 1.05
13 1.75 1.38 1.38 1.10
14 1.93 1.51 1.53 1.23
15 2.09 1.62 1.66 1.30
16 2.24 1.75 1.78 1.38
17 2.39 1.90 1.89 1.48
18 2.47 2.02 1.90 1.56
19 2.46 2.08 1.96 1.61
20 2.35 2.06 1.89 1.62
21 2.16 1.97 1.78 1.58
22 1.91 1.80 1.60 1.47
23 1.67 1.63 1.41 1.35
24 1.46 1.47 1.25 1.25

122
Table B.10 Hourly Report of Required Thermal Load for Cooling the Test Unit in Four
Scenarios in June

Time Base Case PCM Shading Smart Bio-skin

1 1.63 1.62 1.43 1.40
2 1.48 1.52 1.33 1.34
3 1.36 1.43 1.24 1.28
4 1.26 1.35 1.16 1.22
5 1.19 1.31 1.11 1.20
6 1.16 1.29 1.09 1.19
7 1.13 1.27 1.08 1.17
8 1.14 1.24 1.08 1.14
9 1.18 1.20 1.07 1.11
10 1.32 1.24 1.15 1.14
11 1.53 1.33 1.27 1.19
12 1.74 1.46 1.41 1.26
13 1.94 1.59 1.54 1.33
14 2.10 1.71 1.68 1.40
15 2.26 1.83 1.81 1.48
16 2.43 1.97 1.94 1.56
17 2.61 2.13 2.07 1.67
18 2.73 2.27 2.17 1.76
19 2.77 2.35 2.21 1.83
20 2.70 2.37 2.19 1.86
21 2.53 2.29 2.09 1.83
22 2.29 2.13 1.93 1.73
23 2.04 1.94 1.74 1.61
24 1.81 1.77 1.57 1.50

123
Table B.11 Hourly Report of Required Thermal Load for Cooling the Test Unit in Four
Scenarios in July

Time Base-Case PCM Shading Smart Bio-skin

1 1.89 1.84 1.69 1.61
2 1.74 1.73 1.58 1.54
3 1.66 1.65 1.53 1.50
4 1.55 1.57 1.45 1.45
5 1.47 1.52 1.39 1.42
6 1.47 1.52 1.40 1.43
7 1.43 1.49 1.37 1.42
8 1.41 1.47 1.35 1.40
9 1.46 1.44 1.37 1.37
10 1.59 1.47 1.43 1.38
11 1.79 1.57 1.55 1.43
12 2.03 1.71 1.71 1.51
13 2.23 1.85 1.84 1.43
14 2.37 1.98 1.95 1.65
15 2.55 2.13 2.10 1.74
16 2.69 2.26 2.20 1.83
17 2.83 2.40 2.30 1.93
18 2.99 2.54 2.42 2.03
19 3.00 2.60 2.44 2.09
20 2.90 2.59 2.38 2.09
21 2.77 2.53 2.33 2.07
22 2.51 2.35 2.14 1.95
23 2.25 2.15 1.95 1.81
24 2.07 1.99 1.82 1.71

124
Table B.12 Hourly Report of Required Thermal Load for Cooling the Test Unit in Four
Scenarios in September

Hour Base Case PCM Shading Smart Bio-skin

1 1.72 1.71 1.55 1.52
2 1.60 1.61 1.46 1.46
3 1.49 1.53 1.38 1.40
4 1.39 1.46 1.30 1.35
5 1.33 1.42 1.26 1.32
6 1.31 1.41 1.25 1.33
7 1.27 1.38 1.23 1.31
8 1.26 1.36 1.22 1.29
9 1.30 1.33 1.22 1.26
10 1.48 1.40 1.34 1.32
11 1.74 1.53 1.51 1.41
12 2.01 1.69 1.70 1.50
13 2.25 1.86 1.88 1.60
14 2.45 2.02 2.07 1.70
15 2.62 2.15 2.17 1.78
16 2.76 2.28 2.27 1.86
17 2.89 2.41 2.36 1.95
18 2.95 2.51 2.40 2.01
19 2.91 2.54 2.38 2.05
20 2.77 2.50 2.31 2.04
21 2.58 2.40 2.19 1.99
22 2.33 2.21 2.01 1.86
23 2.08 2.01 1.82 1.72
24 1.87 1.83 1.66 1.60

