Applied Science, Faculty of
Mechanical Engineering, Department of
DSpace
UBCV
Gerrard, Kyle
Master of Applied Science - MASc
University of British Columbia
Radiant sky cooling offers a sustainable solution for improving thermal comfort in semitransient public spaces, such as outdoor seating areas, open-air corridors, and transit stations. By optimizing the material spectral emissivity, it is possible to limit solar heat gains and emit strongly through the atmosphere to deep space to achieve a net-negative heat balance and cool a surface below ambient temperatures even in direct sunlight. This work explores the potential of radiant sky cooling to mitigate heat stress without using electricity or actively conditioning a controlled air volume. Through a 2-month field test of a full-size 4’x4’ custom radiant cooling panel this study was able to examine the efficacy of the Radi-Cool commercial radiant film product and calibrate thermal models for use in design planning. A Phase Change Material (PCM) was also synthesized and tested, then added to the underside of the cooling panel to explore the performance benefits of such a material in maintaining adequately low daytime temperatures. The performance of the cooling panel was also compared against a typical urban tree canopy to assess whether a cooling structure based on a similar panel could be a viable alternative to vegetation for decreasing the mean radiant temperature (MRT) experienced by an occupant. Results indicate that a PCM is an excellent candidate to enhance radiant sky-cooling due to its high thermal mass to weight ratio and ability to extend time in desired temperature ranges. The calibrated thermal model also allows the potential for designers to test better performing materials, different orientations and tilts, and assesses real-world factors such as surface fouling and convective losses. This work demonstrates that radiant sky cooling is a viable, low-energy intervention for enhancing the thermal experience in semi-outdoor environments, contributing to sustainable urban cooling strategies.
Achieving Daytime Thermal Comfortwith Radiant Sky-CoolingbyKyle GerrardB.ASc., The University of British Columbia, 2022A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF APPLIED SCIENCEinThe Faculty of Graduate and Postdoctoral Studies(Mechanical Engineering)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)April 2025© Kyle Gerrard 2025The following individuals certify that they have read, and recommend tothe Faculty of Graduate and Postdoctoral Studies for acceptance, the thesisentitled:Achieving Daytime Thermal Comfort with Radiant Sky-Coolingsubmitted by Kyle Gerrard in partial fulfillment of the requirements forthe degree of Master of Applied Science in Mechanical Engineering.Examining Committee:Adam Rysanek, Associate Professor, School of Architecture and LandscapeArchitecture, UBCSupervisorSteven Rogak, Professor, Mechanical Engineering, UBCCo-supervisorPatrick Kirchen, Associate Professor, Mechanical Engineering, UBCSupervisory Committee MemberForrest Meggars, Associate Professor, Andlinger Center for Energy and theEnvironment, Princeton UniversityAdditional ExamineriiAbstractRadiant sky cooling offers a sustainable solution for improving thermal com-fort in semitransient public spaces, such as outdoor seating areas, open-aircorridors, and transit stations. By optimizing the material spectral emis-sivity, it is possible to limit solar heat gains and emit strongly through theatmosphere to deep space to achieve a net-negative heat balance and coola surface below ambient temperatures even in direct sunlight. This workexplores the potential of radiant sky cooling to mitigate heat stress withoutusing electricity or actively conditioning a controlled air volume. Througha 2-month field test of a full-size 4’x4’ custom radiant cooling panel thisstudy was able to examine the efficacy of the Radi-Cool commercial radiantfilm product and calibrate thermal models for use in design planning. APhase Change Material (PCM) was also synthesized and tested, then addedto the underside of the cooling panel to explore the performance benefits ofsuch a material in maintaining adequately low daytime temperatures. Theperformance of the cooling panel was also compared against a typical ur-ban tree canopy to assess whether a cooling structure based on a similarpanel could be a viable alternative to vegetation for decreasing the meanradiant temperature (MRT) experienced by an occupant. Results indicatethat a PCM is an excellent candidate to enhance radiant sky-cooling due toits high thermal mass to weight ratio and ability to extend time in desiredtemperature ranges. The calibrated thermal model also allows the potentialfor designers to test better performing materials, different orientations andtilts, and assesses real-world factors such as surface fouling and convectivelosses. This work demonstrates that radiant sky cooling is a viable, low-energy intervention for enhancing the thermal experience in semi-outdoorenvironments, contributing to sustainable urban cooling strategies.iiiLay SummaryRadiant sky cooling offers a sustainable way to improve comfort in publicspaces like outdoor seating areas and transit stations. This technique coolssurfaces by minimizing heat from the Sun and radiating heat away intospace, allowing surfaces to drop below air temperature, even under directsunlight. A two-month field test evaluated a full-size radiant cooling panelcoated with a commercial product called Radi-Cool. The study also exploredthe benefits of adding a Phase Change Material (PCM) to the panel, whichstores and releases heat to keep temperatures low for longer. Results showthat PCM can lower the surface temperature of the panel by several degreesduring the hottest part of the day and is a simple performance enhancingaddition. The simple thermal model developed from this study that predictsthe surface temperature of the panel can help designers plan better coolingsystems by considering different materials, layouts, and real-world factorslike dirt buildup and wind losses. The research demonstrates that radiantsky cooling has potential as a reasonably effective, low-energy option forurban cooling, and could be an alternative to tree cover for reducing heatexposure.ivPrefaceThis study is based on work conducted on the roof of the CIRS buildingand on the grounds of the Third Space Commons building, both located atUBC’s Point Grey campus. I was responsible for designing the experimentalplan and building and monitoring the test setups. Much consultation wastaken with collaborators Adam Rysanek and JeremyWong, who both helpedtremendously during field study setup and data collection. Generative AIwas used in this work to help speed the process of drafting and troubleshoot-ing both the Python code responsible for the thermal modelling and figurecreation, and the LaTeX code used to create the thesis document.vTable of ContentsAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiiLay Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ivPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vTable of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . viList of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiList of Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . xiiiList of Symbols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvAcknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . xvii1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.2 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . 41.3 Research Framework . . . . . . . . . . . . . . . . . . . . . . . 52 Thermal Model of Cooling Panel . . . . . . . . . . . . . . . . 62.1 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . 62.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.2.1 Overall Heat Balance . . . . . . . . . . . . . . . . . . 92.2.2 Sky-Panel Net Flux . . . . . . . . . . . . . . . . . . . 102.2.3 Solar Radiation Flux . . . . . . . . . . . . . . . . . . 142.2.4 Convective and Conductive Flux . . . . . . . . . . . . 152.3 Python Implementation . . . . . . . . . . . . . . . . . . . . . 182.3.1 Heat Balance Equation Solution . . . . . . . . . . . . 18viTable of Contents3 Cooling Panel Field Testing . . . . . . . . . . . . . . . . . . . 193.1 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . 193.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203.2.1 Experimental Setup . . . . . . . . . . . . . . . . . . . 203.2.2 Baseline Trial . . . . . . . . . . . . . . . . . . . . . . 313.2.3 Mylar Comparison Trial . . . . . . . . . . . . . . . . 313.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313.3.1 Baseline Trial . . . . . . . . . . . . . . . . . . . . . . 313.3.2 Mylar Comparison Trial . . . . . . . . . . . . . . . . 343.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343.4.1 Baseline Trial . . . . . . . . . . . . . . . . . . . . . . 343.4.2 Mylar Comparison Trial . . . . . . . . . . . . . . . . 354 Calibrating the Thermal Models . . . . . . . . . . . . . . . . 364.1 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . 364.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 374.2.1 Baseline Trial . . . . . . . . . . . . . . . . . . . . . . 414.2.2 Cloud Cover Trial . . . . . . . . . . . . . . . . . . . . 414.2.3 Surface Tilt Trial . . . . . . . . . . . . . . . . . . . . 424.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 434.3.1 Baseline Trial . . . . . . . . . . . . . . . . . . . . . . 434.3.2 Cloud Cover Trial . . . . . . . . . . . . . . . . . . . . 464.3.3 Surface Tilt Trial . . . . . . . . . . . . . . . . . . . . 484.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 484.4.1 Baseline Trial . . . . . . . . . . . . . . . . . . . . . . 484.4.2 Cloud Cover Trial . . . . . . . . . . . . . . . . . . . . 494.4.3 Surface Tilt Trial . . . . . . . . . . . . . . . . . . . . 515 Phase Change Material Exploration . . . . . . . . . . . . . . 525.1 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . 525.1.1 General Background . . . . . . . . . . . . . . . . . . . 525.1.2 Salt Hydrate Eutectic PCMs . . . . . . . . . . . . . . 545.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 545.2.1 PCM Synthesis . . . . . . . . . . . . . . . . . . . . . 545.2.2 Thermal Cycling . . . . . . . . . . . . . . . . . . . . . 565.2.3 Field Testing . . . . . . . . . . . . . . . . . . . . . . . 585.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 595.3.1 Thermal Cycling . . . . . . . . . . . . . . . . . . . . . 595.3.2 Field Testing . . . . . . . . . . . . . . . . . . . . . . . 605.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62viiTable of Contents5.4.1 Thermal Cycling . . . . . . . . . . . . . . . . . . . . . 625.4.2 Field Testing . . . . . . . . . . . . . . . . . . . . . . . 626 Theoretical Cooling Structure and Tree Canopy Trial . . . 646.1 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . 646.1.1 Outdoor Thermal Comfort . . . . . . . . . . . . . . . 646.1.2 Thermal Comfort Impacts of Vegetation . . . . . . . 656.1.3 Thermal Comfort Assessment Metrics . . . . . . . . . 666.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 676.2.1 Modelling Thermal Comfort in Application . . . . . . 676.2.2 Tree Canopy Experimental Setup . . . . . . . . . . . 686.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 716.3.1 Theoretical Literature Comparison . . . . . . . . . . 716.3.2 Field Tested Tree Canopy Comparison . . . . . . . . 736.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 746.4.1 Modelled Cooling Structure . . . . . . . . . . . . . . 746.4.2 Field Tested Cooling Structure . . . . . . . . . . . . . 757 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 767.1 Summary of Results . . . . . . . . . . . . . . . . . . . . . . . 767.1.1 Literature Review . . . . . . . . . . . . . . . . . . . . 767.1.2 Thermal Model and Calibration . . . . . . . . . . . . 767.1.3 Panel Performance . . . . . . . . . . . . . . . . . . . 777.1.4 PCM Enhancement . . . . . . . . . . . . . . . . . . . 777.1.5 Theoretical Cooling Shelter . . . . . . . . . . . . . . . 777.2 Study Limitations . . . . . . . . . . . . . . . . . . . . . . . . 787.3 Recommendations for Future Work . . . . . . . . . . . . . . 80Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85AppendicesA Additional Calculations . . . . . . . . . . . . . . . . . . . . . . 99A.1 PET Interpretation Scale . . . . . . . . . . . . . . . . . . . . 99A.2 T-History Calculations . . . . . . . . . . . . . . . . . . . . . 99A.3 Estimating Ambient Temperature during Tilt Trial . . . . . 102B Field Testing Timeline . . . . . . . . . . . . . . . . . . . . . . . 104C Cooling Paint Investigation . . . . . . . . . . . . . . . . . . . 105viiiTable of ContentsD Wind Sensor Accuracy Limitations . . . . . . . . . . . . . . 108E Convective Shield Trial . . . . . . . . . . . . . . . . . . . . . . 110E.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110E.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111E.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112F Additional Field Testing Results . . . . . . . . . . . . . . . . 114F.1 Individual Panel Temperatures . . . . . . . . . . . . . . . . . 114F.2 Baseline Calibration Results and Heat Fluxes . . . . . . . . . 115ixList of Tables2.1 Effective sky emissivity correlations. . . . . . . . . . . . . . . 82.2 Effective sky emissivity cloud corrections. . . . . . . . . . . . 82.3 Short Caption for List of Tables. . . . . . . . . . . . . . . . . 173.1 ESB Davis VantagePro specifications. . . . . . . . . . . . . . 253.2 Assorted sensor specifications. . . . . . . . . . . . . . . . . . . 273.3 Clearsky sub-ambient panel performance results. . . . . . . . 334.1 Basic model parameter definitions and assumptions. . . . . . 394.2 Calibration parameter ranges. . . . . . . . . . . . . . . . . . . 404.3 Baseline calibration parameter results. . . . . . . . . . . . . . 454.4 Cloud cover trial calibration convection parameter and errorresults. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 474.5 Surface tilt trial calibration parameter and error results. . . . 484.6 Average convection coefficient results with sky type. . . . . . 505.1 Primary PCM ’recipe’. . . . . . . . . . . . . . . . . . . . . . . 565.2 T-history PCM thermal cycling property results. . . . . . . . 595.3 PCM-enhancement results for August 03 and August 10. . . . 61A.1 R1.1 T-history calculation inputs and references. . . . . . . . 102xList of Figures1.1 Depiction of the selective emissivity of RSC surfaces. . . . . . 21.2 Research Framework. . . . . . . . . . . . . . . . . . . . . . . . 52.1 Short Caption to Appear in List of Figures. . . . . . . . . . . 102.2 Sidewall heat flow. . . . . . . . . . . . . . . . . . . . . . . . . 183.1 Test site locations. . . . . . . . . . . . . . . . . . . . . . . . . 213.2 CIRS rooftop testing location. . . . . . . . . . . . . . . . . . . 223.3 The cooling panel construction process. . . . . . . . . . . . . 233.4 Cooling panel perimeter section. . . . . . . . . . . . . . . . . 243.5 Baseline modifications to panel with instrumentation identified. 253.6 Cooling panel instrumentation locations. . . . . . . . . . . . . 293.7 The local ambient temperature sensor setup. . . . . . . . . . 303.8 Mylar comparison trial physical setup. . . . . . . . . . . . . . 323.9 Panel temp and ambient temp results on July 20. . . . . . . . 323.10 Air temp, panel temp, and local wind speed comparison onJuly 20. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333.11 Mylar comparison trial temperature results. . . . . . . . . . . 344.1 Calibration model results and participating heat fluxes onJuly 20. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 444.2 Model calibration results for cloud cover, July 22. . . . . . . . 464.3 Model calibration results for heavy cloud cover, Aug. 12. . . . 474.4 Tilted panel calibration temperature results, Sept. 06. . . . . 495.1 PCM Thermal cycling physical setup. . . . . . . . . . . . . . 575.2 PCM field test panel embedment. . . . . . . . . . . . . . . . . 585.3 PCM field test placement and sensor diagram. . . . . . . . . 595.4 PCM lab cycling temperature results. . . . . . . . . . . . . . 605.5 PCM field test temperature results, August 03. . . . . . . . . 616.1 Cooling structure heat balance representation. . . . . . . . . . 68xiList of Figures6.2 Under-tree testing location. . . . . . . . . . . . . . . . . . . . 706.3 Under-tree field trial instrumentation setup. . . . . . . . . . . 716.4 Ideal cooling shelter PET temperature for Cheung et al. data. 726.5 Ideal cooling shelter PET temperature for Ka´ntor et al. data. 726.6 Tree canopy vs. cooling panel temperature results, Sept. 05. . 736.7 Tree canopy vs. ideal cooling shelter PET results, Sept. 05. . 747.1 Theoretical advanced material cooling panel temperatures,July 20. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 817.2 Conceptual cooling shelter rendering. . . . . . . . . . . . . . . 84A.1 PET interpretation scale diagram. . . . . . . . . . . . . . . . 99A.2 T-history calculation components integration diagram. . . . . 101B.1 Field testing timeline. . . . . . . . . . . . . . . . . . . . . . . 104C.1 Cooling paint variations and cure-shrinkage failure. . . . . . . 107D.1 TSI VelociCalc directional error diagram. . . . . . . . . . . . 109E.1 Convective shield trial temperature results, July 26-28. . . . . 111E.2 Convective shield trial sub-ambient temperature delta results,July 26-28. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112F.1 Individual HOBO panel surface temperatures, July 20. . . . . 115F.2 Calibration model results and participating heat fluxes onAug. 03. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116F.3 Calibration model results and participating heat fluxes onAug. 10. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117xiiList of Abbreviations2-D Two Dimensional3-D Three DimensionalAZ ArizonaBEM Building Energy ModellingCIRS Centre for Interactive Research on SustainabilityCS Cooling SurfaceDD Directionally DependantDHI Direct horizontal irradianceDI Directionally IndependantDNI Direct normal irradianceESB Earth Sciences BuildingGHI Global horizontal irradianceIR Infrared RadiationLA Los AngelesLSE Least Squared ErrorLW Longwave (radiation)MRT Mean Radiant TemperatureOTC Outdoor Thermal ComfortPCM Phase Change MaterialPET Physiological Equivalent TemperaturexiiiList of AbbreviationsR&D Research and DevelopmentRH Relative HumidityRSC Radiant Sky-CoolingSD Spectrally DependantSI Spectrally IndependantSW Shortwave (radiation)TWC Total Water ColumnUBC University of British ColumbiaUCLA University of California - Los AngelesUTCI Universal Thermal Climate IndexxivList of SymbolsA surface areaea water vapor pressureFa−b radiation view factor from surface a to surface bG total incoming/absorbed thermal radiationGD direct solar irradianceGd diffuse solar irradianceGr reflected solar irradianceIsolar total solar irradianceJ total outgoing/emitted thermal radiationk material conductivityL lengthn sky cloud fractionqconv overall convective heat flux between panel and surrounding airqground conductive heat flux between panel and groundqsky−panel,net net radiation flux between sky and panelqsolar absorbed solar radiation flux to panelTa ambient temperatureTcs cooling surface temperatureTdp dewpoint temperatureTground ground surface temperature directly below panelxvList of SymbolsTsky sky temperaturet solar timeα total surface absorptivityδkj Kronecker deltaϵ total surface emissivityϵsky−cloud cloud-corrected sky emissivityϵsky−eff effective sky emissivityρ total surface reflectivityσ Stefan-Boltzmann constantτ total surface transmissivityΣ surface tilt anglexviAcknowledgementsThroughout my graduate studies, there have been many people I must ex-press my gratitude to. First and foremost, I would like to extend my sincerestthanks to my supervisor and head of the Building Decisions Research Group(BDRG), Adam Rysanek. Without his guidance and support, I would nothave been able to pursue this path. Thank you also to my co-supervisor,Steven Rogak.I am also grateful to the members of my research group who have given mevaluable input throughout all stages of my work. Jeremy Wong, SanyogitaManu, and Sarah Crosby have been wonderful members of the group; theirsupport has been indispensable.I also thank Tim Herron at CIRS and the rest of the UBC Facilities teamwho worked quickly to give access to a suitable field test site.I must also thank Matty Jeronimo at the SPPH OET lab, his consultation onmeasurements and facilitation of the instrumentation acquisition was veryhelpful.xviiChapter 1Introduction1.1 BackgroundMany regions across the world are struggling to adapt to rising temperaturesand extreme heat waves. For example, in the Metro Vancouver, where thisstudy was implemented, the regional government predicts that by 2050 thearea will see an average temperature increase of 3◦C, a doubling of summerdays above 25◦C, and an increase of the 1-in-20 extreme temperature oc-currence from 34◦C to 38◦C [1]. This climate shift can lead to dangerousconditions for vulnerable populations, as highlighted in the severe heat waveof June 2021 in the Canadian province of British Columbia which was linkedto 619 heat-related deaths [2]. Heat exposure is therefore a critical publichealth concern that has implications for urban design and planning. Thedesign of transient public spaces such as bus stops, pavilions, and outdoordining often considers rain or wind protection but adequate shade and over-heating protection for occupants is not guaranteed. For example, a studyby the UCLA Institute of Transportation Studies found that only an av-erage of 28% of transit stops across 3 transit regions in LA had trees thatcould provide significant shading [3]. Additionally, a study in Phoenix, AZdetermined that about half of respondents at bus stops reported feeling’hot’ or ’very hot’ [4] and this absence of thermal comfort can negativelyimpact the wellness of passengers and reduce ridership [5]. Re-imaginingthe potential of these types of public facilities is essential, especially locallyin the Metro Vancouver region as public transit expands to accommodaterapid population growth [6]. The design challenge that these types of publicspaces present is that they are numerous, so installation and maintenancecosts could be considerable, and are typically open-air, making conventionalair-based active cooling systems wasteful due to significant heat losses.The ’Cold Tube’ [7] project in Singapore is a leading example of the potentialfor radiant cooling to be used in naturally ventilated or outdoor spaces toachieve thermal comfort goals. However, that system used an electricity-11.1. Backgroundbased heat pump to chill the radiant cooling panels. Is there a way topassively achieve a similar goal? If the system does not use a heat pumpto reject heat into the surrounding air, ground, or water, where could itgo? A possible answer: to outer space. Deep space is an enormous heatsink, with effective blackbody exchange temperatures as low as 3K andeffectively limitless capacity [8]. Our planet relies upon this to shed excessheat absorbed from the Sun and maintain a global heat balance, and it isthis balance being negatively affected by changing atmospheric conditionsdue to carbon emissions. How does the heat get from the surface of theplanet to space? Thermal radiation through the atmosphere. Thus, thefundamental challenge in leveraging space for its daytime cooling is twofold:1) limiting the amount of solar radiation being absorbed and 2) emittingthermal radiation strongly enough and in the correct IR wavelength rangesto get through the atmosphere’s ’windows’ of high transmissivity to deepspace, as depicted in