125
Table B.13 Hourly Report of Required Thermal Load for Cooling the Test Unit in Four
Scenarios in October

Hour Base Case PCM Shading Smart Bio-skin

1 1.33 1.38 1.20 1.22

2 1.22 1.31 1.12 1.17

3 1.13 1.24 1.04 1.11

4 1.04 1.17 0.98 1.06

5 0.99 1.13 0.94 1.03

6 0.96 1.10 0.93 1.02

7 0.93 1.07 0.90 0.99

8 0.94 1.05 0.91 0.99

9 0.96 1.03 0.91 0.96

10 1.14 1.11 1.03 1.04

11 1.41 1.27 1.24 1.17

12 1.68 1.42 1.43 1.29

13 1.93 1.58 1.63 1.40

14 2.15 1.74 1.81 1.52

15 2.30 1.85 1.93 1.59

16 2.42 1.95 2.01 1.64

17 2.51 2.05 2.06 1.69

18 2.51 2.10 2.05 1.71

19 2.44 2.12 2.01 1.73

20 2.29 2.07 1.92 1.71

21 2.11 1.96 1.80 1.65

22 1.88 1.80 1.63 1.53

23 1.66 1.63 1.45 1.40

24 1.47 1.48 1.30 1.30

126
 90 40
80
EFFECTIVENESS PERCENTAGE (%) 35

AMBIENT TEMPERATURE (℃)

70
60 30

50 25
40
20
30
20 15
10 10
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 5
-10
-20 0
TIME (HOUR)

PCM Shading Smart Bio-skin Operative Temperature

Figure B.1 Effectiveness of PCM, Shading, & Smart Bio-Skin Scenarios in Reduction of
Thermal Load of Test Unit in April

100 40

80 35

AMBIENT TEMPERATURE (℃)

EFFECTIVE PERCENTAGE (%)

30
60
25
40
20
20
15
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 10

-20 5

-40 0
TIME (HOUR)

PCM Shading Smart Bio-skin Ambient Temperature

Figure B.2 Effectiveness of PCM, Shading, & Smart Bio-Skin Scenarios in Reduction of
Thermal Load of Test Unit in May

127
 40 40
EFFECTIVENESS PERCENTAGE (%)

35
30

AMBIENT TEMPERATURE (℃)

30
20
25

10 20

15
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
10
-10
5

-20 0
TIME (HOUR)

PCM Shading Smart Bio-skin Ambient Temperature

Figure B.3 Effectiveness of PCM, Shading, & Smart Bio-Skin Scenarios in Reduction of
Thermal Load of Test Unit in June

40 40
EFFECTIVENESS PERCENTAGE (%)

35
30 AMBIENT TEMPERATURE (℃)
30
20
25

10 20

15
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 10
-10
5

-20 0
TIME (HOUR)

PCM Shading Smart Bio-skin Ambient Temperature

Figure B.4 Effectiveness of PCM, Shading, & Smart Bio-Skin Scenarios in Reduction of
Thermal Load of Test Unit in July

128
 100 35

EFFECTIVENESS PERCENTAGE (%) 30

AMBIENT TEMPERATURE (℃)

80

25
60
20
40
15
20
10

0 5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

-20 0
TIME (HOUR)

PCM Shading Smart Bio-skin Ambient Temperature

Figure B.5 Effectiveness of PCM, Shading, & Smart Bio-Skin Scenarios in Reduction of
Thermal Load of Test Unit in September

100 35
EFFECTIVENESS PERCENTAGE (%)

30

AMBIENT TEMPERATURE (℃)

80

25
60
20
40
15
20
10

0 5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

-20 0
TIME (HOUR)

PCM Shading Smart Bio-skin Ambient Temperature

Figure B.6 Effectiveness of PCM, Shading, & Smart Bio-Skin Scenarios in Reduction of
Thermal Load of Test Unit in October

129
130
C. Appendix C: Energy Flow Through the East and West Walls of Each
Scenario from April to October

0.12 40

0.1 35

Ambient Temperature (℃)

Energy Flow (kWh/m2)

30
0.08
25
0.06
20
0.04
15
0.02
10
0 5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
-0.02 0
Time (Hour)

Base Case Shading PCM Smart Bio-skin Operative Temperature

Figure C.7 Energy Flow Through the East Wall in April

0.12 40
0.1 35
0.08
Energy Flow (kWh/m2)

Ambient Temperature
30
0.06
25
0.04
20
0.02
15
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 10
-0.02
-0.04 5

-0.06 0
Time (Hour)

Base Case Shading PCM Smart Bio-skin Operative Temperature

Figure C.8 Energy Flow Through the West Wall in April

131
 0.12 40

0.1 35

Ambiant Temperature (℃)

0.08 30
Energy Flow (kWh/m2)

0.06 25

0.04 20

0.02 15

0 10
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
-0.02 5

-0.04 0
Time (Hour)

Base Case Shading PCM Smart Bio-skin Operative Temperature

Figure C.9 Energy Flow Through the East Wall in May

0.12 40

0.1 35
0.08 Ambiant Temperature (℃)
Energy Flow (kWh/m2)

30
0.06
25
0.04
20
0.02
15
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 10
-0.02

-0.04 5

-0.06 0
Time (Hour)

Base Case Shading PCM Smart Bio-skin Operative Temperature

Figure C.10 Energy Flow Through the West Wall in May

132
 0.12 40.00

0.1 35.00

30.00
0.08
25.00
0.06
20.00
0.04
15.00
0.02
10.00

0 5.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
-0.02 0.00

Base Case Shading PCM Smart Bio.skin Ambient Temperature

Figure C.11 Energy Flow Through the East Wall in June

0.12 40.00

0.1 35.00

0.08 30.00

0.06 25.00

0.04 20.00

0.02 15.00

0 10.00
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
-0.02 5.00

-0.04 0.00

Base Case Shading PCM west Ambient Temperature

Figure C.12 Energy Flow Through the West Wall in June

133
 0.12 40

0.1 35

Ambiant Temperature (℃)

30
Energy Flow (kWh/m2)

0.08
25
0.06
20
0.04
15
0.02
10

0 5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
-0.02 0
Time (Hour)

Base Case Shading PCM Smart Bio-skin Ambiant Temperature

Figure C.13 Energy Flow Through the East Wall in July

0.12 40

0.1 35
Ambiant Temperature (℃)
Energy Flow (kWh/m2)

0.08 30

0.06 25

0.04 20

0.02 15

0 10
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
-0.02 5

-0.04 0
Time (Hour)

Base Case Shading PCM Smart Bio-skin Ambiant Temperature

Figure C.14 Energy Flow Through the West Wall in July

134
 Figure C.15 Energy Flow Through the East Wall in September

0.12 35

0.1 30
Ambiant Temperature (℃)
Energy Flow (kWh/m2)

0.08
25
0.06
20
0.04
15
0.02
10
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
-0.02 5

-0.04 0
Time (Hour)

Base Case Shading PCM Smart Bio-skin Ambient Temperature

Figure C.16 Energy Flow Through the West Wall in September

135
 0.12 35

0.1 30

Ambiant Temperature (℃)

Energy Flow (kWh/m2)

0.08 25

0.06 20

0.04 15

0.02 10

0 5
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
-0.02 0
Time (Hour)

Base Case Shading PCM Smart Bio-skin Ambient Temperature

Figure C.17 Energy Flow Through the East Wall in October

0.12 35

0.1
30
0.08
25
0.06

0.04 20

0.02 15

0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 10
-0.02
5
-0.04

-0.06 0

Base Case Shading PCM Smart Bio-skin Ambient Temperature

Figure C.18 Energy Flow Through the West Wall in October

136
D. Appendix D: Psychrometric Chart of Each Scenario from April to October

Figure D.19 Psychrometric Chart of Base Case in April, Drawn with Ladybug
Plugin

Figure D.20 Psychrometric Chart of PCM Case in April, Drawn with Ladybug
Plugin

137
Figure D.21 Psychrometric Chart of Shading Case in April, Drawn with Ladybug
Plugin

Figure D.22 Psychrometric Chart of Smart Bio-Skin Case in April, Drawn with
Ladybug Plugin