Fig 1.1. A third goal for lowering a surface temperatureas much as possible is reducing parasitic thermal exchange, principally fromconvection to the surrounding air.Figure 1.1: Graphical representation of the spectral selective goals in RSCmaterials.21.1. BackgroundThis pursuit of cooling to space will be referred to in this work as ’radiantsky-cooling’ (RSC), and is already observed by many people in the formof dew and frost formation on clear nights. Nighttime radiant cooling hasancient origins [9], has been studied for many decades, and has a number ofuseful applications [10] [11] [12]. Nevertheless, it is only recently that materi-als science has progressed sufficiently to enable daytime radiant sub-ambientcooling under full sun [13]. These advances in spectrally selective materialsthat combine high solar reflectivity and high longwave IR emissivity havelargely been limited to lab-scale material developments. These RSC materi-als are diverse and range from complex multi-layer photonic structures [14][15], to more straightforward hybrid metamaterials [16], to comparativelysimple ultra-white BaSO4 [17] and CaCO3 [18] based ’paints’, to a bio-structure inspired ceramic [19]. One of the more interesting developmentsfrom a deployment simplicity perspective are porous polymer ’filters’ thatare solar reflective but IR transparent such that they can transform whatthey cover effectively into an RSC material or structure [20]. There has alsobeen significant research into the potential applications of these materialsin areas like thermal power plants [21], building cooling and energy saving[22] [23], and photovoltaic power system efficiency [24].The application of these materials are an R&D focus for several companieslike SRI International [25] and are just starting to achieve full commercializa-tion to the point where they represent functional design tools for architectsand engineers over a wide range of thermal problems. Examples of commer-cially available radiant sky-cooling products are fluid cooling panels fromSkyCool Systems [26], cooling film from SpaceCool Inc. [27] and a varietyof films, membranes, coatings, and fabrics from Radi-Cool Radiation Re-frigeration Technology Co. Ltd., recently made available through ColdraysLLC [28]. A start-up named MetaRE based in New York is reported [29] tohave licensed an RSC coating [30] from Columbia University and is headingfor commercialization, but further details were difficult to find.The literature example with the most direct relevance to the work of thisstudy is an exploration of a bus stop style cooling shelter by Mokhtari etal. [31]. This study modelled the use of Radi-Cool based fluid coolingpanels to shed heat with Cold Tube style internal radiant cooling panels tocool occupants and projected up to a 10 degree reduction in the UniversalThermal Climate Index (UTCI) in mid-rise urban setting in Tehran, Iran.However, this study did not have a field testing component, and while thedesign was passive it remained relatively complex with all of the coolingfluid transfer and various panels. Could there be a more direct and simple31.2. Problem Statementapproach that would still be effective in improving outdoor thermal comfort(OTC) in a transient public space?1.2 Problem StatementThe central research questions of this study and the affiliated chapters are:Question 1: Can a thermal model be developed and calibrated to predictthe RSC panel temperature based on weather data, material properties, andpanel geometry? (Chapter 2 and 4)Question 2: What is the applied performance of an RSC panel using acommercially available RSC product? (Chapter 3)Question 3: Could a low phase temperature Phase Change Material (PCM)enhance the performance of the RSC panel? (Chapter 5)Question 4: Could this RSC panel be used to create a theoretical ‘coolingshelter’ that improves outdoor thermal comfort and how does it compare tonatural vegetation protection? (Chapter 6)The following are the main contributions of this thesis:1) A simple Python thermal model for predicting the surface temperature ofthe cooling panel and suitable for future integration into energy simulationsand thermal comfort applications. This model can account for panel tilt,orientation, and cloud cover.2) Field results that examine the applied performance of a basic passiveRadi-Cool RSC panel under several weather conditions and are used tocalibrate the thermal model.3) Field results that demonstrate the successful application of a low-temperatureeutectic salt hydrate PCM to boost the performance of the Radi-Cool RSCpanel.4) Simulation and field results that indicate a 100% passive PCM-based RSCpanel is a viable basis for a cooling structure that promotes outdoor thermalcomfort to protect public health.41.3. Research Framework1.3 Research FrameworkThe following figure describes the general research framework that was fol-lowed to investigate the possibility of using daytime sub-ambient RSC tech-nology to enhance outdoor thermal comfort.Figure 1.2: Description of the research framework’s critical components.5Chapter 2Thermal Model of CoolingPanelThe goal of this chapter is to develop the simplest model possible capableof prediction the steady-state RSC panel surface temperature. This modelserves 2 purposes: 1) assessing the potential of RSC for OTC before commit-ting to a field trial and 2) providing the basis for further calibration based onfield trial observed data. This chapter also reviews relevant literature on themodelling of RSC surfaces. The developed thermal model was structuredand computed using Python for flexibility and simplicity. Values attributedto the Radi-Cool cooling surface are referred to with the subscript ’CS’.2.1 Literature ReviewMany recent studies examine the challenge of modelling radiant sky-cooling.Fundamentally, any body above 0 degrees Kelvin will contain energy andhave the potential to emit that energy through radiation [32]. Radiationheat transfer is therefore the exchange of energy between bodies with nointervening medium required, and can be described by wave theory as theelectromagnetic spectrum.One of the primary bodies that a sky cooling surface will interact with isEarth’s atmosphere: the collection of gases (oxygen, nitrogen, etc.) andwater vapor that lies between the surface of the planet and outer space[33]. The atmosphere has a complex interaction and variable properties. Akey property is the effective transmissivity as seen by a surface at groundlevel, which depends on factors such as total water column (TWC), geo-graphical location, cloud cover, smoke, and pollution [34]. It is critical toaccount for the differences in atmospheric transmissivity as a function ofambient conditions, as this serves to inform the potential efficacy of a skycooling based technology deployed in a given area. In parallel, atmospheric62.1. Literature Reviewtransmissivity/absorptivity changes as a function of viewing angle, due tothe path length becoming longer as the zenith angle increases from lookingstraight up to looking at the horizon. Furthermore, the atmosphere itselfis a source of IR radiation, emitted primarily by water vapor and carbondioxide [35] [36]. The literature typically considers the atmosphere to havea reflectivity of 0, meaning that if radiation is not transmitted, it is ab-sorbed. Previous studies generally divide atmospheric modelling techniquesinto 2 main categories: a) spectrally dependent and directionally dependent(SD–DD), b) spectrally independent and directionally independent (SI–DI)[13]. SD-DD is considered to be the most comprehensive, but the approachincreases model complexity and accurate spectral data for the atmosphererequires access to advanced software such as MODTRAN [37]. Granqvist[38], Lushiku [39], and Berdahl [35] developed directional correlations foratmospheric transmissivity, with the Granqvist correlation being the mostwidely used in more recent modelling. Examples that use the spectrallydefined energy balance approach for modelling are the works of Raman etal. [14], Aili et al. [40], and Zhao et al. [13] among many others. Thisstandard model type also defines the opaque cooling material emissivityspectrally to define the emitted radiation from the surface and the absorbedsolar radiation, but it is usually only the atmospheric transmissivity that isdirectionally dependent. In the spectrally defined models, total solar radia-tion, combining all wavelengths, is typically mapped to the standard AM1.5solar spectrum. That is not necessary for a graybody method as done byGoldstein et al. [41] and Hanif et al. [42].SI-DI atmospheric radiation exchange is the most simple approach, with alarge variety of empirical correlations, including cloud cover corrections, forthe effective graybody sky emissivity. These correlations are based on phys-ical measurements at various geographical locations and a relevant selectionis available in Table 2.1 and 2.2.Table 2.2 has a summary of relevant cloud-correction methods to accountfor the impact clouds will have on the effective sky emissivity.72.1. Literature ReviewAuthor CorrelationSwinbank 1963 [43] ϵsky−eff = 9.37e−6(Ta)2Clark 1978 [44] ϵsky−eff = 0.787 + 0.764ln(T ∗dp/273)Berger 1984 [45] ϵsky−eff = 0.770 + 0.0038(Tdp)Berdahl 1984 [36]ϵsky−eff = 0.771+0.0056(Tdp) + 0.000073(Tdp)2 + 0.013 cos(t)Niemela¨ 2001 [46] ϵsky−eff ={0.72 + 0.009(ea − 2), ea ≥ 20.72− 0.076(ea − 2), ea < 2Lhomme 2007[47] ϵsky−eff = 1.18(ea/Ta)1/7Table 2.1: Effective sky emissivity correlations.Ta = ambient temp [K]T ∗dp = dewpoint temp [K]Tdp = dewpoint temp [◦C]t = solar time [hours]ea = water vapor pressure [hpa]Author CorrectionClark 1978 [44] ϵsky−cloud = ϵsky−eff [1 + 0.0334(n) − 0.0035(n2) +0.00028(n3)]Kasten 1980 [48] ϵsky−cloud = (1 + 0.026n)ϵsky−effTable 2.2: Effective sky emissivity cloud corrections.n = sky cloud fraction, 0-1082.2. Methods2.2 MethodsA basic graybody dual-emissivity approach with no directional dependencewas chosen in this work over the more complex spectrally-defined energybalance commonly applied in the literature for 4 main reasons. 1) This in-terpretation of the radiant physics is the most common in building energymodelling (BEM) simulations within the architecture industry. 2) The sim-plicity of the model delivers the computational speed useful for integrationinto BEM softwares and the calibration process. 3) The model was intendedas a field test interpretation and preliminary design tool, so accuracy require-ments were not as stringent. 4) The manufacturer of the field-tested com-mercial Radi-Cool product did not wish to disclose the proprietary spectralemissivity data.The sky cooling surface is assumed to be at a steady-state temperature whenall heat fluxes in and out of the surface are balanced and the net heat flux iszero. The single unknown in the balance is the surface temperature and thebalance is solved in Python with a binary search. Throughout this processthe energy balance will deal with heat flux in units of W/m2. Other basicmodel assumptions are:1) All materials are of uniform temperature across their surfaces and throughtheir depth.2) All materials are opaque (no transmissivity) and diffuse (no directionaldependence on radiative properties).3) Cooling panel surface has no mass nor heat capacity, with instant diffusionof heat throughout the material.4) An infinite plane geometry.2.2.1 Overall Heat BalanceIn combining all the heat transfer modes, the general heat balance equationat equilibrium is defined by Eqn. 2.1 and shown visually in Fig 2.1.qsky−panel,net + qsolar + qconv + qground = 0 (2.1)92.2. MethodsFigure 2.1: Physical interpretation of participating heat fluxes.2.2.2 Sky-Panel Net FluxRadiosity MethodThe radiosity method was used to determine the net flux between the skyand the cooling surface, while assuming a simplified ’infinite plane’ geometrywhere the view factor between the cooling surface and sky and vice-versa is1. The radiosity method is a framework for accounting for the net radiationheat balance at each surface in an enclosure and was adapted for this workbased on the textbook by Siegel and Howell [49]. An enclosure is definedas a set of surfaces in either 2-D or 3-D that have continuous boundaries.Firstly, the total hemispherical emissive power of a graybody surface is givenby Eqn. 2.2.E = ϵσT 4 (2.2)σ: Stefan-Boltzmann constant[Wm2K4]ϵ: emissivity of the surface, 0-1T : temperature of the surface [◦K]Another important concept is view factor reciprocity. A view factor is de-fined as the fraction of energy that leaves a given surface and is incident onanother surface, and is therefore unique to a set of surfaces and the directionthe energy travels. However, if the surfaces in question are assumed to bediffuse and uniform, reciprocity applies as given by Eqn. 2.3.102.2. MethodsA1F1−2 = A2F2−1 (2.3)Ak: area of surface k[m2]F1−2: the view factor of energy leaving surface 1 and incident onsurface 2For an enclosure analysis, the following view factor property also applies forN surfaces:N∑j=1Fk−j = 1 (2.4)View factors must be determined geometrically either by hand, based ontabulated standard relationships, or through ray-tracing.Thus, the net energy flux for a given surface k will be the balance of outgoingenergy Jk and incoming energy Gk:qk = Jk −Gk[Wm2](2.5)Where Jk and Gk are defined as:Jk = ϵσT4k + ρkGk (2.6)ρk: reflectivity of surface kGk =N∑j=1JjFk−j (2.7)Finally, for an enclosure with N surfaces, there will be N instances of thegeneral master net flux equation:N∑j=1(δkjϵj− Fk−j 1− ϵjϵj)QjAj=N∑j=1Fk−j σ(T 4k − T 4j)(2.8)112.2. MethodsWhere for an opaque, gray, diffuse, and uniform surface the following prop-erty relationships hold true:α = ϵ ; ϵ+ ρ = 1 (2.9)α: total surface absorptivityρ: total surface reflectivityFurther, the Kronecker delta δkj is defined as follows:δkj ={1 if k = j0 otherwise(2.10)To facilitate solving the master net flux equation for any enclosure regardlessof the number of surfaces, a Python script was used. The master net fluxequation can be solved by matrix math like so:N∑j=1akj qj = Ck (2.11)a: [N x N]C: [N x 1]Where akj and Ck are defined as follows:akj =1ϵ1− F1−1 (1−ϵ1)ϵ1 . . . F1−j(1−ϵj)ϵj.... . ....−Fk−1 (1−ϵ1)ϵ1 . . . 1ϵj − Fk−j(1−ϵj)ϵjCk =F1−1σ(T41 − T 41 ) . . . + F1−jσ(T 41 − T 4j )...Fk−1σ(T 4k − T 41 ) . . . + Fk−jσ(T 4k − T 4j )Thus the N net fluxes are solved as follows, and the cooling surface net fluxis extracted:122.2. Methodsq = a−1 · C (2.12)Sky Emissivity and TemperatureTo solve for the net flux radiation of the cooling surface, the cooling surfaceand sky IR emissivity and temperature are needed. The radiant coolingmaterial is given 2 graybody emissivities, one for solar wavelengths (short-wave) of roughly 280nm-4000nm (SW), the other is for further (longwave)IR wavelengths of 4000nm-70000nm (LW). The cooling surface IR emis-sivity is a manufacturer-provided property that was further verified withfield testing. However, for the sky emissivity, general empirical correlationsare required to generate estimates. This approach is regarded as spectrallyand directionally independent in terms of surface properties. The model inthis work uses the effective sky emissivity estimation and cloud correctionmethod developed by Clark and Allen [44]. This correlation was chosenbecause it allows for dynamic humidity dependence and is a widely usedcorrelation that a default within the industry standard EnergyPlus BEMsoftware developed by the US Department of Energy [50]. The calculationof dewpoint temperature based on the ambient temperature and relativehumidity (RH) was made with the Python library ’psychrolib’ [51].Although there are complex radiation interactions at play within the atmo-sphere, this model assumes the sky acts as a single surface with a tempera-ture equal to the ambient air temperature. The justification for this is thatthe majority of the IR radiation incoming from the atmosphere is producedby water vapor at low elevations where the temperature is near-ambient.Thus the effective ’blackbody’ sky temperature is calculated with Eqn. 2.13Tsky = (ϵsky−eff )1/4Ta [◦K] (2.13)Tsky: effective blackbody sky temperature [◦K]Ta: ambient air temperature [◦K]ϵsky−eff : effective sky emissivity, 0-1Adjusting IR Exchange with Sky and Surroundings for SurfaceTiltThe reason for this adaptation is that if the cooling surface is tilted awayfrom flat, it begins to ’see’ and interact radiatively with the ground sur-132.2. Methodsrounding it. For this model, the view factors must be adapted and a 3rdsurface added: the surrounding ’ground’. The view factor between the cool-ing surface and the infinite ’ground’ plane is now determined by Eqn. 2.14[49].Fpanel−ground =1− cos(Σ)2(2.14)Fpanel−ground: radiation view factor from cooling surface to ground [-]Σ: cooling surface tilt relative to ground [degrees or radians]Although not a part of the current model, a similar approach to this would betaken to include the radiative effects of surrounding buildings in a more com-plex urban environment. If this model was coupled with an existing BEMsoftware and the geometries defined, that software could feasibly provide theshading and view factors required to adjust for surrounding buildings.2.2.3 Solar Radiation FluxAny surface that is exposed to solar radiation during the day encounters sig-nificant heat pressure, and minimizing the solar heat ’gained’ by the surfaceis critical to a high performance sky-cooling surface. The second emissivityproperty of the cooling surface is the solar emissivity/reflectivity, which isalso provided by the manufacturer and can be verified with field testing.This property determines how much solar radiation is absorbed by the cool-ing surface. Thus, the absorbed solar radiation is calculated by Eqn. 2.15where αsolar = (1− ρsolar):qsolar = αsolar Isolar[Wm2](2.15)Isolar: total solar irradiance[Wm2]αsolar: cooling surface total solar absorptivity, 0-1ρsolar: cooling surface total solar reflectivity, 0-1Adjusting Solar Irradiance for Surface TiltIf the cooling surface is tilted relative to ground plane, Isolar is adaptedbased on the surface tilt and azimuth with the following equations. Thetotal solar irradiance is defined as:142.2. MethodsIsolar = GD +Gd +Gr = Direct + Diffuse + Reflected[Wm2](2.16)Where the components are defined as follows:GD = DNI · cos(θ)[Wm2](2.17)Gd = DHI · 1 + cos(Σ)2[Wm2](2.18)Gr = GHI · ρg 1− cos(Σ)2[Wm2](2.19)(2.20)DNI: Direct normal irradiance[Wm2]DHI: Direct horizontal irradiance[Wm2]GHI: Global horizontal irradiance[Wm2]ρg: solar ground reflectanceΣ: surface tilt angleθ: solar incidence angleDuring Python implementation, the tilt-adjusted solar loading was simpli-fied by using functions supplied in the library titled ’pvlib’ [52].2.2.4 Convective and Conductive FluxThe cooling surface will also be subjected to convective and conductive ’par-asitic’ heat exchange, which play an important role in determining the effec-tiveness of the surface in maintaing the lowest possible surface temperatures,and thus MRT for occupant comfort.152.2. MethodsTop ConvectionThis model contains a single convective flux term, defined by the tempera-ture difference between the panel surface and the ambient air and an averageconvective heat transfer coefficient in Eqn. 2.21.qconv = hc(Ta − Tcs)[Wm2](2.21)hc: average convective heat transfer coefficient[Wm2K]Tcs: cooling surface temperature [◦K]Ta: local ambient air temperature [◦K]Where hc can be approximated as done by Raman et al. [14] with a con-stant value derived from a representative numerical simulation of the setupwithout external wind using the FEA software COMSOL. Alternatively, thecoefficient can be experimentally derived for a specific apparatus as done byZhao et al. [53]. A field deployment includes changing wind speed and direc-tion and inconsistencies of the apparatus that can contribute to turbulenceand flow separation. Therefore, the conventional dimensionless coefficientslike Nusselt numbers or wind-tunnel based experiments are too sensitive tospecific conditions to be useful over a wider range of installations. Thus, theapproach used in this model is that the convective heat transfer coefficientfor a flat surface can be approximated by the linear correlation in Eqn. 2.22as done by Kumar 2010 [54] and Armstrong 2010 [55].hc = a+ bv[Wm2K](2.22)v: local wind speed[ms]a, b: linear fit coefficientsFitting field data to this relationship develops an application-specific correla-tion. Table 2.3 contains a list of relevant convective heat transfer coefficientrelationships used for flat plates. These correlations were used as comparisonpoints for the fitting done to the field data in this study.162.2. MethodsAuthor Correlation NotesZhao 2019 [53] hc ={2.5 + 2v shield8.3 + 2.5v no shieldHanif 2014 [42] hc = 3.1 + 4.1v when v = 0.1-2m/sEriksson 1982 [56] hc = 5.7 + 3.8vErell 2000 [57] hc = 1.8 + 3.8v when v = 1.35-4.5m/sAili 2019 [40] hc = 8.55 + 2.56vTable 2.3: convective heat transfer coefficient correlations.Ground ConductionBecause of the additional temperature sensor placed between the bottom ofthe insulation and building roofing pavers during the field test, it is possibleto add a specific conduction heat flux from below. The heat flux through thebottom 4” of rigid insulation (k=0.034) [58] was the most straightforward,with an assumed 2-D conduction profile between solids as shown in Eqn.2.23:qground =kL(Tground − Tcs)[Wm2](2.23)k: material conductivity[WmK]L: material thickness [meters]Tground: temperature at insulation bottom [◦C]Note: although the cooling panel test installation was built with a combi-nation of DuroSpan 250 XPS from PlastiFab and FOAMULAR NGX C-300XPS from Owens Corning, a constant thermal conductivity equal to theslightly lower performing DuroSpan was used.Sidewall Heat TransferThe heat flux through the cooling panel sidewall insulation is a complexinteraction but was deemed to be small in magnitude because of the thickinsulation and this not included in the model. Fig. 2.2 shows an approx-imate representation of the indirect conduction path from the ambient air172.3. Python Implementationto the panel surface as compared to the simple conduction on the bottom.To gather an indication of the heat flux absorbed by the apparatus, a sin-gle temperature sensor was placed on the eastern sidewall of the field testapparatus. However, since only a single sidewall temperature was measuredthe data only gives a partial indication of the heat transfer on the other 3sides and tops which have different orientations and thus solar loading andconvective behavior.Figure 2.2: Artistic representation of complex heat flow at sidewalls.2.3 Python ImplementationTo numerically process the equations developed to model the surface temper-atures of the RSC panel, a Python script was developed. Python was usedbecause of the simple implementation environment, flexibility, and access topowerful libraries for data processing, analysis, and visualization.2.3.1 Heat Balance Equation SolutionTo solve the heat balance equations 2.1, a binary search algorithm performswell. This uses the ambient temperature as an initial guess and a ± specifiedtolerance as the initial boundaries, then depending on the sign of the heatbalance result, narrows the search grid by making that guess one of the newboundaries. This process continues to iterate until the calculated net heatflux is within a specified tolerance of zero, usually ± 0.5 Wm2.18Chapter 3Cooling Panel Field TestingThe goal of this chapter is to design, build, and field test an RSC panel usinga commercially available RSC product. This field testing develops data for 2main uses: 1) the assessment of panel performance in an applied setting and2) calibrating the thermal model. The field test process also helps gain