138
Figure D.23 Psychrometric Chart of Base Case in May, Drawn with Ladybug
Plugin

Figure D.24 Psychrometric Chart of PCM Case in May, Drawn with Ladybug
Plugin

139
Figure D.25 Psychrometric Chart of Shading Case in May, Drawn with Ladybug
Plugin

Figure D.26 Psychrometric Chart of Smart Bio-skin Case in May, Drawn with
Ladybug Plugin

140
Figure D.27 Psychrometric Chart of Base Case in June, Drawn with Ladybug
Plugin

Figure D.28 Psychrometric Chart of PCM Case in June, Drawn with Ladybug
Plugin

141
Figure D.29 Psychrometric Chart of Shading Case in June, Drawn with Ladybug
Plugin

Figure D.30 Psychrometric Chart of Smart Bio-Skin Case in June, Drawn with
Ladybug Plugin

142
Figure D.31 Psychrometric Chart of Base Case in July, Drawn with Ladybug
Plugin

Figure D.32 Psychrometric Chart of PCM Case in July, Drawn with Ladybug
Plugin

143
Figure D.33 Psychrometric Chart of Shading Case in July, Drawn with Ladybug
Plugin

Figure D.34 Psychrometric Chart of Smart Bio-Skin Case in July, Drawn with
Ladybug Plugin

144
Figure D.35 Psychrometric Chart of Base Case in September, Drawn with Ladybug
Plugin

Figure D.36 Psychrometric Chart of PCM Case in September, Drawn with

Ladybug Plugin

145
 Figure D.37 Psychrometric Chart of Shading Case in September, Drawn with
Ladybug Plugin

Figure D.38 Psychrometric Chart of Smart Bio-Skin Case in September, Drawn

with Ladybug Plugin

146
Figure D.39 Psychrometric Chart of Base Case in October, Drawn with Ladybug
Plugin

Figure D.40 Psychrometric Chart of PCM Case in October, Drawn with Ladybug
Plugin

147
 Figure D.41 Psychrometric Chart of Shading Case in October, Drawn with
Ladybug Plugin

Figure D.42 Psychrometric Chart of Smart Bio-Skin Case in October, Drawn with
Ladybug Plugin

148
Table D.14 Summary of Range and Most Frequent Temperature and Relative Humidity for
Each Case (April- October)

Month Case Temperature Relative Most Frequent Situation

Range (℃) Humidity Frequency Temperature Relative
Range (Hours) (℃) Humidity
(%) (%)
Base 21-41 20-95 28/720 32 60
April PCM 31-42 20-80 35/720 34 60
Shading 21-38 25-100 31.5/720 31 60
S.Bio- 21-38 25-95 66.5/720 32 60-70
skin
Base 29-43 15-80 32.6/744 38 40
May PCM 31-42 20-80 41/744 34 60
Shading 28-40 20-75 65.2/744 33-35 65-75
S.Bio- 30-40 20-80 41/744 34 65
skin
Base 33-45 25-80 78.4/720 39-41 40-50
June 78.4/720 35-37 60-70
PCM 33-44 25-80 49/720 39 45
Shading 31-42 30-85 98/720 36-38 50-60
S.Bio- 32-41 30-85 49/720 34 70
skin 98/720 36-38 50-60
Base 35-45 30-80 66.4/744 38 65
July PCM 36-45 30-75 74.7/744 38 60
Shading 34-41 35-85 83/744 35 75
S.Bio- 35-42 35-85 83/744 36 70
skin
Base 34-45 25-85 44.8/720 36 70
September 89.6/720 40-42 45-55
PCM 35-45 25-80 50.4/720 37 60
Shading 33-43 30-90 44.8/720 35 70
44.8/720 38 55
S.Bio- 34-42 30-85 168/720 36-38 50-70
skin
Base 32-42 20-90 24/744 39 40
October 24/744 34 60
PCM 33-42 25-85 30/744 38 40
Shading 33-43 30-90 360/744 34-41 45-75
S.Bio- 32-39 25-90 30/744 37 45
skin 60/744 33-35 60
30/744 34 70

149