anunderstanding of the practical aspects of designing and constructing a work-ing cooling panel. This chapter reviews relevant literature for field testing ofradiant sky-cooling devices, and explains the methods for the experimentaldesign, data collection, and construction of the test apparatus. Key resultsfrom the summer of 2024 test period using the Radi-Cool commercial filmas an RSC surface are presented and analyzed. Performance of the panel ismeasured by the sub-ambient temperature difference, defined as the panelsurface temperature minus the local ambient air temperature.3.1 Literature ReviewA field test to verify real-world performance and validate and/or calibratethermal models is a common feature of the RSC material development stud-ies identified during the literature review. Techniques like FTIR can de-velop spectral material properties, but exposing the material to environ-mental conditions can confirm expected performance and give additionalinsight into applied conditions such as wind-driven convection. The fieldtests reviewed in this work fall into two general categories: 1) hyper-isolatedmaterial verification and 2) applied performance testing.In material-specific testing such as the works by Li et al. [17], Raman etal. [14], Liu et al. [59], and Kou et al. [60] each have slightly different testsetups but the main goals remain consistent. The primary objective is toisolate the small material sample from convective and conductive losses, sothe the sub-ambient cooling potential is maximized and also to reduce theinfluence of these variables in the modelling. This is usually done with a193.2. Methodscombination of transparent polyethylene wind covers for suppressing con-vection, and rigid insulation and suspending the material sample to limitconduction. This can even be taken to extremes with vacuum isolation asdone by Chen et al. [61] to achieve a maximum of 42 ◦C below ambienttemperature. Some materials testing like a study by Li et al. [17] utilized aprogrammed heating device to raise the surface temperature to ambient soparasitic losses were reduced to near zero. This helps to isolate the radiantthermal exchange and characterizes the cooling power in place of the sub-ambient performance. Kou et al. [60] also used a heating device with anequivalent test piece but with minimized radiation exchange through highreflectivity in all wavelengths to experimentally estimate the average par-asitic loss coefficient of the test apparatus. Another feature of these fieldtests is reducing the solar gains of the test apparatus itself, either by usingcoatings or coverings that are highly solar reflective like aluminized Mylaror highly solar transparent materials like high clarity acrylic.Applied testing is oriented toward proof-of-concept with the implementationof an RSC surface. Zhao et al. [53] and Aili et al. [40] both demonstrate aworking scale deployment of RSC fluid cooling panels with meaningful sur-face areas and prototype designs that aim to inform commercial production.Most field setups in both material verification and applied testing are onroof tops to maximize the sky view factor of the RSC material, with testsite security as an additional benefit. To help contextualize results, fieldtesting will also often have comparison materials in identical setups. Theseare usually a ’control’ surface of nominal properties like bare metal, and aconventionally reflective material or coating like a standard white paint. Allfield testing will typically collect these primary local variables: RSC surfacetemperature, ambient air temperature, ambient RH, total solar irradiance,and wind speed.3.2 Methods3.2.1 Experimental SetupLocationThe cooling panel experiments were all conducted on the roof of the Centrefor Interactive Research on Sustainability (CIRS) building on the Vancouvercampus of the University of British Columbia (UBC). Locations relevant tothe field testing are shown in Fig 3.1. This location was chosen for the203.2. Methodsfollowing reasons:1) Sky view factor - placing the cooling panel on the roof simplified themodelling by allowing an almost unobstructed ’view’ of the sky.2) Access - the roof of the building is a controlled-access location only ashort walk from the BDRG offices, allowing for security of the test setupand quick access for monitoring and setup changes.3) Availability - the roof access was granted easily and without significantdelay, which is a barrier on a campus as dense and highly trafficked as UBC.A note on spatial scales: in the discussion surrounding the field studies,’local’ data and conditions refers to immediate area surrounding a givensensor, and ’regional’ refers to the general area of the testing (UBC - Van-couver campus).Figure 3.1: Locations of region and test sites with true north indicated forreference.213.2. MethodsFigure 3.2: CIRS rooftop testing location viewed from 2 perspectives.Cooling Panel ConstructionFor this RSC application investigation the goal was to move beyond thestandard material science approach of testing a small very well isolated ex-perimental wafer to instead build a cooling panel prototype with scalabilityand durability as objectives. Radi-Cool RSC film is a silver-backed polymerembedded with glass micro-beads and is produced as a self-adhesive producton a roll. It has an advertised solar reflectivity of 0.91 and IR emissivityof 0.93. The Radi-Cool RSC film was chosen as the cooling surface for thepanel because it was the only known commercially available RSC material ata useful scale and had a short lead time. Before this product was discoveredthere was an investigation into duplicating the work by Li et al. at Purdue[17] and synthesizing a custom ultra-white paint, but this was ultimately notsuccessful and details are available in Appendix C. The size of the coolingpanel was determined by the Radi-Cool film, which was available in pre-cut4’x4’ pieces, and by mobility concerns surrounding the overall weight of thepanel and its ability to easily fit through doorways. This 4’x4’ size makes thecooling panel much larger than a standard experimental wafer, but smallerthan a full-size applied cooling structure roof capable of shading multipleoccupants. The frame construction and final panel are shown in Fig. 3.3The Radi-Cool film was self-adhesive and rolled out by hand onto a thor-oughly cleaned 18 gauge 5052 aluminum sheet. This substrate was chosendue to its mixture of rigidity and lightweight while being strongly thermallyconductive to aid in developing a more uniform temperature across the panel223.2. MethodsFigure 3.3: (left) the construction of an insulated panel frame piece (right)the completed cooling panel with HOBO surface temperature probes in-stalled.and throughout the depth. The ’underside’ of the aluminum web was cov-ered in the Radi-Cool ’membrane’, a thicker butyl-rubber backed productwith similar properties to the film but easier to install and better for moreuneven surfaces and given with the film at no extra cost. The original in-tention was that because of the high IR emissivity of the Radi-Cool productthis would serve as a convenient underside for a suspended application. Aninsulated ’sandwich’ frame was built out of 1/2” plywood and 2.5” widerigid insulation, with a depth of 2”. The rigid insulation acted to limit theconductive heat flows through the side perimeter. 2 of these frames werescrewed together by plywood brackets to sandwich the 4’x4’ cooling surfacewith double-sided VHB tape on one side and a sealing neoprene gasket onthe other around the perimeter, at a 3/4” indentation as described in Fig3.4. This insulated frame was then painted pure white to help limit solarradiation absorption. This insulated frame not only provided the structurenecessary to support the thin cooling surface, but also created a ’lip’ onthe panel perimeter than gave some protection from wind-based convectivelosses and ambient IR irradiance.Baseline ModificationsTo further decrease undesirable heat transfer through the sides and bottomof the apparatus during calibration trials, an extra 2” of rigid insulationwas added to the bottom and sides, and another 1” was added to the topof the sidewalls. An aluminized Mylar sheathing was then taped on top ofthis insulation to minimize solar gains on the apparatus that would decreasethe overall cooling performance and introduce additional heat flows. These233.2. MethodsFigure 3.4: A not to scale sectional representation of the cooling panelperimeter structure.modifications to the core cooling panel are shown in Fig. 3.5Instrumentation and Data CollectionProper capture of the most relevant variables was critical to the successof the field trial. A standard averaging+logging interval of 5 minutes waschosen to provide a balance of higher-than-normal resolution in terms ofbuilding energy simulations, that typically calculate at 1 hour intervals, butnot so resolved as to create an unnecessarily large dataset. The intentionof the field test was not a fine-grained thermal study with a large dynamiccomponent but rather a multi-day and week exploration of the performanceof an architectural feature. The ESB (Earth Science Building) weather datacould not be acquired at 5 minutes, and so was downsampled to 5 minuteintervals for modelling. The Particle instrument measurements were setto 15 second intervals because there were no data storage constraints in theInfluxDB database. Further, it was larger than the SHT35 2 second responsetime for temperature and 8 seconds for RH, and because they were also usedfor the PCM lab cycling. This data was averaged to 5 minute intervals forRSC modelling.Regional weather data was collected with a Davis VantagePro weather sta-tion at 15 minute averages, operated by the UBCWeather Forecast ResearchTeam. The key variables of interest were total (direct+diffuse) normal solar243.2. MethodsFigure 3.5: (left) The extra insulation that was added to the basic coolingpanel to help minimize unwanted conduction and convection heat flow fromthe bottom and sides. (right) The aluminized Mylar sheathing that wasadded to minimize solar gains on the apparatus.irradiance[Wm2], ambient dry-bulb temperature [◦C], RH [%], and averagewind speed [m/s]. Regional wind direction was available but not used.Measurement Type Range Accuracy (Precision)Solar Irradiance silicone photodiode 1-1800[Wm2] ±5% (1[Wm2])Ambient Temp PN Junction Si Diode -40-65◦C ±0.31(0.1) ◦CRH film capacitor 1-100% ±2 (1) %Wind Speed solid-state magnetic 0-89m/s ±5% or ±0.9 (0.4)m/s1 Ambient temperature has a +0.3◦C additional error at 1040 Wm2with an aspirated solar radi-ation shield.Table 3.1: ESB Davis VantagePro specifications.Panel surface temperatures were collected with Onset HOBO commercialtemperature probes at 5 minute intervals, logging to the onboard DAQ underbattery power. 4 of theses probes were spaced evenly across the panel to geta good surface average but sufficiently away from the perimeter to minimizeboundary effects. The probes were sandwiched between the aluminum panelsubstrate and the underside Radi-Cool butyl membrane with copper tapeand thermal paste to maximize the accuracy of the reading. The positioningof the 4 HOBO panel surface temperature probes and 2 SHT35 perimetersurface temperature probes is demonstrated in Fig 3.6.Other associated temperatures and RH throughout iterations of the field253.2. Methodstesting including ambient air temperature and RH, sidewall temperature,ground temperature, and PCM temperature were measured with SHT35probes paired with Particle Photon loggers at 15 second data intervals.These probes gave excellent accuracy and response time but their mostvaluable experimental feature was the Wifi-based data capture uploadedto an InfluxDB server and displayed for visualization on a Grafana dash-board. This feature made the data nearly impossible to misplace during adownload, and more importantly allowed for off-site observation of the datastreams, allowing for quick diagnosis of sensor or setup problems even whena member of the team was not able to physically check on the test setup.This proved to be invaluable, however the simplicity and resiliency of thecommercial products was excellent and they performed well. Although tem-perature sensing accuracy was an important data consideration, the overallcomplexity of the heat transfer and sensitivity of the experimental setupto environmental factors dictated that the sensors did not require fully in-volved calibration procedures. To ensure the general function of the SHT35and HOBO sensors within the manufacturers’ specifications they were leftto achieve steady state while all sandwiched next to each other in thermalisolation and checked to make sure temperature readings were all within0.05◦C of one another, which they achieved.The installation of the ambient temperature sensing SHT35 on the CIRS roofrequired additional attention due to the error produced by the absorption ofsolar radiation. An accurate local ambient temperature measurement wasimportant because the air temperature immediately surrounding the cool-ing panel is used in the key sub-ambient sky cooling performance metric:sub-ambient cooling temperature delta. This data point was a must-havebecause the regional temperature had larger averaging periods and was de-signed to measure a temperature that was not effected by local heating ofthe building roof, which is the exact condition the cooling panel was beingsubjected to. To shade the sensor at any solar angle yet allow sufficient freeairflow to prevent stagnation and local heating, an oversized 2-piece insu-lated and Mylar covered housing was built right next to and slightly abovethe cooling panel as seen in Fig. 3.7.Wind speed was critical to capture for calibration, based on the literature’sidentification of forced convection playing a significant role in reducing theeffectiveness of RSC. Although regional wind speed was available throughthe ESB weather station, a hyper-local measurement was deemed necessarybecause of the difference in height between locations and the local features ofthe CIRS roof that would have an effect on air flow. For the CIRS roof, the263.2. Methodsmajor feature that could effect wind flow is the 1m tall perimeter wall. Giventhe sensitivity of thermal comfort metrics and the convective heat flows towind speed, it was decided that a high accuracy wind speed measurementwas needed, with a range that could drop below the standard 0.5m/s lowerlimit on conventional wind speed instruments. The TSI VelociCalc 9555combined air speed/temperature/RH instrument was chosen due to its highaccuracy, combined measurement ability, availability, and commercial cali-bration. The instrument was obtained from UBC SPPH’s Occupational andEnvironmental Health Lab after consultation with their lab experts. Whileon the CIRS roof, the extendable probe was mounted at a corner of thecooling panel and raised to a height of 30cm above the cooling surface.This height was chosen to balance capturing the local free-flow air speedwithout being too affected by the panel structure or the heating of the roofpavers, while not extending too far away from the panel. While exposedto direct sunlight on the CIRS roof the temperature and RH measurementscould not be used due to over-estimation errors produced by the absorptionof solar radiation. The probe has an integrated DAQ powered by either ACwall power, and logged at 5 minute intervals. The one aspect of concernto note with regards to sensor accuracy is the protective shroud around thesensor element, as explained in Appendix D. Local wind direction was notcaptured.Measurement Type Range Accuracy PrecisionHOBO Temp n/a -40-100◦C ±0.15◦C 0.002◦CSHT35 Temp n/a -40-125◦C ±0.1◦C 0.01◦CSHT35 RH n/a 0-100% ±1.5% 0.1%VelociCalc Temp n/a -18-98◦C ±0.3◦C 0.1◦CVelociCalc RH n/a 0-95% ±3.0% 0.1%VelociCalc Wind hot-wire 0-30m/s ±3% or ±0.015m/s 0.01m/sTable 3.2: Assorted sensor specifications.Mean radiant temperature (MRT) was an important indirect measurementfor this work for the thermal comfort performance of the tree canopy. Thestandard simple and low-cost method for MRT measurements is the black273.2. Methodsglobe temperature, however there can be errors introduced by free convec-tion and that method provides only a single average temperature [62]. Toachieve better measurement resolution and improve accuracy, the ’CUBE’MRT sensor from CHAOSense Inc. was employed. This product uses 8directional Melexis thermopiles with IR filters to generate surface temper-atures within each cone field of view, assuming blackbody behavior of sur-rounding surfaces. The manufacturer gives a full spherical MRT accuracyof ±0.3◦C and range of -40 - 70◦C. Data is directed to the same InfluxDBserver and Grafana visualization dashboard, also using a Particle Photonwith Wifi connection, and is powered via an AC adapter.283.2. MethodsFigure 3.6: (top) The approximate location of the cooling panel temperaturesensors in a plan view, not to scale. (bottom) The approximate locations ofthe cooling panel temperature sensors in a section view.293.2. MethodsFigure 3.7: (left) The solar-reflective shelter built to house the ambient airtemperature and RH sensor to minimize the effect of solar loading (right)A close-up view of this sensor, showing the concealment for all solar angleswhile maintaining good air flow.303.3. Results3.2.2 Baseline TrialFor a baseline performance analysis of the Radi-Cool RSC surface and thecooling panel design, a fully insulated and flat setup was used, as detailed inSec. 3.2.1. The best performing panel data with no additional PCM for thiswas from July 20. The best data that included a PCM was from August 03and August 10. Further exploration of this baseline data is included in themodel calibration section 4.3.2.3 Mylar Comparison TrialTo demonstrate the special performance of a dedicated RSC surface in com-parison to a conventionally solar reflective surface, 50% of the cooling panelwas covered with aluminized Mylar. This was done for a 24 hour periodon a mostly clearsky, relatively hot day in mid August with the flat, fullyinsulated baseline configuration with no convective shield. The Mylar sheetwas adhered to the half of the cooling panel that contained the PCM testsample as shown in Fig 3.8. This hybrid setup was chosen due to the conve-nience of a simple modification as compared with building an entire secondcooling panel, and the benefit of an exact side-by-side comparison of the 2surfaces subjected to almost identical conditions. This data was not usedfor modelling or model calibration.3.3 Results3.3.1 Baseline TrialData from the average of the 4 HOBO panel temperature probes, localambient temperature, ground temperature, sidewall temperature, and solarirradiance for July 20 are plotted in Fig. 3.9. Local wind speed for July20 is plotted in Fig. 3.10. Additional data for other days can be found inAppendix F.Numerical performance results for sub-ambient temperature delta (∆T ) indegrees for the 3 baseline days are displayed in Table 3.3, with negativevalues indicating the panel temperature is below ambient air temperature.This temperature delta is described by the equation ∆T = TCS − Ta.313.3. ResultsFigure 3.8: Half of the cooling panel was covered with aluminized Mylarmaterial.Figure 3.9: A comparison of cooling panel surface temps against local am-bient temperature and regional solar irradiance on July 20.323.3. ResultsFigure 3.10: A comparison of local ambient air temperature and averagepanel temperature with local wind speed on July 20.Date ∆T ∆T daytime ∆Tsolar,peak ∆Tbest ∆TworstJuly 20 -6.5 -5.1 -1.7 -14.1 0.1August 03 -3.8 -2.3 0.4 -8.9 1.3August 10 -3.4 -2.3 0.2 -6.7 0.7∆T = average sub-ambient temp delta.∆T daytime = average temp delta during solar irradiance.∆Tsolar,peak = temp delta at peak solar irradiance.∆Tbest = the best (most negative) temp delta over the full day.∆Tworst = the worst (least negative) temp delta over the full day.Table 3.3: Baseline panel performance sub-ambient temperature results [◦C].333.4. Discussion3.3.2 Mylar Comparison TrialResults from the 24hr. covering August 15 and 16 Mylar comparison areplotted in Fig. 3.11. The panel surface temperature data from the PCMHOBO probe is also plotted for reference, but PCM performance is exploredprimarily in Cha 5.Figure 3.11: A comparison of Mylar surface temperatures both with andwithout the PCM, against the Radi-Cool surface Aug 15-16.3.4 Discussion3.4.1 Baseline TrialThe baseline performance results for the basic RSC panel with no PCMshow that the panel struggles to achieve sub-ambient temperatures duringthe daytime. The performance also reduces between July 20 and August 03and 10. This could be from a poor panel design that does not adequatelyprotect the cooling surface from parasitic convection and solar gains. Thesidewall temperature shown in Fig. 3.9 shows that the outer surface ofthe sidewall is indeed heating up due to solar gains, and while the heavy343.4. Discussioninsulation of the cooling surface is suppressing this heat flux, it could stillbe reducing performance. These results could also be simply the realisticperformance of an applied RSC panel using a material with low-performancesolar reflectivity of 0.91, in comparison to the more advanced RSC materialslike the ultra-white paint from Li et al. [17] with a solar reflectivity of 0.97.Also of note is the rapid increase in sub-ambient performance past 1˜4:00when the solar irradiance begins to reduce. This highlights that reflectingsolar irradiance is key to RSC and that the OTC performance of the panelis highly dependant on the time of day identified. While the local ambientair temperature remains high long into the evening, the high emissivity andlow mass of panel allow it to rapidly cool.An unusual feature that was noted is the large panel temperature oscilla-tions at night. The local wind data plotted in Fig. 3.10 shows a generalcorrelation between wind spikes and temperature oscillations. This wouldmake sense as during the night the lack of solar irradiance produces a largertemperature difference between the cooling surface and ambient air. This inturn bring the convective heat transfer, which is a function of this tempera-ture difference and wind speed, to greater prominence in the heat balance.Another possible factor is that the cooling surface drops below the dewpoint,causing water to form on the surface, as noted when the test site was visitedin the early morning. If this moisture is undergoing a cycle of evapora-tion and condensation, that is also affected by wind speed, this latent heattransfer could be affecting the panel surface temperatures. Although thenighttime oscillations are intriguing, the focus for outdoor thermal comfortis the daytime, so these were not investigated in greater detail.3.4.2 Mylar Comparison TrialThe results show a notable surface temperature difference between the Radi-Cool and Mylar coatings, with the non-PCM Mylar 6 degrees higher atpeak solar irradiance. This test also showcases the performance enhancingproperties of the PCM mass with the PCM Mylar 3 degrees lower than thenon-PCM at peak solar irradiance. However, these results are impacted bythe hybrid test setup, with the conductive aluminum substrate presumablydrawing all 4 temperatures toward each other slightly. The Radi-Cool is seenunable to maintain the same sub-ambient temperatures observed earlier inthe field test period, possibly due to this conduction through the substratebut likely also connected to the possible decrease in solar reflectivity exploredin Cha. 4.35Chapter 4Calibrating the ThermalModelsThe goal of this chapter is to test and calibrate the predictive ability ofthe thermal model developed in Chapter 2. This chapter will cover thecalibration efforts engaged to match the theoretical thermal model withthe field testing data obtained during the summer of 2024 for a prototypecooling panel, including a literature review of calibration approaches. Thepurpose of the model calibration was two-fold: 1) helping explain the ther-mal behavior of the RSC panel by identifying the relative contribution ofthe constituent thermal heat flows and 2) determining the potential of thethermal model to assess the RSC panel performance under conceptual con-ditions. A thermal model calibrated to field data is important because ofthe many complexities introduced by a field application as compared to amore controlled lab setting. Further, a field-calibrated thermal model ofreasonable accuracy (< 1◦C) can be used to assess design choices such ascooling surface material emissivity, panel orientation and tilt, and the effectsof climate-specific conditions such as expected ambient temperature, cloudcover, and relative humidity. The success of the calibration process wasmeasured by the average and maximum error between the known and pre-dicted panel temperature in addition to parameter variation between daysand the general fit of the model predictions to the known data.4.1 Literature ReviewMathematical modelling of a physical process is frequently paired with cali-bration to increase the accuracy and clarify or reduce uncertainty in modelparameters and is a wide field of study. Calibration is defined for this workas the selection of model parameters such that the model output matches ob-served data as closely as possible. There exist many pathways to successfulcalibration of a model, depending on the process goals and problem context,364.2. Methodsand the choice relies on professional judgment as much as any other factor.Calibration and the characterization of the parameter error behavior andrelationship to observations is challenging and can easily lead to overtuningand an optimistic assessment of the predictive accuracy of the simulation[63]. Calibration can be approached through Bayesian statistical inference[64] [65] that combines calibration simultaneously with uncertainty analy-sis, implicit calibration using genetic algorithms for parameter iteration [66][67], or explicit calibration using mathematically defined guess-and-checkanalytics [68] to name some. Some other possible components of the simu-lation calibration process are a parameter sensitivity analysis to determinethe parameters with the most significant effect on model results as done bySheppard [69] based on the Morris Method [70] and a parameter identifia-bility analysis to investigate the ’uniqueness of solution’ [71].4.2 MethodsPython was leveraged to calibrate the thermal models, using the most basicguess-and-check method available with static parameters. This approachwas chosen due the small number of parameters, the computational sim-plicity and speed of the model, and the research goal of creating just apreliminary design tool. A more complex calibration approach would not beuseful while the underlying thermal model remained coarse and the uncer-tainties associated the field testing would overshadow any accuracy gains.The input parameters of interest were passed to functions from the Pythonlibrary Optuna with expected value ranges [72]. Optuna then created asmany parameter combinations (’trials’) as specified by the user; a matrixof possibilities that were assessed concurrently with no feedback loop. Forall calibrations this ntrials was set to 1000, after a sensitivity analysis de-termined enlarging the number of trials further did not change the resultsappreciably. The objective function used to assess the performance of a giventrial was the standard least squared error between the model-predicted paneltemperature and the observed panel temperature from the average of theHOBO probes as shown in Fig 4.1.LSE =n∑i=1(yi − yˆi)2 (4.1)yi: predicted panel temperature at time i [◦C]374.2. Methodsyˆi: observed panel temperature at time i [◦C]The trial with the lowest LSE was determined to be the model parametervalues set that best predicted that data range. The calibration parame-ters and ranges given to the procedure were varied to test the effect on thecalibration procedure and results. The top 5% of trial parameter combi-nations were averaged for each day as done by Sheppard [69]. To assessthe flexibility of the calibration, the distribution of the calculated parame-ters between days was analyzed, but a true train-test split of the data wasnot implemented due to the limited data. In this way, the more consistentthe calculated parameters are, the more generalizable the calibrated modelcould be.It was determined that due to the already simplified mathematical modelwith a small number of parameters, an in depth sensitivity analysis wasnot required for the purpose of choosing the most important parameters toadjust, as done by Sheppard [69]. However, the magnitude of the modelledparticipating heat flows was recorded to assess the relative contribution, andthus importance, of each component. All measured data streams for environ-mental variables such as ambient temperature and local wind speed were notparameterized. Model parameters and their assumptions are summarized inTable 4.1.For all calibrations, the parameter ranges were set according to Table 4.2.These ranges were assigned based on the literature review of convectionparameters and the Radi-Cool manufacturers’ reported material properties,plus a generous margin. As long as the calibration results did not approachany range limits, they were considered to be acceptable and not expanded.For all trials and calibrations, the dewpoint temperature used as an input forthe Clark-Allen effective sky temperature estimation was calculated usingthe regional RH and ambient dry-bulb temperature from the ESB weatherstation.Lastly, although the data captured during the field testing is time-varying,because the data frequency is 5 minutes and the panel is considered ’lightweight’,to simplify the modelling and calibration the panel was considered to havezero thermal mass. Therefore, the model is not truly dynamic; rather it isa series of steady state equilibrium points over time.384.2. MethodsParameter Definition Assumptionρsolar Radi-Cool solar reflectivity calibration parameterϵIR Radi-Cool IR emissivity calibration parameter1a linear constant for convec-tive coefficientcalibration parameterb linear wind-dependent con-stant for convective coeffi-cientcalibration parameterv wind speed field dataTpanel average panel temperature field dataTamb,local local air temperature field dataTsky effective sky temperature estimationTsidewall sidewall external tempera-turefield dataTground temperature under appara-tus centerfield dataTsurround surrounding roof tempera-tureestimationIsolar total normal solar irradi-ancefield dataRHlocal local relative humidity field dataRHregional regional relative humidity field data1 : calibrated with nighttime data then held static.Table 4.1: Basic model parameter definitions and assumptions.394.2. MethodsParameter RangeϵIR 0.85-0.95ρsolar 0.80-0.95a 0-15b 0-15Table 4.2: Calibration parameter ranges.404.2. Methods4.2.1 Baseline TrialThe most essential field data collected was for the baseline calibration of thethermal models was the flat, fully insulated trial with no convective barrierand the best possible sky view on a clear day. This setup allowed the minimalnumber of variables and prioritized the assessment of the cooling surface’sinteraction with the Sun and atmosphere.Throughout the field testing period, 3 days were selected for calibrating themodel based on matching the criteria of 1) clear sky with minimal or nocloud or haze 2) as hot as possible, ideally >25◦C at peak and 3) all criticalsensors functional with reliable data. These days were July 20, August 3,and August 10.4.2.2 Cloud Cover TrialAn important weather condition to address for modelling the cooling panel iscloud cover. This is a conventional component of energy modelling weatherfiles, and a model that can account with reasonable accuracy for cloud coveris more flexible and of more use to designers. Cloud cover is also a commonweather condition, even in summer, and the water content in clouds impactsthe IR radiation exchange with the cooling surface and can also attenuatesolar radiation. During the 27 day fully insulated field testing period, 14days had some mixture of cloud cover. Out of these days, the most completedata was collected for July 22 and 24 and August 12. The cloud cover wasvariable over July 22/24, which is useful as it forces the model to be exposedto a wider range of cloud cover for calibration. August 12 provided data for auniformly cloudy day as a benchmark condition. This evolution of the modelcalibration is only valid for daytime cloud cover during the time period of0700-1700. Data limits on August 12 resulted in a slightly shorter timerange of 0935-1830. The ρsolar and ϵIR model parameters were isolated forthis calibration procedure, and given the values determined during clearskycalibration. This was determined to be an ϵIR of 0.93 for all days, a ρsolarof 0.90 for July 22/24 and a ρsolar of 0.88 for August 12.The cloud cover fraction was a post-calculated quantitative estimation basedon the ratio of a cloudy day’s regional solar irradiance to that of a nearbyclearsky day’s. Thus, if for a given time interval on a cloudy day the solarirradiance matched or exceeded the clearsky irradiance for the same timeinterval, that would be an ncloud value of 0. Conversely, the ncloud valueof 10 was defined as the ratio of peak (13:15) cloudy irradiance for August414.2. Methods12 to the peak clearsky irradiance at that same time for August 10. Theclearsky irradiance comparison data used for July 22 and 24 was July 20.Cloud cover fraction was visually estimated during the testing period withfish-eye images for reference, but this method had data gaps and was notconsistent, and thus not used.4.2.3 Surface Tilt TrialAlthough accounting for surface tilt is an important design consideration, itwas not a primary focus of the field trial. Thus, only 12 hours of data and onesurface tilt angle and azimuth orientation are available, on September 06.Nevertheless, this clearsky day offers the opportunity to assess the ability ofthe model to adapt to a surface that is not flat. For example, this variationwould allow designers to determine the performance impacts of installing acooling panel at an angle that would allow for easy rain drainage, or as avertical wall. Furthermore, the cooling surface orientation (azimuth) can beleveraged to increase performance by minimizing solar loading, generally byorienting the surface away from the dominant sun path in a given location,especially at higher latitudes. Additionally, the same calculation principlesinvolved in accounting for surface tilt can be adapted to account for thepresence of surrounding buildings or other objects that would interact withthe cooling surface in the longwave, which is critical in a denser urbancontext.For September 06, there was no Grafana temperature probe data availablefor the local ambient or sidewall temperature. Moreover, while the panel wastilted the bottom of the apparatus was exposed to the ambient air. There-fore, the typical ’ground’ and sidewall temperatures were both assigned thelocal ambient air temperature as a compromise. The convection heat fluxon the underside and the reflected solar radiation and ambient IR radiationexchange for the bottom of the panel were not explicitly accounted for. Forthe local ambient air temperature, neither the regional ambient air tem-perature from the ESB weather station nor the solar-affected local ambienttemperature provided by the TSI VelociCalc were suitable, as previous dis-cussed. Therefore, a placeholder estimation between the two was used, asexplained in Appendix section A.3.For the tilted panel’s IR radiation exchange with the surrounding roof, thesurface was given a gray-body emissivity of 0.8, roughly consistent withthe concrete that makes up the majority of the roof surface. IntermittentFLIR IR camera measurements during field testing revealed that the rest of424.3. Resultsthe roof was highly susceptible to solar warming and could achieve surfacetemperatures as high as 40◦C during the day. The roof surface temperaturewas not directly measured for September 06, but because the panel tilt,and thus the radiation exchange view factor between roof and panel surface,was not large, an approximation was used. This approximation was theTSI VelociCalc temperature plus 5 degrees, given that the VelociCalc probeis known to be sun-affected so the daily profile would be similar, but theconcrete is assumed to absorb and store heat more readily. The panel tiltwas measured to be 14 degrees, and the panel azimuth was 60 degrees (truenorth = 0◦, east = 90◦). This surface tilt could be reasonable for a roofslope.4.3 Results4.3.1 Baseline TrialA key result of the model calibration procedure was determining if the modelparameters for solar reflectivity and IR emissivity would match the manufac-turer’s specifications in an applied setting. Firstly, the IR emissivity of thepanel was calibrated using only the nighttime data, since the solar reflectiv-ity could be eliminated in the absence of solar irradiance. The IR emissivitycould then be confirmed and held static during calibration of the full 24 hourdata cycle. Calibration for all 3 days demonstrated that the IR emissivitywas approximately 0.93, which is the longwave emissivity specified for theRadi-Cool film and was an excellent confirmation. The full-day calibrationresults for all 3 baseline days are listed in Table 4.3 and an example of themodelled temperature and heat fluxes for July 20 are shown in Fig. 4.1.Further data is available in Appendix F434.3. ResultsFigure 4.1: (top) A comparison of the model predicted panel temperaturesand the known (non-PCM) panel temperatures for July 20 as an example ofthe calibration results. (bottom) The accompanying modelled heat fluxes.444.3. ResultsSource ρsolar ϵIR a[Wm2K]b[Wsm3K]e [◦C] emax [◦C]July 20 0.90 0.93 1.0 1.9 0.7 3.1Aug 03 0.88 0.93 2.9 4.8 0.5 1.9Aug 10 0.88 0.93 4.6 3.8 0.4 1.3Average1 0.89 0.93 2.8 3.5 1.1 4.51 The ’average’ error results are the average results of the average modelparameters applied against all 3 dates.Table 4.3: Calibration model parameter results at ntrials = 1000 for eachflat, fully insulated with no convection shield test day with the correspondingaverage and maximum error. Radi-Cool advertised properties are ρsolar =0.91, ϵIR = 0.93.454.3. Results4.3.2 Cloud Cover TrialAs an example of variable but light cloud cover, results from July 22 areplotted in Fig. 4.2. For comparison, the heavily and uniformly clouded datafrom August 12 is plotted in Fig. 4.3. Calibration results for the convectioncoefficient are shown in Table 4.4.Figure 4.2: A comparison of the basic model predicted panel temperaturesand the known panel temperatures with irradiance ratio cloud cover correc-tion for July 22.464.3. ResultsFigure 4.3: A comparison of the basic model predicted panel temperaturesand the known panel temperatures with a comparison of clearsky and fullycloudy sky temperature estimations for August 12.Date a[Wm2K]b[Wsm3K]e [◦C] emax [◦C ]July 22 5.5 0.1 0.8 2.2July 24 6.1 0.2 0.8 2.7Aug 12 8.6 0.1 0.2 0.5Table 4.4: The results for July 22/24 and August 12 individually for theaverage of the top 5% of parameter combinations for ntrials = 1000.474.4. Discussion4.3.3 Surface Tilt TrialDate ρsolar a[Wm2K]b[Wsm3K]e [◦C] emax [◦C]Tilt-adjusted 0.85 2.0 4.4 0.36 1.1No adjustment ” ” ” 0.79 2.2Table 4.5: The results for Sept 06 11:55am-11:55pm comparing the tilt ad-justment and no tilt adjustment for the average of the top 5% of parametercombinations with ntrials = 1000.4.4 Discussion4.4.1 Baseline TrialThese results show that the basic model has an acceptable level of averageaccuracy/error, allowing reasonable prediction of panel temperatures forthe purpose of energy modelling or thermal design. Additionally, theseconvection coefficient parameter results should be considered valid for localwind speeds of between 0 and 1.5 m/s, as that is the range on which themodel was calibrated.It is interesting to note that the calibrated solar reflectivity drops from 0.90on July 20, which is close the manufacturer’s specification of 0.91, to 0.88on Aug 03 and 10. A lower than expected solar reflectivity could be a resultof the solar heating of the apparatus and thus the model attributing thisadditional energy as directly absorbed by the panel. Further, the drop insolar reflectivity over time might in part be caused by fouling of the filmsurface. Keeping the cooling panel surface clean during the testing periodwas challenging, as dust from nearby sanding work, wind-carried debris,and bird droppings were continually deposited. If true, this would pose adistinct applications problem for this technology, as the solar reflectivity ofthe surface is of paramount importance, with every percent counting. Onthe positive side, cooling for outdoor thermal comfort in more moderateclimates is generally only needed in the 2-4 hottest months of the year,making maintenance of a cooling surface a seasonal and not year-roundissue in those situations.The convective coefficient parameters a and b are relatively consistent with484.4. DiscussionFigure 4.4: A comparison of the basic model predicted panel temperaturesand the known panel temperatures for a tilted panel on Sept 06.each other and with the literature, shown in Table 2.3. There is considerablerange in these correlations depending on the test configuration and condi-tions, but these field test calibration results appear to be within reasonablelimits. The range in these coefficients underlines the difficulty in accuratelypredicting the convective heat transfer for a specific design over a range ofenvironmental conditions.The modelled heat fluxes shown in Fig 4.1 demonstrate that the dominantheat flows are radiative. Convection plays a secondary but important role,especially at night when the temperature difference with the ambient air issignificantly higher. Conduction through the bottom is shown to be smallbut not entirely negligible.4.4.2 Cloud Cover TrialThe convective heat transfer correlation coefficients determined for the cloudcover trial are quite different from those developed for baseline clearsky cali-bration, featuring essentially 0 dependence on the wind speed, despite being494.4. Discussionsubjected to similar wind conditions. However, they are relatively consis-tent with one another across all 3 days,, which is good. It is likely thatthe calibration procedure is sensitive to the time range, and the cloud covertrial was calibrated over daytime instead of a full 24 hour cycle. Whatthis means is that under consistent and low wind conditions, without thenighttime surface temperature cycles, the calibration procedure is not subtleenough to recognize the difference between wind-driven and baseline convec-tion. Thus, the linear fit parameters that determine the overall convectivecoefficient have multiple independent solutions. However, when examiningthe mean convection coefficients across all calibrated days in Table 4.6, theyare more consistent, with July 20 being unusually low, and August 12 beingslightly higher. These results underline the shortcomings of this indirectmethod for determining convective behavior.Date Sky Type hc[Wm2K]July 20 clear 1.7Aug 03 clear 5.2Aug 10 clear 6.5July 22 variable 5.6July 24 variable 6.2Aug 12 heavy cloud 8.7Table 4.6: The average convective coefficients for both clearsky and cloudycalibration dataAugust 12 provides interesting data because that day the sky was observedto be fully 100% occluded by thick cloud cover. There was no rain, buthumidity was higher than average at around 80% RH all day. This thickercloud cover resulted in much lower regional solar irradiance with a peakof only 300 [W/m2] in comparison to July 22/24 which had variable cloudcover but still saw peaks of 800 [W/m2]; just as strong as a clearsky day.Just estimating cloud coverage visually does not reflect the more complexradiative effect clouds have depending on their type, elevation, and density.The August 12 data clearly demonstrates the importance of adjusting theeffective sky temperature estimation, as Fig 4.3 shows a difference of 10504.4. Discussiondegrees or more between a clearsky and fully cloudy sky temperature.4.4.3 Surface Tilt TrialThe calibration results in Tab. 4.5 show an acceptable level of accuracy,and the convective coefficients are reasonable and similar to the flat panelcalibration. The average and maximum error of this calibrated model are infact better than previous trials, possibly due to a smaller data time range.What is alarming is that the calibration procedure has again downgradedthe solar reflectivity of the panel surface, this time to 0.85. ρsolar was usedas a parameter because there was no calibrated data in close proximityto September 06. While the additional complexity of modelling a tiltedpanel and the estimated local air temperature increases the uncertainty ofthis result considerably, it does appear to build on the unfortunate trend ofdecreasing performance that was observed between July 20 and early Augustduring the clearsky calibration.The calibration parameters were also applied to the basic model but withno adjustment for surface tilt. These results demonstrate the importance ofadjusting for the surface tilt, as they give approximately double the errorof the tilt-adjusted model, even for a relatively modest surface tilt. Whilethese results are promising, the lack of data does not allow for a completepicture of the model performance. The uncertainty of the local ambient tem-perature, the more complex convective behavior of a tilted surface, and thelack of diversity in tilt and orientation variations prevented a more completecalibration procedure.51Chapter 5Phase Change MaterialExplorationThe goal of this chapter is to quickly develop a working PCM at a phasechange temperature relevant to human thermal comfort and field test itseffect on RSC panel performance. A PCM intervention was chosen halfwaythrough the field testing because the basic panel performance was not meet-ing expectations and this was a unique option. This chapter also covers aliterature review of the general premise of PCMas in terms of their func-tionality and development, the specific properties of the chosen salt-basedeutectic PCM, and how they have thus far been developed and deployed.Further, there is a explanation of the methods used to synthesize and char-acterize a custom PCM at prototype scale and how it was deployed duringthe cooling panel field testing, including the resulting increase in coolingperformance. Cooling performance benefits were evaluated based on whereduring the day there was an increase in the sub-ambient temperature deltaand the magnitude of that improvement compared with the basic non-PCMpanel temperatures.5.1 Literature Review5.1.1 General BackgroundThe term ’PCM’ refers in this work to a material that provides additionalthermal energy storage through the latent heat of fusion exchanged duringmelting/solidification. This step change in the energy flow is caused by theadditional heat required or released to change the vibrational state of theconstituent material molecules [73]. When the melting point or range of amaterial is close to the temperatures expected or desired in an architecturalapplication, PCMs become an attractive passive thermal control tool as theycan potentially absorb and release substantially greater amounts of heat525.1. Literature Reviewfor their mass than an equivalent single-phase material. PCMs also buffertemperature fluctuations more acutely during the phase change itself bybehaving in a quasi-isothermal fashion, thus becoming both a temperatureand energy management technology [74].What makes a PCM an interesting possibility for improving the performanceof a sky-radiant cooling application is the heat buffering capability thatcould help bridge the gap between the intense solar and convective heatgains during the day when cooling struggled, and the higher performingcooling available at night. The potential isothermal behavior during thephase change was also beneficial in a thermal comfort scenario so that surfacetemperatures, and thus the comfort of an occupant through MRT, could bemaintained within the desired range for longer.PCMs have been widely researched for architectural applications includingembedding PCM plates or similar within an air-based HVAC system [75],integrating PCM material into building envelope components in an active[76] or passive [77] [78] way, and coupling PCMs with additional coolingstrategies like nighttime radiant cooling [79] [79]. These PCMs are typicallyorganic such as paraffins and fatty acids, or inorganic salts and salt hydrates.There are a variety of thermal, physical, chemical, environmental and eco-nomic factors that influence the suitability of a given PCM to each appli-cation [80]. Key thermo-physical parameters that were considered for thiswork were: melting temperature and range, specific heat, latent heat of fu-sion, supercooling, and congruence. Supercooling is a phenomenon commonin inorganic PCMs where the material requires significant subcooling beforethe phase change is initiated due to the kinetic behavior of the crystallizationprocess, leading to inexact temperature control and cycling challenges [81].Congruence refers to the ability of a salt hydrate to change phase without adissociation of the water from the salt that leads to a breakdown of cyclingstability due to phase segregation and resultant sedimentation [82]. This cangreatly limit the efficiency of the PCM over time [83] but can be alleviatedby gelling/thickening with a variety of additives such as sodium polyacry-late or carboxymethyl cellulose [84]. Important for widespread application,but outside of this scope, are thermal conductivity, solid and liquid density,embodied energy, recyclability, environmental impact, cycling stability, cor-rosiveness, toxicity, and flammability. Moreover, cost and manufacturabilityare always of concern for effective commercialization of any PCM solution.At the time of project initialization no published studies that combined aPCM with daytime sub-ambient radiative sky cooling for outdoor thermalcomfort were found.535.2. Methods5.1.2 Salt Hydrate Eutectic PCMsDue to low cost, relatively high heat of fusion, wide range of melting tem-peratures, and smaller solid/liquid density differences, inorganic salt hy-drate PCMs have the potential to be high performing and affordable. Salthydrates generally suffer from higher degrees of supercooling, poor cyclingstability, lower than ideal thermal conductivity, and high corrosiveness [85].One of the most common salt hydrate PCMs with a long history of studyand application is ’Glauber’s salt’ or sodium sulfate decahydrate (SSD)(Na2SO4 · 10H2O) [86]. However, the melting temperature of SSD is 32Celsius [87], which is useful for some architectural applications but too highfor effective MRT management in the pursuit of outdoor thermal comfort.A salt hydrate eutectic is a mixture of different salts within a single PCMslurry for hybrid properties, and SSD can be combined with ionic salts likeKCl [88] or NH4Cl to lower the phase change temperature. But many estab-lished ’low-temperature’ salt hydrate eutectics are designed for cold chainapplications [89] and have phase change temperatures that are generally toolow to be effective for thermal comfort purposes [90]. A salt hydrate eutec-tic PCM that is low cost, easy to manufacture, has non-toxic and widelyavailable constituents, and with a phase change temperature range that isuseful for controlling MRT is SSD combined with sodium chloride (NaCl).NaCl and SSD has not been widely studied and the complex behavior is notyet well understood, but there are several pieces of work available to drawinsights from [91] [92] [93] [94].5.2 Methods5.2.1 PCM SynthesisThis section includes the process and iteration involved in synthesizing anddeploying a working salt hydrate eutectic PCM using readily available mate-rials and methods. This PCM combined anhydrous sodium sulfate (Na2SO4)from the UBC Chemistry Stores with sodium chloride ((NaCl) in distilledwater along with Borax (Na2B4)17 ·10H2O) and Xanthan gum (C35H49O29),all available through grocery stores.A salt hydrate eutectic PCM was chosen because of the availability and lowcost of the ingredients, a relatively high latent heat of fusion, and the abil-ity to tune the melting temperature range. The inspiration for the eutecticadaptation of adding NaCl to the Na2SO4 to theoretically lower the meltingtemperature from 32◦C to 18◦C came from the YouTuber NightHawkIn-545.2. MethodsLight’s video titled ”DIY Supermaterial Could Save You From Heatstroke:Salt based PCMs” [95]. The best performing PCM ’recipe’ is presented inTab 5.1. The Borax weight percentage of 2-3% was targeted as per resultsfrom Beaupere et al. [96]. The purpose of the Borax is to promoted nu-cleation during the cooling phase change so that the supercooling effect isminimized. The Xanthan gum was added as a weight percentage relative tothe distilled water base as per recommendations from NightHawkInLight’sprocess to perform stabilization through gelation and was adjusted a fewtimes qualitatively. The ratio of Na2SO4 to NaCl was based on informationprovided in Table 7 of Sharma et al. [97] and scaled to maximum solubilitywithin the distilled water at 40◦C as determined by having only a smallamount of undissolved salt left after vigorous stirring. The general proce-dure including physical order of operations and the timing of the stirringwas an adapted version of the method detailed in Thakkar et al. [90] incombination with aspects of the NightHawkInLight process. First the dis-tilled water was measured by weight and heated in a warm water bath to atleast 40◦C, then Na2SO4 was measured by weight and added slowly as thewater was mixed by a magnetic stir bar for at least 5 minutes. Next, theNaCl was measured by weight and added slowly while still mixing, again forat least 5 minutes. Mixture temperature was monitored and periodically re-heated to maintain maximum solubility of the Na2SO4. Finally, the Boraxand Xanthan gum were measured by weight and added, with the Xanthangum being ’activated’ by heat and vigorous stirring to promote gelation.The liquid was then poured into the intended container, whether bag fordeployment or test tube for thermal cycling, and allowed to cool.A subtle but key factor in deploying PCMs that was encountered in this workis availability. There are multiple commercial options for PCM solutionswith the desired melting temperature range of 18−24◦C such as the RT linefrom Rubitherm Technologies GmbH (Germany), ClimSel from Climator(Sweden), or PureTemp (USA). However, they were not considered to bereadily available in the Pacific Northwest and due to the weather-specificconstraints of the summer field testing, the timeline did not allow for thelogistics involved in acquiring any. Fortunately, the process of synthesizingand iterating a custom PCM delivered valuable insight into the propertiesand functionality of this technology.555.2. Methods5.2.2 Thermal CyclingQuantitative laboratory ’bench’ testing was primarily conducted to ensurethat the PCM that was intended for the cooling panel field test was per-forming as expected and not just as conventional single-phase thermal mass.Controlled thermal cycling enabled the assessment of thermal properties ofthe best performing formulation including the phase change temperaturerange, the specific heat of the liquid and solid, and the heat of fusion. Thisinformation was compared against results from the literature and createdinputs for any further thermal modelling analysis of the field test results.Thermal cycling was primarily performed for the R1.1 PCM formulation,the same that was used in the cooling panel field testing. The componentsand their mass fractions are recorded in Table 5.1, with the mass fractioncalculated as the component mass divided by the total mass of the PCM.Component Mass Fraction [%]H2O 68.1Na2SO4 18.7NaCl 8.6Borax 2.9Xanthan gum 1.7Table 5.1: R1.1 PCM formulation details.Thermal cycling was performed according to the T-history method, a simplealternative to the standard but expensive differential scanning calorimetry(DSC). This method was suitable for the needs of this exploration becausethe goal not an in-depth materials science characterization and thus did notrequire high accuracy or the through-put of dozens of samples. Rather, theT-history method was easily employed with materials and instruments onhand and gave a ballpark result to ensure the custom PCM was performingwithin reasonable expectations. This method was developed by YinpingZhang and Yi Jiang in 1999 for the purpose of simplifying the thermalanalysis of PCMs [98]. The basic concept is to heat up and cool down thePCM sample and a comparison sample of a known material (distilled water)and calculate the thermal properties according to the equations found in565.2. MethodsAppendix A.The PCM sample and distilled water were placed in 200mm long, 23mminner diameter, 2mm thick test tubes. SHT35 temperature probes werethen waterproofed and inserted into the center of the sample through ahole in the rubber tube stopper with a thin wooden dowel for support.The thermal cycling was achieved by placing them in the warm (55-60◦C)bath until they were at equilibrium and within approximately 0.3◦C of eachother, then immediately transferring them to the ice bath (∼0◦C) to cooluntil both samples again achieved equilibrium. The ice bath was created bymixing regular tap water with sufficient ice cubes to maintain a near-zerotemperature in the water throughout the cooling period, with a magnetic stirbar to gently mix and encourage a homogeneous temperature throughout thewater column. This process was repeated on 4 separate days to investigatethe consistency of the results, and to a lesser extent the stability of thePCM when repeatedly cycled. A full analysis of the cycling stability wasnot pursued. The physical setup is demonstrated in Fig 5.1.Figure 5.1: (left) a close up view of the PCM and distilled water in theirtubes with temperature probes inserted. (right) showing the test samples inthe holder in the ice bath placed on magnetic stirrer with probe data cablescoming out and the larger warming bath beside.575.2. Methods5.2.3 Field TestingThe field testing of the PCM was important to explore the challenges ofdeploying a PCM to an architectural cooling structure, develop and under-standing of the effect a PCM will have on the performance of a coolingpanel, and to provide additional data for calibrating a more advanced ther-mal model in the future. A small packet of the same R1.1 PCM formulationthat was lab tested was embedded within the cooling panel on the CIRSroof, under one of the 4 HOBO surface temperature sensors as noted in Fig3.6. This PCM was 460g grams of PCM gel within a thick plastic bag thatwas sealed with conductive copper tape and measured 17cm by 17cm by1.5cm thick. As this field testing was only intended as a proof of concept fora PCM deployment and creating the PCM in greater quantities was timeprohibitive, this scaled down version of a full panel was deemed a good com-promise that also allowed a direct temperature comparison similar to theMylar trial. The physical embedment of the PCM is shown in Fig 5.2 anda cross sectional sensor position diagram is provided in Fig 5.3Figure 5.2: (left) Insulation under the panel was cut away to mount thePCM directly underneath the panel cooling surface. Visible is the ’bottom’temperature probe (right) an insulation plug slightly proud of the bottomfollowed, ensuring good contact between the PCM and substrate.585.3. ResultsFigure 5.3: A diagram showing the placement of the PCM relative to theHOBO surface temperature sensor with an additional Particle SHT35 tem-perature probe adhered to the bottom.5.3 Results5.3.1 Thermal CyclingMaterial property results for all 4 test days are available in Tab 5.2. cp,sis the solid specific heat, cp,l is the liquid specific heat, and Hf,PCM is theenthalpy/heat of fusion. A representative cooling temperature curve withselected phase change start and end points is shown in Fig 5.4 for September04.Date cp,s [J/gK] cp,l [J/gK] Hf,PCM [J/g] Phase Range [◦C]Aug 27 14.9 4.0 41.9 25.6-20.9Sept 04 10.2 4.3 42.4 25.2-20.1Sept 05 16.7 4.6 44.5 24.4-20.2Sept 06 13.7 4.7 44.3 24.0-19.5Table 5.2: T-history method results for 4 separate cooling cycles.595.3. ResultsFigure 5.4: A comparison of the R1.1 PCM and distilled water sampletemperatures over time, with relevant time markers noted for the purposesof thermal property calculations.5.3.2 Field TestingPCM results from field testing are available over a variety of days in Au-gust and September, but a representative baseline clearsky day’s results areshown here for August 03 in Fig 5.5.The nominal performance enhancement data for August 03 and 10 is dis-played in Table 5.3. These are the differences between the average paneltemperature with no PCM and the 2 measured PCM probes for the top andbottom. The average ∆T and maximum temperature difference ∆Tbest arefor the time period of 0800-1700.605.3. ResultsFigure 5.5: A comparison of panel temperatures between the PCM materialand the bare panel for August 03, including the top and bottom of the PCMmaterial.Date ∆T top ∆T bottom ∆Tbest,top ∆Tbest,bottomAugust 03 -1.6 -3.0 -2.9 -4.9August 10 -1.1 -2.1 -2.4 -4.1Table 5.3: PCM-enhancement results for August 03 and August 10 [◦C].615.4. Discussion5.4 Discussion5.4.1 Thermal CyclingGiven the potentially unstable nature of the salt hydrate based PCM andthe uncertainty of the measurement and calculation procedures, these resultswere considered relatively consistent, with the solid heat capacity varyingthe most. Although the phase change temperature range is wider and higherthan ideal for a radiant cooling application, it was acceptable enough to moveforward with the field test deployment. The enthalpy of fusion was muchlower than the reported values in the literature and commercially, whichrange generally above 100 J/g. Examples include the 127 J/g latent heatof ClimSel21, a commercial salt hydrate PCM [99], and the 72-172 J/g de-termined via T-History for a KCl-Na2SO4 based PCM by De Paola et al.[88]. This was unfortunate but not of critical importance as the quick andimprecise method of synthesis is not entirely reliable and this was not amaterial optimization exercise. The liquid and solid heat capacities demon-strate that in addition to the latent heat storage capacity of a PCM, thesingle-phase heat capacities are also high performing. This is in comparisonto conventional architectural thermal mass options such as concrete (0.75-0.96 J/gK) [100] and water (4.18 J/gK) [101]. From a structural perspectivein application, higher specific heat capacities are beneficial as they decreasethe mass required for a given thermal inertia gain. A qualitative result ofthis thermal cycling was the distinct difference in behavior between coolingand heating phases. During the cooling portion the phase change tempera-ture shape was as expected, though the temperature gradient was not zeroit was significantly suppressed as the crystallization propagated through thematerial. Lastly, the Borax addition worked as designed, as there was noobservable supercooling effect in any of the trials.5.4.2 Field TestingThe baseline clearsky results from the application of a PCM to the insulatedRSC panel are promising. Both August 03 and 10 show very similar paneltemperature decreases directly over the PCM. This small dynamic thermalmass was able to reduce the panel temperature by an average of 1.4 degreesduring the major heating period, and the more insulated bottom side by anaverage of 2.5 degrees. For context, a 2.5 degree average panel temperaturedecrease represents the same thermal benefit as increasing the solar reflec-tivity by 4%, a significant success. The 24 hours profile for August 03 shownin Fig. 5.5 demonstrates the temperature lag brought on by the PCM that625.4. Discussiondelays the heating of the panel to improve thermal comfort when it is mostneeded.63Chapter 6Theoretical CoolingStructure and Tree CanopyTrialThis chapter will discuss a literature review of outdoor thermal comfortstudy approaches and evaluation metrics, and the benefits and drawbacksof vegetation-based interventions. Further, it will explain the adaptations tothe calibrated thermal model that were made to assess the thermal comfortof an occupant in an ideal passive cooling shelter with a Radi-Cool coolingpanel roof and the methods used in a field tested comparison of the pro-totype panel and a green enclosure on UBC-Vancouver campus. Lastly, itwill compare the theoretical cooling shelter model’s thermal comfort per-formance against a tree’s and against a person in direct sunlight from twoliterature field tests and the results from the local field test.6.1 Literature Review6.1.1 Outdoor Thermal ComfortOutdoor thermal comfort (OTC) is an important design consideration forlandscape architects, urban planners, engineers, and policy makers. OTCis steadily gaining attention as urbanization and climate change put pres-sure on the public’s experience in the built environment. Thermal comfortis commonly defined as the ”state of mind in which human feels satisfiedwithin the thermal environment” [102]. The study of a human’s comfortresponse to a variety of factors present in an outdoor space is a relativelynew field, that has expanded in the 21st century and is complex, with sig-nificant uncertainty. OTC studies typically concern one or more heat mit-igation strategies such as vegetation, surface types, or water features, andcan rely on mathematical comfort indices that predict an occupant’s expe-646.1. Literature Reviewrience based on environmental parameters or direct surveys that solicit userfeedback [103]. For the exploration of environmental parameters, there aregenerally three methods that can also be hybridized: 1) computer simula-tions like CFD [104] [105] including the popular program ENVI-met [106] orthermal radiation software like RayMan [107] or SOLWEIG [108]. 2) Scalemodels of the urban environment that allow for more explicit control ofvariables [109]. 3) Field deployments with static direct [110], mobile direct[111], or remote sensing [112] of variables.6.1.2 Thermal Comfort Impacts of VegetationAlthough many different pathways to sub-ambient sky cooling have been de-veloped in recent years, design applications have thus far been mostly limitedto cooling panel additions that can improve an existing air conditioning sys-tem’s efficiency for indoor settings. What about outdoor thermal comfortand fully passive solutions? One of the most resilient and highest perform-ing passive outdoor design approaches is to use tree canopies and vegetationto improve people’s experience. This is especially important in urban areas,where air and surface temperatures can be increased by the ’urban heatisland’ (UHI) effect [113]. Green spaces help to suppress the UHI effectin dense areas and through solar shading and evaporative cooling (transpi-ration) can produce remarkably comfortable environments in hot weatherscenarios [114]. Many factors contribute to the thermal comfort impactsof vegetation in an outdoor environment including geometry/arrangement[115], and tree properties like crown size, shape, and density [116] [117][118]. Evapotranspiration is a vegetation-specific phenomenon that reducesair temperature [119] [120] and MRT through lower vegetation surface tem-peratures [121]. However, vegetation can negatively affect thermal comfortby suppressing wind speeds [122] and increasing humidity [123].Despite the excellent performance of vegetation, it is still valuable to gainadditional outdoor cooling approaches in the designer’s ’toolbox’ to increaseflexibility in responding to application-specific needs. Hence, the RSC pas-sive cooling shelter concept. There are various scenarios where an artificialcooling structure would be attractive to decision makers. For example, large,dense, mature trees capable of providing robust shading may take up to sev-eral decades to grow [124], so an artificial shelter could be a rapid responsemeasure until vegetation is ready. Moreover, those larger trees have deeperroot systems and wider and taller canopy requirements, potentially resultingin conflicts with other infrastructure in the area. Lastly, depending on the656.1. Literature Reviewlocal climate, vegetation that is suitable for improving thermal comfort maybe resource-intensive to support [125]. Thus, a critical question is: how wellcould a radiant cooling shelter perform and how does it compare to a treecanopy in terms of outdoor thermal comfort?Cheung et al. [126] and Ka´ntor et al. [127] are two studies that examinethe comparison a person’s thermal comfort in full sunlight versus in theshade of a tree. Cheung et al. collected dry-bulb air temperature, RH,global solar irradiance, wind speed, and black globe temperature results fora broadleaf evergreen Ficus microcarpa tree during 3 consecutive days inJune, July, and August 2017 in Hong Kong, China. Ka´ntor et al. have sim-ilarly structured results for a large but sparsely leafed Sophora japonica inSzeged, Hungary. Both trees have similar crown diameters, but the Hungarylocation is in a public square with a less dense urban structure and Ka´ntorcalculated longwave and shortwave with more resolution and accuracy using6 directions and net radiometers instead of a single black globe instrument.Based on the Ko¨ppen climate classification system [128], Hong Kong is Cwa(monsoon-influenced humid subtropical), Szeged is Dfa (hot-summer humidcontinental) and Vancouver is Cfb (temperate oceanic climate).6.1.3 Thermal Comfort Assessment MetricsTo determine the effectiveness of a fully passive ’cooling shelter’ that couldbe suitable at locations such as bus stops, the thermal comfort of an occu-pant of that space needs to be predicted. There exist a variety of metricsfor evaluating the thermal comfort of a human in an outdoor setting, orquasi-outdoor as a permeable cooling shelter would be. These can be basedon thermal sensation of test subjects under a ranger of conditions like theextensively used Predicted Mean Vote (PMV) by Fanger [129], or ’equivalenttemperature’ models such as Physiological Equivalent Temperature (PET),Universal Thermal Climate Index (UTCI), and Standard Effective Temper-ature (SET). Equivalent temperature metrics are defined as the equivalentair temperature (◦C) of a reference environment that would produce thesame thermal stress as experienced by a person in the true environment ofinterest, and as such are easier to interpret. However, the comfort rangesthat help resolve results into categories of thermal stress differ between met-rics and are also up to interpretation based partially on climate zone [130].PET is calculated based on the Munich Energy-balance Model for Individ-uals (MEMI) [131], a realistic thermal estimation of the human body, andis designed explicitly for the evaluation of outdoor thermal comfort.666.2. Methods6.2 Methods6.2.1 Modelling Thermal Comfort in ApplicationResults from Ka´ntor et al. on May 20, 2015 and Cheung et al. on August06, 2017 were used as comparison points for the theoretical performanceof a passive cooling shelter based on the calibrated thermal model. Bothdays had higher temperatures, clear skies, and strong solar irradiance of atleast 800 W/m2 at peak. PET was selected as a suitable evaluation metricof thermal comfort because of its wide usage in literature [130], and the0.5 m/s lower wind speed limit for UTCI that prevented convergence formuch of the data. PET also specifies a wind speed measured at 1.1m aboveground, which matches the field data of this work, as opposed to UTCIwhich requires an estimation for a 10m height. The PET interpretationscale for human comfort perception was taken from the work of Matzarakiset al. [132] as used by Ka´ntor et al. and shown in Appendix A.For the purpose of a preliminary exploration, a simplified theoretical coolingshelter was modelled for a best case scenario of performance as an infiniteplane, as described in Fig 6.1. The ground is considered to be fully shaded,and its surface temperature is estimated as the local ambient air tempera-ture. Each side of the cooling panel is assumed to have the same convectiveheat transfer coefficient as the average of the Baseline Trial calibration re-sults in 4.3.1: a = 2.8, b=3.5. Both sides of the panel are subjected tothe same wind speeds. The cooling panel is assigned the manufacturer’sspecification of ρsolar = 0.91 and ϵIR = 0.93.This simplification does not account for the radiant exchange between oc-cupants and the underside of the cooling panel when calculating the steadystate panel temperature. This interpretation could be compared to a suf-ficiently large pavilion-type structure that would fully shade the occupant,and reduce the effective view factor of side walls, surrounding buildings, orsun-heated ground down to near zero.The view factors between the ground and the underside of the cooling panelare both considered to be 1, with the ground graybody emissivity estimatedas 0.8, and the cooling panel graybody emissivity assigned a value of 0.93,equal to that of the Radi-Cool membrane in the LW. The overall heat balanceequation is thus adjusted to Eqn 6.1 and illustrated in Fig 6.1.qsolar + qsky−panel,net + qground−panel,net + qconv,top + qconv,bottom = 0 (6.1)676.2. MethodsFigure 6.1: Representation of the heat balance for a simplified cooling panelpowered cooling structure.A key input parameter for PET is the Mean Radiant Temperature (MRT)experienced by the occupant. This is an average surface temperature ’seen’by the occupant, and weighted by the emissivity and view factor of eachsurface with respect to the occupant. The view factors between an occupantand the roof and ground for an infinite plane scenario are assumed to be 0.5for each surface. The MRT was calculated based on equ. 6.2.MRT =(∑Ni=1 ϵiFi(Ti)4∑Ni=1 ϵiFi) 14(6.2)Air temperature and RH are assigned the local ambient values. The under-lying calculations for PET were computed using the Python library ’pyther-malcomfort’ [133] using default setting for clothing, age, sex, metabolism,atmospheric pressure, height, weight, and external work.6.2.2 Tree Canopy Experimental SetupSuccessful modelling of the thermal environment beneath a tree canopyrequires a complex understanding of the canopy structure and vegetationtypes, evaporative cooling, the local effects on wind and radiant exchange ofan occupant with a wide variety of ambient surfaces. Therefore, this study686.2. Methodsfocused on field testing by placing a sensor cluster in a nearby vegetativeenclosure. The location was the vegetation directly adjacent to the south-west side of the Third Space building at UBC - Vancouver and was chosenfor a combination of useful factors:1) Proximity to the CIRS roof cooling panel setup. This allowed the assump-tion that regional weather conditions were approximately identical, see Fig3.1. Although moving the cooling panel off the roof and next to the vegeta-tion would have been an even more apples-to-apples test of performance dueto differences in local wind speeds, ambient temperatures, and the radiativeenvironment caused by shading and IR emissions of surrounding buildings.However, because these test were considered supplemental, the assessmentof the roof placement was simpler, and placement on the ground near theThird Space was not secure, the panel was left on the CIRS roof.2) A nearly complete canopy enclosure, see Fig 6.2. This meant that therewould be almost a 100% view factor from person to vegetation and it wouldprovide excellent shading at all times of day. Although some sun and lightdoes penetrate the vegetation, more than a deep forest for example, it wasfelt that this location was an excellent ’best-case’ of tree shading for an urbanenvironment. This full shading had the supplemental benefit of providing ameasure of security for the instruments so that they could be left unattendedto extend the allowable test period.3) Access to power. Since the location was directly next to a building theteam had access to, consistent power was available via extension cord. Thisgave the setup excellent reliability for the sensors and was strongly preferredover the mobile battery options.A small sensor cluster was placed in the approximate center of the clear-ing. This packaged consisted of the TSI VelociCalc air speed, dry bulb-temperature, and RH probe with attached data logger, and the MRT ’CUBE’sensor from CHAOSense Inc. See Sec. 3.2.1 for details regarding these sen-sors. Both these sensors were placed at 1.1m above ground, the approximatecenter of mass for an adult [134].For the thermal comfort calculation of PET during the tree canopy trial, thecooling structure was further idealized using the average surface temperatureof the fully insulated cooling panel. This neglects the additional convectionand ground radiation exchange of a suspended panel, but gains relevance andaccuracy from being a more direct field comparison in place of a theoreticalthermal model.696.2. MethodsFigure 6.2: (left) Under-tree MRT testing tree canopy view from the outside(right) tree canopy view from insideThe PCM-enhanced RSC panel temperature used was the direct ’top’ HOBOsensor, as explained further in Cha. 5.The wind speed encountered by the occupant is assumed to be equal to thelocal wind measured beneath the tree canopy. This was done because PETwas noted to be sensitive to wind speed, and assigning a higher wind speedto the PET calculation beneath the CIRS RSC panel based shelter wouldgive it a distinct thermal comfort advantage.706.3. ResultsFigure 6.3: Under-tree MRT sensor package6.3 Results6.3.1 Theoretical Literature ComparisonFor each study the theoretical PET for a Radi-Cool cooling panel idealshelter is plotted in comparison to the PET calculated by each study for theenvironmental variables recorded at that location.716.3. ResultsFigure 6.4: A comparison of ideal cooling shelter PET against the under-tree PET and full sun PET for the results of August 06, 2017 in Cheung etal.Figure 6.5: A comparison of ideal cooling shelter PET against the under-tree PET and full sun PET for the results of May 20, 2015 in Ka´ntor et al.726.3. Results6.3.2 Field Tested Tree Canopy Comparison3 data periods were recorded for the Tree Canopy Comparison: September04, 05, and 07-08. September 04 was a short 5 hour run to ensure thetest setup and sensor were functioning properly. September 05 is longer,targeting the afternoon/early evening peak temperatures, and was chosento be the representative results. Once the overnight security of the test sitewas confirmed, a longer 48 hour test was engaged on September 07/08, butunfortunately these days were much cooler and cloudier than September 05and thus not used.Figure 6.6: Temperature results comparing both PCM and non-PCM cool-ing panel temperatures against the UBC canopy MRT and local ambienttemperature within the vegetation for September 05.736.4. DiscussionFigure 6.7: A PET comparison between an ideal cooling shelter, with bothPCM and no PCM, and the UBC Star Magnolia tree canopy for Sept 05.6.4 Discussion6.4.1 Modelled Cooling StructureThe results from the literature comparison with Ka´ntor and Cheung showthat an RSC cooling shelter can perform as well as a tree for improvingthermal comfort of an occupant during the day. In comparison with thetree in Hong Kong in Fig. 6.4, the RSC shelter PET is very close to the treePET during the day, but hotter at night. The RSC shelter performed betteragainst the tree in Hungary as plotted in Fig. 6.5, showing a PET on average4 degrees lower than the tree. This brings the RSC shelter down into a lowerheat stress category for about half the test period. This additional success isprimarily based on better heat rejection to the atmosphere in Hungary due toa cooler effective sky temperature of 10-14 ◦C versus the 18-22◦C estimatedin Hong Kong. This behavior is consistent with results in the literaturethat show RSC performing better in dryer climates with better atmospherictransmissivity and lower downwelling radiation from water vapor [34] [135][136]. This underlines the importance of a climate-aware design process for746.4. Discussionevaluating the potential of RSC technology in a given location. Lastly, thecomparison against the full-sun PET highlights that interrupting direct solarradiation is critical for outdoor thermal comfort, with controlling MRT andpromoting air flow being of secondary importance.6.4.2 Field Tested Cooling StructureThe field results from the match-up of the RSC panel on the CIRS rooftopand the nearby Star Magnolia tree demonstrate a similar relative PET per-formance to the modelled literature comparison. The ideal RSC shelter wasestimated to perform just as well as the tree canopy with similar PET. Be-cause the ambient air temperature was hot but not extremely so, the humanresponse to this PET range would not be more than a slight heat stress. Thiscan be anecdotally confirmed by the project team’s comfort inside the treecanopy on September 05. The PCM increased thermal comfort performanceby a maximum of approximately 2.5 degrees at peak solar irradiance. Thecooling surface temperature, and thus MRT experienced by an occupant, isjust one of many parameters that determine PET. This dilutes the impactof just cooling surface performance gains, making it critical that a shelterbe design wholistically to shade well, encourage good air flow, and have ahigh cooling surface view factor.75Chapter 7Conclusion7.1 Summary of Results7.1.1 Literature ReviewA significant body of literature was reviewed during the process of this work.The most relevant findings were that 1) work in the field of RSC has acceler-ated greatly in the last decade and a wide variety of materials, applications,and possible commercial products are being worked on. 2) There are manyPCM types, including commercial products, suitable for low-temperaturethermal comfort but this remains an under-explored area of PCM appli-cation. Eutectic salt hydrate PCMs have complex thermal behavior anddespite being inexpensive with high latent heats and tunable phase changetemperatures, remain challenging to implement due to cycling instabilityand poor temperature control during the heating/solidification phase. 3)Outdoor thermal comfort is gaining attention as a result of urbanizationand climate change. There are many strategies to increase outdoor thermalcomfort including vegetation, water features, and other types of shading.RSC for the purpose of thermal comfort is being actively explored, espe-cially in the area of personal clothing made from extra-cool fabrics.7.1.2 Thermal Model and CalibrationThis study successfully produced a simple thermal model to predict theRadi-Cool RSC panel steady-state temperatures under baseline, cloudy, andtitled conditions. In all cases, the calibrated models were able to achieve thegoal of below 1 degree of average predictive error, with the baseline averageresults being just over at 1.1 degrees. The baseline calibration discoveredan apparent decrease in solar reflectivity as the field testing progressed fromclose to the manufacturer’s stated 0.91 at the beginning of testing to as lowas 0.85 at the end. IR emissivity was confirmed to be consistent at 0.93.Baseline calibration produced some variation in the convection parameter767.1. Summary of Resultsvalues consistent with wide range reviewed in the literature and with aver-age values of a=2.8 and b=3.5. The cloud cover calibration was challengingand generated internally consistent convection parameters but quite differ-ent from the baseline results. There was significant uncertainty about themethods used to determined cloud cover fraction, and this was discovered tobe a more complex exercise than anticipated. The tilt functionality of themodel was not thoroughly tested, but in the one setup the model performedadmirably with similar convection parameters to the baseline calibration.7.1.3 Panel PerformanceThe field study determined that the baseline Radi-Cool panel can achievesub-ambient daytime cooling but struggles under full sun. The initial solarreflectivity was close to advertised but there remains uncretainty about thelong-term performance of the product.7.1.4 PCM EnhancementA low-temperature eutectic salt hydrate PCM was successfully synthesizedwith simple materials and procedures. This PCM was determined to havea phase change temperature range of 20-25 ◦C suitable for thermal comfortapplications and a latent heat of fusion of approximately 43 J/gK per theT-History method. The PCM-assisted panel averaged between 1.0 and 3.0degrees more sub-ambient than the original baseline panel during the heatingperiod. This shows that low temperature PCMs offer simple and robustperformance increase potential for RSC applications.7.1.5 Theoretical Cooling ShelterBoth field testing and modelling using data from 2 literature sources con-firmed that an idealized cooling shelter using the Radi-Cool RSC panel de-veloped in this study as a roof would perform equally well to a well-shadedtree based on the PET metric. However, primary thermal comfort gainswere achieved from complete shading of the occupant, with the passive radi-ant cooling delivered by the panel being a secondary benefit. The Radi-CoolRSC surface chiefly prevented the ’roof’ surface from achieving the high tem-peratures that are typical for conventionally absorptive surfaces exposed tosignificant solar gains.777.2. Study Limitations7.2 Study LimitationsThere are several important limitations of this study that should be notedto apply appropriate context to the methods and results discussed.The first limitation is that this study did not explicitly field test a workingscale RSC cooling shelter. Partial data was available for a suspended panelwith some side shading, but a more definitive analysis of the convectivebehavior of an installed RSC panel ’roof’ or even sides was not completed.Next, the calibration procedure that produced linear fit convective transfercoefficients was not cross-examined for validity. As identified in the litera-ture review, though these coefficients serve to fit the model prediction abilityto the observed data, it does not explicitly define the convective behaviorof the RSC panel. For example, the parametrization of the thermal modelcould be in error, with an under or simply un-defined thermal exchangebeing responsible for energy that the calibration procedure attributed toconvection. Even assuming the convective behavior of the panel is ade-quately defined by the calibration of this thermal model, it is not clear howapplicable this could be to a much larger panel or roof surface suitable fora working cooling pavilion, or for significantly higher wind speeds. The im-pact of the perimeter lip on forced and natural convection was not explicitlyinvestigated.Similarly, the calibration results for ρsolar and ϵIR do not represent directmeasuring of the panel properties, but instead the parameters that providethe best prediction of the observed data. This subtle distinction is importantfor all parameter calibration results.Furthermore, the sidewall temperature data indicates clearly that the RSCcooling panel structure was absorbing a certain amount of solar radiation,in addition to presumed convection from the surrounding air. This was notadequately explained in the thermal model due to the assumption that therigid insulation with Mylar covering was sufficient to minimize these effects.Even being lightweight with high thermal resistance, the addition of so muchrigid insulation could still have had an effect on the temperature behaviorof the panel surface, especially since adding thickness also increased theabsorptive surface area of exterior.The simplicity of the thermal model method created a straightforward andfast predictive process for the performance of the RSC panel with easy trou-bleshooting. However, a model with much greater spectral resolution would787.2. Study Limitationsallow for an understanding of the radiant exchange with higher fidelity.Similarly, directional dependence for the cooling surface and atmosphericemissivity were not accounted for, and this could be important dependingon how much the material properties of a given RSC surface change withangle. The graybody empirical sky emissivity correlation selected in thiswork may not be suitable for a comparison across different climate zones,and has an estimated error of ± 10 W/m2. More advanced in-situ monitor-ing of effective sky temperature could be a path to clarifying the differencebetween a general estimation and results for a given location and conditions.That being said, predictive models, such as the one developed in this work,are intended to function with typically available climate weather files whichthemselves are estimated averages.The modelling of the thermal comfort performance of a cooling shelter de-sign utilizing this RSC panel was heavily idealized. In practice, occupantswould interact radiatively with the underside of the panel, convection, bothbuoyancy driven and forced would not act the same as the topside, andambient radiation from both the sun and surroundings would not be fullyshielded from the occupants. Lastly, although a sky view factor of 1 is idealfor cooling, there would be limited cases where that assumption could bemade for an urban street-level cooling shelter installation.The thermal behavior of the PCM, including its own complex phase changeand its impact on the RSC panel, is not explained in this work. The scopeof the work only allowed for the deployment of a PCM as an exploration ofits potential. Much work remains to expand the thermal model and test awider variety of PCM formulations, masses, and the challenges of how bestto deploy a PCM material to an RSC panel. The material property resultsfor the T-History method calculations come with uncertainty based on theunderlying assumptions for that method surrounding the convection of thefluid bath surrounding the test tubes, the true temperature of the coolingice bath, and the influence of the temperature sensor itself embedded withinthe sample.To complete a more reliable justification for the tilt functionality of model,more tilt and azimuth angles would need to be field tested. What is more, thecalibration of the forced convective components with tilt is more complicatedthan a flat panel, and would depend on both wind direction and speed. Thesimple linear fit method for coefficient calibration may lose validity in thesesituations.The sky view factor in the thermal model was assumed to be 1, but that797.3. Recommendations for Future Workis not physically true. With the roof perimeter wall, stairwell, and vari-ous other obstacles, the sky view would be slightly less. Moreover, a fewsurrounding roof objects, such as a research communications dish, providedsome partial shading at various times during the day, impacting the observeddata slightly but not accounted for.A transparent convective shield was trialed but detailed material transmis-sivity and emissivity data was not obtained, nor air temperature data frominside the air gap. This limited the analysis of how the convective shieldwas impacting results, detailed in Appendix E.A cost analysis for a production-ready RSC panel with PCM, includingmanufacturing and maintenance, was not pursued in this work. Indepen-dent of the anticipated performance or comfort gains from the use of thistechnology or similar, cost is the single biggest hurdle that would impactadoption by public or private customers. The main cost-related aspect ofthis work was intentionally choosing cooling surface and PCM materials thatare considered low-cost in comparison to their counterparts.7.3 Recommendations for Future WorkThere are still many avenues for further work in the domain of radiant sky-cooling and its applications to outdoor thermal comfort, and more widely,building energy savings. The first recommendation is deploying a fieldscale cooling shelter to compare the theoretical modelling done in this workagainst the a real world design. This would allow for a true measurement ofpanel performance with the underside exposed to convection and radiation.This could also more intentionally test the additional of an IR-transparentconvective barrier on the underside of the panel that could reduce convec-tive losses while maintaining IR exchange, and thus cooling, with occupantswhile being relatively safe from damage. This investigation could be basedon calculated thermal comfort metrics as in this work, or could go beyondthat to create a full-size pavilion type structure that could host test occu-pants to gather comfort response survey data. A full size installation wouldalso bring the challenges of cost and structural design, with its impact oncooling effectiveness, to the fore. A conceptual design of a full size coolingpavilion with sky-radiant roof developing a high view factor between the oc-cupants and the roof underside, and sidewalls that control against incidentsunlight and longwave radiation but are permeable to wind and some lightis shown in Fig. 7.2.807.3. Recommendations for Future WorkAnother low-hanging fruit is to pursue higher performance cooling surfacetypes to increase the cooling performance of a cooling panel of this type. Asfurther commercial options become more widely available, this type of workcan accelerate, ideally reaching the point where radiant sky-cooling surfacesor units like panels are a viable option for designers and not an obscuretechnology. For example, using the average baseline calibrated convectionparameters, a material with a theoretical solar reflectivity and IR emissivityof 0.97 could lower the RSC panel temperature by an average of 5 degreesfrom 0800-1700, as shown in Fig. 7.1 for the July 20 data.Figure 7.1: The performance increase of a more sophisticated RSC materialwith a solar reflectivity and IR emissivity of 0.97, applied to July 20 fielddata.This study has shown that a low temperature PCM is a promising perfor-mance addition to an RSC panel. Further work could involve investigatingthe effect of other suitable PCM types that have useful phase change temper-ature ranges but superior latent heat of fusion or other relevant propertiessuch as availability, cost, or thermal conductivity. The discovery and testingof suitable commercial PCM options would go a long way toward simplifyingand justifying a practical PCM-enhanced cooling panel design. To comple-ment this physical testing work, the simple thermal model developed in thisstudy, or indeed a more evolved model, should be adapted to include thethermal effects of the PCM dynamically. This model could be validated817.3. Recommendations for Future Workagainst field data from this work, or additional field studies conducted witha PCM with better understood properties and behavior. A working PCMthermal model would then be able to more quickly assess a variety of PCMformulation options as a design exercise. Further, a working model couldevaluate design parameters like the PCM thickness on the underside. Byintuition, panel performance would increase with the thickness of the PCMdeployment as the mass increases, but there may be a balance point whereadditional thickness either results in marginal performance gains. Too muchthermal mass could also result in the mass not being able to adequatelycool overnight, limiting performance. There may also be a lack of thermalconductivity in the PCM that effectively insulates the lower surface, andthus the MRT produced for an occupant, too much from the top surface.Lastly, there could be a comparison including cost and performance be-tween a PCM-enhanced cooling panel and one benefiting from conventionalsingle-phase thermal mass such as concrete.An RSC-based cooling shelter has the advantage of providing cooling formultiple people and being a ’hands-off’ solution for a wide variety of tran-sient occupants requiring no additional investment on their part. However,a promising and logical next step is to explore the possibilities of the nextscale down: personal cooling equipment/clothing. This could be modifi-cations to outerwear, collapsible items like parasols, or even the hardhatsmandated on work sites. Many studies and organizations are already inves-tigating this area and this can be a powerful addition to tool set of solutionsfor the application of RSC in outdoor thermal comfort [137] [138].While the simple thermal model developed in this work has been shownto maintain acceptable accuracy after calibrating for convective heat trans-fer coefficient parameters, further investigation could be made to assess theconvective losses through more complex techniques such as CFD modelling.This could help validate the simpler linear coefficient approach in an ap-plied setting and could also serve to explore more subtle design featuressurrounding minimizing convection such as the perimeter lip wind shield.Additional field data on the possible degradation or fouling of RSC surfacesexposed to environmental conditions would be helpful to inform applicationsuitability. For example, even monthly cleaning of a cooling surface of anyreal size would amount to a serious cost. The RSC materials themselvesmay also be susceptible to damage from prolonged expose to solar radiation,thermal cycling, water, or abrasion.A key applied aspect of the cooling shelter that was not field tested in827.3. Recommendations for Future Workthis work but is deserving of further attention is the impact of surroundingbuildings on the cooling performance of the panel. Many locations thatwould benefit from a shelter of this type are in built-up urban areas withelevated air temperatures, increased ambient IR radiation, and reduced skyview factor. Field testing of a cooling panel in a variety of urban area typescoupled with a thermal model that can accept local geometry inputs tomodify solar irradiance, view factors, surrounding IR radiation, and windspeeds would clarify the potential of RSC-based cooling shelter for outdoorthermal comfort.To fully maximize the benefits of radiant sky-cooling, work should continueon performance enhancements that have robust applied potential, such as aPCM. Is it possible to develop a structurally rugged but IR transparent con-vective shield that could survive an extended deployment? Could a one-wayIR non-reciprocal ’window’ device that blocks incident but allows outgoingIR radiation [139] be feasible? Would coupling a cooling surface with a ther-moelectric device [140] to boost emitted IR radiation at the expense of someelectrical energy use be a legitimate path forward?A weather feature that has not yet been systematically accounted for inmost sky-cooling thermal models, including the one in this work, is the ef-fect of forest fire smoke on IR radiation exchange with the cooling surface.While not as commonplace as clouds, in many regions, including BritishColumbia, forest fires are generally increasing in both frequency and mag-nitude [141] [142]. These widespread fires can potentially cover large areaswith dense smoke for many days in the hottest months of the year. Thissmoke could have sky-cooling performance impacts by affecting longwave at-mospheric downwelling radiation and transmissivity, and there would likelybe a beneficial reduction of solar irradiance [143]. Adapting the thermalmodels to include forest fire smoke data could be important in informingdesign decisions for areas where this weather condition is prominent.The thermal model developed in this study was built with simplicity as aprimary goal, and this makes it a good candidate to merge with a buildingenergy simulation such as TRNSYS [144]. This integration could serve toinclude sky-radiant cooling technologies in the design discussions of buildingcooling retrofits and new build strategies. RSC can be in fluid cooling panels[53], embedded in the structure [145], applied to the roof [146], walls [147],windows [148], or otherwise integrated in the building envelope [149], andthus provides a wide latitude of design flexibility. A reasonable body of workhas been done in this area in recent years, but more evidence and diversity of837.3. Recommendations for Future Workapplied case studies could help push the technology toward wider adoptionby designers and policy makers.Figure 7.2: An artistic rendering by June Geyer of what a possible RSCcooling shelter could be in practice; keeping people comfortable and pro-tected during the heat.84Bibliography[1] Pacific Climate Impacts Consortium and Pinna Sustainabil-ity. Climate projections for metro vancouver. Technical re-port, Metro Vancouver, 2016. https://metrovancouver.org/services/air-quality-climate-action/Documents/climate-projections-for-metro-vancouver-2016.pdf.[2] Michael Egilson. Extreme heat and human mortality: A reviewof heat-related deaths in b.c. in summer 2021. Technical report,British Columbia Coroners Service, 2022. https://www2.gov.bc.ca/assets/gov/birth-adoption-death-marriage-and-divorce/deaths/coroners-service/death-review-panel/extreme_heat_death_review_panel_report.pdf.[3] M. Reginald. Progress, priorities, and obstacles to providing adequateshade and lighting at bus stops in los angeles. UCLA: Institute ofTransportation Studies, 2024.[4] Y. Dzyuban and D.M. Hondula. Public transit infrastructure and heatperceptions in hot and dry climates. Int. J. Biometeorol, 66:345–356,2021.[5] A. Fraser and M. Chester. Transit system design and vulnerability ofriders to heat. Journal of Transport Health, 4:216–225, 2017.[6] Nathan Griffiths. Metro vancouver expected to growby 50,000 people a year: Report. The Vancouver Sun,July 2024. https://vancouversun.com/news/local-news/metro-vancouver-expected-grow-50000-people-year.[7] E. Teitelbaum and et al. The cold tube: Membrane assisted radi-ant cooling for condensation-free outdoor comfort in the tropics. InJournal of Physiscs: Conference Series, page 1343, 2019.85Bibliography[8] D.J. Fixsen. The temperature of the cosmic microwave background.Astrophys. J., 707:916, 2009.[9] M.N. Bahadori. Passive cooling systems in iranian architecture. Sci.Am., 238:144—-155, 1978.[10] S. Catalanotti and et al. The radiative cooling of selective surfaces.Sol. Energy, 17:83—-89, 1975.[11] U. Eicker and A. Dalibard. Photovoltaic–thermal collectors for nightradiative cooling of buildings. Sol. Energy, 85:1322––1335, 2011.[12] A. Dyreson and F. Miller. Night sky cooling for concentrating solarpower plants. Appl. Energy, 180:276–286, 2016.[13] B. Zhao, M. Hu, X. Ao, N. Chen, and G. Pei. Radiative cooling: A re-view of fundamentals, materials, applications, and prospects. AppliedEnergy, 236:489–513, 2019.[14] A. Raman, M. Anoma, and L. Zhu. Passive radiative cooling belowambient air temperature under direct sunlight. Nature, 515:540–544,2014.[15] S.Y. Jeong, C.Y. Tso, and et al. Field investigation of a photonic multi-layered tio2 passive radiative cooler in sub-tropical climate. Renew.Energy, 146:44–55, 2020.[16] Y. Zhai, Y. Ma, and et al. Scalable-manufactured randomized glass-polymer hybrid metamaterial for daytime radiative cooling. Science,355:1062—-1066, 2017.[17] X. Li, J. Peoples, and et al. Ultrawhite baso4 paints and films forremarkable daytime subambient radiative cooling. ACS Applied Ma-terials and Interfaces, 13:21733–21739, 2021.[18] X. Li, J. Peoples, and et al. Full daytime sub-ambient radiative cool-ing in commercial-like paints with high figure of merit. Cell ReportsPhysical Science, 1:100221, 2020.[19] K. Lin, S. Chen, and et al. Hierarchically structured passive radia-tive cooling ceramic with high solar reflectivity. Science, 382:691–697,2023.86Bibliography[20] E. Torgerson and J. Hellhake. Polymer solar filter for enabling directdaytime radiative cooling. Solar Energy Materials and Solar Cells,206:110319, 2020.[21] K. Zhang, D. Zhao, and et al. Modelling study of the low-pump-powerdemand constructal t-shaped pipe network for a large scale radiativecooled-cold storage system. Appl. Therm. Eng., 127:1564–1573, 2017.[22] K. Zhou, N. Miljkovic, and L. Cai. Performance analysis on system-level integration and operation of daytime radiative cooling technologyfor air-conditioning in buildings. Energy Build, 235:110749, 2021.[23] K. Zhang and D. Zhao. Energy saving and economic analysis of a newhybrid radiative cooling system for single-family houses in the usa.Appl. Energy, 224:371–381, 2018.[24] T.S. Safi and J.N. Munday. Improving photovoltaic performancethrough radiative cooling in both terrestrial and extraterrestrial envi-ronments. Optic Express, 23:A1120, 2015.[25] SRI International. Self-Cooling Paint.https://www.sri.com/fcd_technology/self-cooling-paint-a-passive-radiative-cooling-solution/,2024. Accessed: 2024-11-20.[26] SkyCool Systems. Radiative Cooling Panels. https://www.skycoolsystems.com/technology/, 2024. Accessed: 2024-11-20.[27] SpaceCool Inc. Film. https://spacecool.jp/en/aboutspacecool/,2024. Accessed: 2024-11-20.[28] Coldrays LLC. Radi-Cool Film. https://coldrays.com/product-category/wholesale-materials/reflective-film/,2024. Accessed: 2024-11-20.[29] X.Z. Lim. The super-cool materials that send heat to space. Nature,577(7788):18–20, 2020.[30] J. Mandal, Y. Fu, and et al. Hierarchically porous polymer coatingsfor highly efficient passive daytime radiative cooling. Science, 362:315–319, 2018.[31] R. Mokhtari, G. Ulpiani, and R. Ghasempour. The cooling station:Combining hydronic radiant cooling and daytime radiative cooling forurban shelters. Applied Thermal Engineering, 211, 2022.87Bibliography[32] C. Balaji. The Essentials of Radiation Heat Transfer. Springer, Cham,Switzerland, 1st edition, 2021.[33] R. Zevenhoven and M. Fa¨lt. Radiative cooling through the atmo-spheric window: A third, less intrusive geoengineering approach. SolarEnergy, 151:27–33, 2018.[34] Md. Hossain and M. Gu. Radiative cooling: principles, progress, andpotentials. Advanced Science, 3:1500360, 2016.[35] P. Berdahl, M. Martin, and F. Sakkal. Thermal performance of radia-tive cooling panels. Int J. Heat. Mass Transf., 26:871–880, 1983.[36] P. Berdahl and M. Martin. Emissivity of clear skies. Solar Energy,32:663–664, 1984.[37] Inc. Spectral Sciences and the U.S. Air Force. MODTRAN: ModerateResolution Atmospheric Transmission. Spectral Sciences, Inc., 2024.[38] C.G. Granqvist and A. Hjortsberg. Radiative cooling to low temper-atures: general considerations and application to selectively emittingsio films. J Appl Phys, 52:4205–4220, 1981.[39] E.M. Lushiku, A. Hjortsberg, and C.G. Granqvist. Radiative coolingwith selectively infrared-emitting ammonia gas. J Appl Phys, 53:5526–5530, 1982.[40] A. Aili, D. Zhao, and et al. A kw-scale, 24-hour continuously opera-tional, radiative sky cooling system: Experimental demonstration andpredictive modeling. Energy Conversion and Management, 186:586–596, 2019.[41] E. Goldstein, A. Raman, and S. Fan. Sub-ambient non-evaporativefluid cooling with the sky. Natural Energy, 2:17143, 2017.[42] M. Hanif, T.M.I. Mahlia, and et al. Potential energy savings by radia-tive cooling system for a building in tropical climate. Renewable andSustainable Energy Reviews, 32:642–650, 2014.[43] W.C. Swinbank. Long-wave radiation from clear skies. Royal Meteo-rological Society, 89:339–348, 1963.[44] Clark G. and Allen C. The estimation of atmospheric radiation forclear and cloudy skies. In Proceedings of the 2nd national passive88Bibliographysolar conference (AS/ISES), volume 396, pages 675–678. Universityof Pensssylvania, 1978.[45] X. Berger, D. Buriot, and F. Garnier. About the equivalent radiativetemperature for clear skies. Solar Energy, 32:725–733, 1984.[46] S. Niemela¨, Ra¨isa¨nen P., and H. Savija¨rvi. Comparison of surfaceradiative flux parameterizations part 1: longwave radiation. AtmosRes, 58:1–18, 2001.[47] J.P. Lhomme, J.J. Vacher, and A. Rocheteau. Estimating downwardlong-wave radiation on the andean altiplano. Agric For Meteorol,145:139–148, 2007.[48] F. Kasten and G. Czeplak. Solar and terrestrial radiation dependenton the amount and type of cloud. Solar Energy, 24:177–189, 1980.[49] J.R. Howell, K. Daun, M.P. Mengu¨c, and R. Siegel, editors. ThermalRadiation Heat Transfer. CRC Press/Taylor Francis, Boca Raton,FL, 7th edition, 2021.[50] Lawrence Berkeley National Laboratory and U.S. Department of En-ergy. Energyplus: Building energy simulation software. https://energyplus.net, 2024. Available under an open-source license.[51] D. Meyer and D Thevenard. Psychrolib: a library of psychrometricfunctions to calculate thermodynamic properties of air, 2020. Version2.5.0.[52] William F. Holmgren, Clifford W. Hansen, and Mark A. Mikofski.pvlib python: a python package for modeling solar energy systems,April 2018.[53] D. Zhao, A. Aili, Y. Zhai, J. Lu, D. Kidd, G. Tan, X. Yin, and R. Yang.Subambient cooling of water: Toward real-world applications of day-time radiative cooling. Joule, 3(1):111–123, 2019.[54] S. Kumar and S.C. Mullick. Wind heat transfer coefficient in solarcollectors in outdoor conditions. Solar Energy, 84:956–963, 2010.[55] S. Armstrong and W.G. Hurley. A thermal model for photovoltaicpanels under varying atmospheric conditions. Applied Thermal Engi-neering, 30:1488–1495, 2010.89Bibliography[56] Eriksson T.S. and C.G. Granqvist. Radiative cooling computed formodel atmospheres. Appl. Opt., 21:4381–4388, 1982.[57] E. Erell and Y. Etzion. Radiative cooling of buildings with flat-platesolar collectors. Building and Environment, 34:297–305, 2000.[58] Plasti-Fab. Duro-span 250 specifications, 2024.[59] J. Liu, J. Zhang, D. Zhang, S. Jiao, J. Xing, H. Tang, Y. Zhang, S. Li,Z. Zhou, and J. Zuo. Sub-ambient radiative cooling with wind cover.Renewable and Sustainable Energy Reviews, 130, 2020.[60] J.L. Kou, Z. Jurado, and et al. Daytime radiative cooling using near-black infrared emitters. ACS Photonics, 4:626—-630, 2017.[61] Z. Chen, L. Zhu, and A. Raman. Radiative cooling to deep sub-freezingtemperatures through a 24-h day–night cycle. Nat Commun, 7:13729,2016.[62] E. Teitelbaum, H. Alsaad, D. Aviv, and et al. Addressing a systematicerror correcting for free and mixed convection when measuring meanradiant temperature with globe thermometers. Scientific Reports, 12,2022.[63] K. Campbell. Statistical calibration of computer simulations. Relia-bility Engineering System Safety, 91:1358–1363, 2006.[64] D. Higdon, C. Nakhleh, and et al. A bayesian calibration approach tothe thermal problem. Computer Methods in Applied Mechanics andEngineering, 197:2431–2441, 2008.[65] M.C. Kennedy and A. O’Hagan. Bayesian calibration of computermodels. J R Stat Soc B, 63:425–464, 2001.[66] J. Wang, Z. Zhou, and J. Zhao. A method for the steady-state thermalsimulation of district heating systems and model parameters calibra-tion. Energy Conversion and Management, 120:294–305, 2016.[67] K.F. Man, K.S. Tang, and S. Kwong. Genetic algorithms: conceptsand applications. IEEE Trans Industr Electron, 43:519–534, 1996.[68] J. Sun and T.A. Reddy. Calibration of building energy simulationprograms using the analytic optimization approach (rp-1051). HVACRResearch, 12:177–196, 2006.90Bibliography[69] Denon Sheppard. Integrating membrane-assisted radiant cool-ing panels in building energy simulation, 2020. Retrieved fromhttps://open.library.ubc.ca/collections/ubctheses/24/items/1.0395533.[70] F. Campolongo, J. Cariboni, and A. Saltelli. An effective screening de-sign for sensitivity analysis of large models. Environmental modellingsoftware, 22:1509—-1518, 2007.[71] J.V. Beck and K.J. Arnold. Parameter Estimation in Engineering andScience. John Wiley Sons, Inc., 1977.[72] Takuya Akiba, Shuji Suzuki, and Keisuke Fukuda. Optuna: A next-generation hyperparameter optimization framework. Proceedings ofthe 25th ACM SIGKDD International Conference on Knowledge Dis-covery & Data Mining, pages 2623–2631, 2019.[73] Amy S. Fleischer. Thermal Energy Storage Using Phase Change Ma-terials. Springer, 2015.[74] Harald Mehling and F. Luisa Cabeza. Heat and cold storage withPCM. Springer Berlin, Heidelberg, 2010.[75] S. Deng, C. Nie, H. Jiang, and W-B. Ye. Evaluation and optimizationof thermal performance for a finned double tube latent heat thermalenergy storage. Int J Heat Mass Transfer, 130:532–544, 2019.[76] K. Yanbing, J. Yi, and Z. Yinping. Modeling and experimental studyon an innovative passive cooling system—nvp system. Energ Buildings,35:417––425, 2003.[77] A.A. Ghoneim, S.A. Kllein, and J.A. Duffie. Analysis of collec-tor—storage building walls using phase change materials. Solar En-ergy, 47:237—-242, 1991.[78] K.O. Lee, M.A. Medina, E. Raith, and X. Sun. Assessing the inte-gration of a thin phase change material (pcm) layer in a residentialbuilding wall for heat transfer reduction and management. AppliedEnergy, 137:699—-706, 2015.[79] Shuo Zhang and Jianlei Niu. Cooling performance of nocturnal radia-tive cooling combined with microencapsulated phase change material(mpcm) slurry storage. Energy and Buildings, 54:122–130, 2012.91Bibliography[80] Farah Souayfane, Farouk Fardoun, and Pascal-Henry Biwole. Phasechange materials (pcm) for cooling application in buildings: A review.Energy and Buildings, 129:396–431, 2016.[81] M. Goswami, N. Kumar, and et al. Understanding supercooling mech-anism in sodium sulfate decahydrate phase-change material. J. Appl.Phys., 129:245109, 2021.[82] K. Pielichowska and K. Pielichowska. Phase change materials for ther-mal energy storage. Progress in Materials Science, 65:67–123, 2014.[83] H.W. Ryu, S.W. Woo, B.C. Shin, and S.D. Kim. Prevention of su-percooling and stabilization of inorganic salt hydrates as latent heatstorage materials. Sol Energy Mater sol Cells, 27:161–172, 2021.[84] D. Akamo, N. Kumar, and et al. Stabilization of low-cost phase changematerials for thermal energy storage applications. iScience, 26:107175,2023.[85] B.K. Purohit and V.S. Sistla. Inorganic salt hydrate for thermal energystorage application: A review. Energy Storage, 3:212, 2021.[86] M. Farid, A. Khudhair, S. Razack, and S. Al-Hallaj. A review on phasechange energy storage: materials and applications. Energy Conversionand Management, 45:1597–1615, 2004.[87] X. Dong, J. Mao, and et al. Study on performance optimization ofsodium sulfate decahydrate phase change energy storage materials.Journal of Thermal Analysis and Calorimetry, 143:3923—-3934, 2021.[88] M.G. De Paola, N. Arcuri, V. Calabro`, and M. De Simone. Thermaland stability investigation of phase change material dispersions forthermal energy storage by t-history and optical methods. Energies,10:354, 2017.[89] A. Ilangovan, S. Hamdane, and et al. Promising and potential ap-plications of phase change materials in the cold chain: a systematicreview. Energies, 15, 2022.[90] J. Thakkar, S.B. Annavajjala, and et al. Stabilizing a low tempera-ture phase change material based on glaubers salt. Journal of EnergyStorage, 91:111936, 2024.92Bibliography[91] P. Marliacy, R. Solimando, M. Bouroukba, and L. Schuffenecker.Thermodynamics of crystallization of sodium sulfate decahydrate inh2o–nacl–na2so4: application to na2so4·10h2o-based latent heat stor-age materials. Thermochimica Acta, 34:85–94, 2000.[92] P. Bharmoria, P.S. Singh, H. Gupta, and A. Kumar. Temperature-dependent solubility transition of na2so4 in water and the effect of nacltherein: Solution structures and salt water dynamics. The Journal ofPhysical Chemistry B, 118:12734—-12742, 2014.[93] Z. Jiang and S. Tie. Preparation and thermal properties of glauber’ssalt-based phase-change materials for qinghai–tibet plateau solargreenhouses. International Journal of Modern Physics B, 31:1744085,2017.[94] A.S. Deepa and A. Tewari. Phase transition behaviour of hydratedglauber’s salt based phase change materials and the effect of ionicsalt additives: A molecular dynamics study. Computational MaterialsScience, 203:111112, 2022.[95] NightHawkInLight. Diy supermaterial could save you from heatstroke:Salt based pcms’, 2024. Accessed: 2024-09-12.[96] N. Beaupere, U. Soupremanien, and L. Zalewski. Nucleation trigger-ing methods in supercooled phase change materials (pcm), a review.Thermochim. Acta, 670:184–201, 2018.[97] S.D. Sharma, H. Kitano, and K. Sagara. Phase change materials forlow temperature solar thermal applications. ResRepFacEngMie Univ.,29:31–64, 2004.[98] Y. Zhang and Y. Jiang. A simple method, the t-history method, ofdetermining the heat of fusion, specific heat and thermal conductiv-ity of phase-change materials. Measurement Science and Technology,10:201–205, 1999.[99] Climator AB. ClimSel21 Product, 2024. Accessed: November 20,2024.[100] The Engineering Toolbox. Solids - specific heats, 2024. Accessed:November 20, 2024.93Bibliography[101] John R. Rumble and ed., editors. CRC Handbook of Chemistry andPhysics. CRC Press/Taylor Francis, Boca Raton, FL, 105th edi-tion, 2024. THERMOPHYSICAL PROPERTIES OF WATER ANDSTEAM, Table 1.[102] American Society of Heating, Refrigerating and Air-Conditioning En-gineers. ANSI/ASHRAE Standard 55-2020: Thermal EnvironmentalConditions for Human Occupancy. ASHRAE Inc., 2010.[103] E. Johansson, S. Thorsson, R. Emmanuel, and E. Kru¨ger. Instru-ments and methods in outdoor thermal comfort studies - the need forstandardization. Urban Climate, 10:346–366, 2014.[104] T.S. Saitoh, T. Shimada, and H. Hoshi. Modeling and simulation ofthe tokyo urban heat island. Atmospheric Environment, 30:3431–3442,1996.[105] A. Mochida and I.Y.F. Lun. Prediction of wind environment andthermal comfort at pedestrian level in urban area. Journal of WindEngineering and Industrial Aerodynamics, 96:1498–1527, 2008.[106] ENVI-met GmbH. ENVI-met 4.4 - Microclimate and EnvironmentalModeling. Essen, Germany, 2024. https://www.envi-met.com.[107] A. Matzarakis, F. Rutz, and H. Mayer. Modelling radiation fluxes insimple and complex environments—basics of the rayman model. IntJ Biometeorol, 54:131—-139, 2010.[108] F. Lindberg, B. Holmer, and S. Thorsson. Solweig 1.0—modellingspatial variations of 3d radiant fluxes and mean radiant temperaturein complex urban settings. Int J Biometeorol, 52:697—-713, 2008.[109] M. Park, A. Hagishima, J. Tanimoto, and K.I. Narita. Effect of urbanvegetation on outdoor thermal environment: field measurement at ascale model site. Build. Environ., 56:38–46, 2012.[110] E. Johansson. Influence of urban geometry on outdoor thermal comfortin a hot dry climate: a study in fez, morocco. Build. Environ., 41:1326–1338, 2006.[111] A. Qaid, H.B. Lamit, D.R. Ossen, and R.N.R. Shahminan. Urbanheat island and thermal comfort conditions at micro-climate scale ina tropical planned city. Energ. Buildings, 133:577–595, 2016.94Bibliography[112] J.A. Voogt and T.R. Oke. Thermal remote sensing of urban climates.Remote Sens. Environ., 86:370–384, 2003.[113] M. Santamouris, editor. Energy and Climate in the Urban Built En-vironment. Routledge, Abingdon, United Kingdom, 2013.[114] Y. Wang, U. Berardi, and H. Akbari. Comparing the effects of urbanheat island mitigation strategies for toronto, canada. Energ. Buildings,114:2–19, 2016.[115] D.D. Milosˇevic´, I.V. Bajˇsanski, and S.M. Savic´. Influence of changingtrees locations on thermal comfort on street parking lot and footways.Urban For. Urban Green., 23:113–124, 2017.[116] Y. Wang and H. Akbari. The effects of street tree planting on urbanheat island mitigation in montreal. Sustain. Cities Soc., 27:122–128,2016.[117] M.F. Shahidan, M.K. Shariff, P. Jones, E. Salleh, and A.M. Abdullah.A comparison of mesua ferrea l. and hura crepitans l. for shade creationand radiation modification in improving thermal comfort. Landsc.Urban Plan., 97:168–181, 2010.[118] L. Kong, K.K.L. Lau, C. Yuan, Y. Chen, Y. Xu, C. Ren, and E. Ng.Regulation of outdoor thermal comfort by trees in hong kong. Sustain.Cities Soc., 31:12–25, 2017.[119] T.R. Oke, editor. Boundary Layer Climates. Routledge, Abingdon,United Kingdom, 20002.[120] L.V. de Abreu-Harbich, L.C. Labaki, and A. Matzarakis. Effect oftree planting design and tree species on human thermal comfort in thetro-pics. Landsc. Urban Plan., 138:99–109, 2015.[121] L. Bianco, V. Serra, F. Larcher, and M. Perino. Thermal behaviourassessment of a novel vertical greenery module system: first resultsof a long-term monitoring campaign in an outdoor test cell. Energ.Effic., 10:625–638, 2017.[122] G.M. Heisler, S. Grimmond, R.H. Grant, and C. Souch. Investigationof the influence of chicago’s urban forests on wind and air temperaturewithin residential neighborhoods. Chicago’s Urban Forest Ecosystem:Results of the Chicago Urban Forest Climate Project, page 19, 1994.95Bibliography[123] T.E. Morakinyo, K.K.C. Dahanayake, O.B. Adegun, and A.A. Ba-logun. Modelling the effect of tree-shading on summer indoor andoutdoor thermal condition of two similar buildings in a nigerian uni-versity. Energ. Buildings, 130:721–732, 2016.[124] M.V. Monteiro, T. Levanicˇ, and K.J. Doick. Growth rates of commonurban trees in five cities in great britain: A dendrochronological eval-uation with an emphasis on the impact of climate. Urban ForestryUrban Greening, 22:11–23, 2017.[125] P. Symes and G. Connellan. Water management strategies for urbantrees in dry environments: Lessons for the future. Arboriculture UrbanForestry, 39:116–124, 2013.[126] P.K. Cheung and C.Y. Jim. Comparing the cooling effects of a treeand a concrete shelter using pet and utci. Building and Environment,130:49–61, 2018.[127] M. Ka´ntor, A. Kova´cs, and A. Taka´cs. Small-scale human-biometeorological impacts of shading by a large tree. Open Geo-sciences, 8:231–245, 2016.[128] M.C. Peel, B.L. Finlayson, and T.A. McMahon. Updated world map ofthe ko¨ppen-geiger climate classification. Hydrology and Earth SystemSciences, 11:1633–1644, 2007. Modern update to the Ko¨ppen-Geigerclassification system.[129] P.O. Fanger. Thermal Comfort: Analysis and Applications in Envi-ronmental Engineering. Danish Technical Press, Copenhagen, 1970.[130] O. Potchter, P. Cohen, T.P. Lin, and A. Matzarakis. Outdoor humanthermal perception in various climates: a comprehensive review ofapproaches, methods and quantification. Sci. Total Environ., 631:390–406, 2018.[131] P. Ho¨ppe. The physiological equivalent temperature - a universal in-dex for the biometeorological assessment of the thermal environment.International Journal of Biometeorology, 43:71–75, 1999.[132] A. Matzarakis, H. Mayer, and M.G. Iziomon. Applications of a uni-versal thermal index: physiological equivalent temperature. Int. J.Biometeorol., 43:76––84, 1999.96Bibliography[133] F. Tartarini and S. Schiavon. pythermalcomfort: A python packagefor thermal comfort research. SoftwareX, 12:100578, 2020.[134] H. Mayer and P. Ho¨ppe. Thermal comfort of man in different urbanenvironments. Theor Appl Climatol, 38:43–49, 1987.[135] C. Liu, Y. Wu, and et al. Effect of atmospheric water vapor on radia-tive cooling performance of different surfaces. Solar Energy, 183:218–225, 2019.[136] M. Hu and et al. Parametric analysis and annual performance eval-uation of an air-based integrated solar heating and radiative coolingcollector. Energy, 165:811–824, 2018.[137] Y. Peng, J. Chen, and et al. Nanoporous polyethylene microfibres forlarge-scale radiative cooling fabric. Nat. Sustain., 1:105–112, 2018.[138] P.-C. Hsu and X. Li. Photon-engineered radiative cooling textiles.Science, 370:784–785, 2020.[139] M. Wei, W. Wu, and et al. Universal strategy for all-weather and all-terrain radiative cooling with non-reciprocal mid-infrared windows.Sol. Energy, 207:471–478, 2020.[140] Z. Zhan, M. ElKabbash, and et al. Enhancing thermoelectric outputpower via radiative cooling with nanoporous alumina. Nano Energy,65:104060, 2019.[141] J.L. Beverly, M.D. Flannigan, and et al. The association betweennorthern hemisphere climate patterns and interannual variability incanadian wildfire activity. Can J For Res, 41:2193––2201, 2011.[142] X. Wang, D. Thompson, and et al. Increasing frequency of extremefire weather in canada with climate change. Climatic Change, 130:573––586, 2015.[143] I.G. McKendry and A. Christen. Impacts of an intense wildfire smokeepisode on surface radiation, energy and carbon fluxes in southwesternbritish columbia, canada. Atm. Chem. and Phys., 19:835–846, 2019.[144] Thermal Energy System Specialists (TESS). TRNSYS 18: A Tran-sient System Simulation Program. University of Wisconsin-Madison,Madison, WI, USA, 2020. Version 18.97[145] T. Li, Y. Zhai, and et al. A radiative cooling structural material.Science, 364:760–763, 2019.[146] A. Baniassadi, D.J. Sailor, and G.A. Ban-Weiss. Potential energy andclimate benefits of super-cool materials as a rooftop strategy. UrbanClim., 29:100495, 2019.[147] J. Mandal, Y. Yang, and et al. Paints as a scalable and effectiveradiative cooling technology for buildings. Joule, 4:1350–1356, 2020.[148] Z. Zhou and X. Wang. Transparent polymer coatings for energy-efficient daytime window cooling. Cell Rep. Phys. Sci., 1:100231, 2020.[149] D. Zhao, A. Aili, and et al. Roof-integrated radiative air-cooling sys-tem to achieve cooler attic for building energy saving. Energy Build,203:109453, 2019.[150] MatWeb. Thermal properties: Borosilicate float glass.https://www.matweb.com/search/datasheet_print.aspx?matguid=7546f96147b847a5ab00091f3c36b8ce. Accessed: 2024-11-04.[151] NightHawkInLight. Making infrared cooling paint from grocery storeitems (w/novel caco3 microsphere synthesis), 2024. Accessed: 2024-09-12.[152] Tech Ingredients. Air conditioning isn’t free... but we’re close, 2023.Accessed: 2024-09-12.98Appendix AAdditional CalculationsA.1 PET Interpretation ScaleThe following figure is presented in the work of Ka´ntor et al. [127] to aid ininterpreting the human comfort response thresholds for PET results.Figure A.1: PET interpretation scale and how PET compares outdoor ther-mal comfort to an indoor reference environment.A.2 T-History Calculationscps,PCM =mwcpl,w +mtubecps,tubemPCMA3A5− mtubemPCMcps,tube (A.1)cps,PCM : solid specific heat of PCM[JgK]99A.2. T-History Calculationscpl,w: liquid specific heat of distilled water[JgK]cps,tube: solid specific heat of test tube glass[JgK]mw: mass of distilled water [grams]mtube: mass of test tube [grams]mPCM : mass of PCM [grams]A3: area under temp-time curve, PCM solid [◦C-second]A5: area under temp-time curve, water phase-change [◦C-second]cpl,PCM =mwcpl,w +mtubecps,tubemPCMA1A4− mtubemPCMcps,tube (A.2)cpl,PCM : liquid specific heat of PCM[JgK]A1: area under temp-time curve, PCM liquid [◦C-second]A4: area under temp-time curve, water up to phase-change [◦C-second]Hf,PCM =mwcpl,w +mtubecps,tubemPCMA2A4(To−Tm,1)−mtubecps,tube(Tm,1 − Tm,2)mPCM(A.3)Hf,PCM : enthalpy of fusion of PCM[Jg]A2: area under temp-time curve, PCM phase change [◦C-second]To: initial temperature [◦C]Tm,1: PCM phase change start temperature [◦C]Tm,2: PCM phase change end temperature [◦C]A visual representation of the integration calculations for the PCM anddistilled water are provided in Fig A.2.A1 =∫ t1to(TPCM − T∞)dt (A.4)100A.2. T-History CalculationsFigure A.2: (left) PCM cooling phase integration areas with phase changebased timing marks noted. (right) distilled water reference material coolingphase integration areas.A2 =∫ t2t1(TPCM − T∞)dt (A.5)A3 =∫ tendt2(TPCM − T∞)dt (A.6)to: experiment start time, directly before cooling begins [seconds]t1: time at which phase change begins [seconds]t2: time at which phase change ends [seconds]tend: experiment end time, equilibrium [seconds]TPCM : PCM temperature [◦C]T∞: ice bath temperature, boundary condition [◦C]A4 =∫ t1to(TH2O − T∞)dt (A.7)A2 =∫ t2t1(TH2O − T∞)dt (A.8)101A.3. Estimating Ambient Temperature during Tilt TrialTH2O: Distilled water temperature [◦C]Relevant material properties and calculation inputs for the R1.1 formulationT-history calculations are detailed in Table A.1.Input Value [unit] NotesmPCM 75.5 [grams]cps,tube 0.83[JgK]Schott BOROFLOAT® [150]cpl,w 4.18[JgK]Thermophysical Properties at 20◦C [101]mtube 41 [grams]mw 61 [grams]Table A.1: R1.1 T-history calculation inputs and references.A.3 Estimating Ambient Temperature duringTilt TrialThe local ambient temperature for the tilt trial data on September 06 wasestimated as an average of regional ESB and local VelociCalc recorded tem-peratures. This estimation had calibration factors based on the solar irra-diance and local wind speed, as per Eqn. A.9.T ∗local,amb = (TESB+TV elociCalc)/2−csolar ∗(Isolar/Isolar,max)−dwind∗(vlocal)(A.9)T ∗local,amb: simulated local ambient temperature [◦C]TESB: regional ambient dry-bulb temperature [◦C]TV elociCalc: TSI VelociCalc local ambient temperature [◦C]csolar: solar absorption calibration coefficient [◦C]dwind: wind speed calibration coefficient [◦Csm ]Isolar,max: maximum solar irradiance for that day[Wm2K]102A.3. Estimating Ambient Temperature during Tilt Trialvwind: local wind speed [m/s]This equation was calibrated using clearsky data from July 20 and August03. The coefficients that gave the minimum error between the simulatedlocal ambient temperature and the recorded local ambient temperature wascsolar = 1.2 and dwind = 0.8. These coefficients gave e = 0.59◦C and anemax = 2.7◦C. While not perfect, this approximation is significantly betterthan either the VelociCalc temperature with e = 2.4 ◦C and emax = 7.6 ◦Cor the ESB regional temperature with e = 1.5 ◦C and emax = 4.8 ◦C.103Appendix BField Testing TimelineFigure B.1: A general timeline of the field testing during the summer of2024.104Appendix CCooling Paint InvestigationBecause high-performance sky-cooling materials are not widely available yet,the original concept for this work was to synthesize an ultra-white ’paint’ toserve as the cooling surface. The methods for creating this surface coatingwere taken from the YouTube explorations of NightHawkInLight’s video ti-tled ”Making Infrared Cooling Paint From Grocery Store Items (w/NovelCaCO3 Microsphere Synthesis)” [151] and ”Air Conditioning isn’t free... butwe’re close” by Tech Ingredients [152]. Both these YouTube educators areloosely reverse-engineering the cooling surface coating developed at Purdue[17]. The value of the Purdue approach is a combination of simplicity andperformance, achieving excellent daytime cooling potential without the useof complicated multi-layer photonics or lab-grade manufacturing. The coat-ing is made up of 2 parts: the micro-spheres white pigment and the acrylicmatrix. The pigment does some solar reflection but is chiefly responsible for8-13µm IR emission, and can be BaSO4 like Purdue’s coating or CaCO3.The acrylic matrix provides a structure for the pigment and contributes tosolar reflectivity through a ’snow-scattering’ effect. The coating is made by’melting’ acrylic with a solvent such as dimethylformamide or acetone. Thepigment is mixed in to this syrup-like liquid, and applied to a surface. Thesolvent then evaporates, with the acrylic forming crystals that reflect lightthrough a series of partial reflections.A CaCO3 micro-spheres pigment was created as per NightHawkInLight’sinstructions by mixing Calcium Chloride and Citric Acid with Calcium Car-bonate, each dissolved in distilled water. This liquid is then immediatelymixed in a blender to influence the size of the micro-sphere crytilization.Mixing for longer creates smaller micro-spheres, thus 1 minute, 5 minute,and 8 minute mixings are combined in a 9:3:1 ratio to maximize packingdensity. The now milky liquid is allowed to settle, then poured off and re-filled with distilled water 2 times to clean off impurities. The final liquidis poured into a filter setup made from printer paper placed in a loaf panwith a slot cut in the bottom. Once all the free water has been strained105Appendix C. Cooling Paint Investigationout through gravity, the filter cake is placed in a household dehydrator for72 hours, until pigment is powdery and crumbles easily off the filter paper.Because quality control and evaluation of the final pigment was challengingwith this DIY option, and due to the time and space required to make asuitable volume of pigment, a commercial BaSO4 nanoparticle pigment wasalso purchased for testing from Chemazone Inc. with an average particlesize of 500nm.Paint variations were tested on 10cm x 10cm aluminum sheet metal pieces,and some plywood veneer as well. Surface preparation was cleaning withacetone then sanding to rougher the surface with 120 grit sandpaper, theneither a self-etching primer or sanding an epoxy resin into the substrateand allowing it to cure. The epoxy preparation had superior adhesion toaluminum, which was difficult to work with, but was messy and not easyto scale up in size. Multiple matrix options were trialed, including acrylicdissolved in acetone, acrylic dissolved in dimethylformamide, acrylic paintbase, and polyurethane clear base. Application methods were tested rangingfrom pouring into a mold like Purdue described [17] to spraying with a paintsprayer. Many ratios of solvent to acrylic and pigment weight % were alsoexplored. These test samples were intended to be compared against oneanother in a mini version of the eventual rooftop setup and their steady-state temperatures compared on a clear sky day to inform which combi-nation resulted in a superior coating as seen in Fig C.1. However, the 3biggest obstacles found before that final information was determined were1) cure shrinkage and resulting loss of substrate adhesion 2) durability and3) scalability. Although acetone is faster at initially dissolving the acrylic,it was found that dimethylformamide evaporated slower, which resulted ina more stable coating and less cure shrinkage. Nonetheless, all options wereextremely delicate, and suffered from a general lack of consistency and ad-hesion as demonstrated in Fig C.1. In terms of scalability, it was determinedthat for a larger architectural research application, such as a full-size cool-ing panel instead of a small materials testing wafer, it would take too manyresources to produce that volume of paint even if it did function as designed.106Appendix C. Cooling Paint InvestigationFigure C.1: (left) Cooling paint variation testing with multiple versionssimultaneously (right) typical cooling paint cure-shrinkage failure mode107Appendix DWind Sensor AccuracyLimitationsA protective metal cap on the TSI VelociCalc wind speed probe restrictsaccurate readings to air flow directions within a 90-degree cone on either sideof the shroud, as shown in Fig. D.1. This is because this type of instrumentis typically used in ventilation ducting applications, and removing the shroudwould make the delicate sensing element extremely vulnerable to damage.To compensate for this, the wind speed sensor was manually adjusted toface the dominant wind direction as given by a wind indicator placed on theCIRS roof, and checked multiple times per day while in use.108Appendix D. Wind Sensor Accuracy LimitationsFigure D.1: An illustration of the direction-based error introduced by theprotective shroud on the TSI VelociCalc probe end.109Appendix EConvective Shield TrialE.1 MethodsBecause the literature described an IR-transparent convective shield as acommon performance enhancing intervention, several days in late July werededicated to an initial test of a shield of this type. It was challenging tosource a material with known transmissivity properties, especially spectrallydefined, in the correct size to allow full coverage of the cooling panel. Ear-lier, during the initial suspended panel attempt, household cling wrap wasused as a prototype shield, due to its extremely low thickness. However,because it was only available in 18” wide rolls, the shield was formed frommultiple strips which did not hold together well at the seams. The result-ing poor tension allowed the shield to be easily ’pumped’ by the wind likea trampoline, and ultimately was ripped away after only 24 hours in mildwind conditions. A re-designed shield was implemented for this trial, us-ing 2 pieces of thin polyolefin heat welded together and stretched across aplywood frame with ultra-sticky VHB tape at the perimeter. This mate-rial was chosen because it was readily available from the shipping/receivingdepartment of the UBC bookstore, had extremely high visual clarity, andwas heat-formable, allowing a low-profile seam and post-tensioning of thematerial once it was adhered to the frame. Critically, this material wasalso very thin at 0.6 mil as measured with a calibrated micrometer. Theshield was designed to fit snugly within the cooling panel frame, creatinga reasonable seal while maintaining simple installation and removal. Smallrigid insulation spacers were used to place the shield at 1” above the coolingpanel surface, and only a single air gap depth was tested.Based on the low performance of the panel in during the limited field datathat was gathered, no adaptation or calibration of the thermal model wasattempted. Moreover, details of the material’s transmissivity were not ableto be obtained from the manufacturer and the project did not have access tothe FTIR measurement tools needed to establish radiative properties defini-110E.2. Resultstively. Due to limited time, no other methods for determining approximatetransmissivity were engaged.E.2 ResultsThe recorded panel and ambient temperatures over the test period are shownin Fig. E.1. The temperature difference between the panel and the localambient temperature is plotted for reference in Fig. E.2Figure E.1: A comparison of the average panel temperature and the localambient temperature with solar irradiance during July 26 to 28.Peripheral weather conditions to note during this trial period are increasinghaze over July 26 and 27, possibly either pollution or forest fire smoke, andmedium cloud conditions on July 28, with an estimated 50% cloud coveragethroughout the day. Although the undefined ’haze’ was recorded in the fieldnotes, quantification of this phenomenon was not pursued for this work,beyond comparing solar irradiance magnitudes against a known clearskyday. Similar to the cloud cover trials, no cloud is available at night.111E.3. DiscussionFigure E.2: The temperature difference between the cooling panel surfaceand the local ambient temperature during July 26 to 28, with a negativedifference meaning the panel temperature is lower than the local air tem-perature.E.3 DiscussionThe shielded cooling panel did not experience the expected performancegain, but in fact lost performance and routinely exceeded the ambient tem-perature at peak solar irradiance by as much as 5 degrees. The most obviousfactor that caused this was the insufficient material transmissivity. In the IRwavelength range this would modulate the cooling surface’s ability to emitradiation by either reflecting or absorbing and re-emitting the radiation.Similarly, in the solar wavelength range the material could have absorbedand re-emitted solar radiation, and also trapped solar radiation throughcounter-reflection when the cooling surface reflected the portion that wastransmitted initially. It is also possible that the air gap between the shieldand cooling surface was heating up due to solar gains of the participatingmedia, trapped IR within the cavity, and conduction through the insulatedsidewall, suppressing the cooling performance. The temperature of the air112E.3. Discussionwithin the air gap was not directly measured during this trial, but that couldbe a useful data point in future work. Adjusting the thickness of the air gapwould also undoubtedly impact the performance of the convection shield.Conventional theory indicates that for a cooling surface that is colder thanthe air gap above it, the buoyancy effects within the air gap would dictatethat the air stagnate and stratify. If this did occur, than a thicker air gapwould represent more insulation against the ambient air and an increasein panel performance. However, since it is likely that the sidewalls andair gap medium are absorbing solar radiation, and the panel surface wasabove ambient, significant internal convection within the air gap may haveoccurred. This presents an intriguing thermal modelling problem but wasdeemed outside the scope of this work.On a practical note, a convective shield is a difficult design problem in ap-plication. Because the IR transmissivity, and consequently effective radiantcooling performance, is heavily dependent on material thickness as describedby Beer’s Law in Equ E.1 it is necessary to make the shield as thin as pos-sible, ideally below 1 mil.τλ = e−kλL (E.1)τλ: spectral transmissivitykλ: spectral absorption coefficientL: material thickness [m]That thickness constraint results in convection shields that are delicate, andare extremely susceptible to damage from snow, heavy rain, strong winds,falling debris, cleaning procedures, and transportation. Furthermore, wefound sourcing a suitable material to be challenging, with transmissivitydata rare and sizing often constraining panel dimensions. Lastly, in the con-text of the intended transient outdoor application of the cooling panel setup,convection-based parasitic performance losses may not be a critical factor.It can be argued that in a design case where there are significant ambientwind conditions, that same wind would help maintain the thermal comfortof an occupant, reducing the reliance of the system on the radiant coolingprovided by the roof surface. This would rely on a cooling shelter designthat balances wind permeability with solar and ambient IR protection.113Appendix FAdditional Field TestingResultsIncluded here are additional field testing and calibration results for datesnot shown in the main text.F.1 Individual Panel TemperaturesFig. F.1 shows an example of the spread between individual HOBO tem-perature sensors. While they are reasonably close to each other during thenight and morning, there is some significant divergence as night falls. Thereis directionality to the incident solar radiation and some differences in shad-ing based on nearby research instrumentation for another project that couldhelp account for this difference. HOBO 1 and 4 are the 2 northern (closestto the CIRS door) probes, and HOBO 2 and 3 are the southern. That meansthat HOBO 2 and 3 are shaded faster as the sun sets. This does not fullyaccount for the anomalies seen but undoubtedly contributes. Overall, theuse of the average surface temperature for the thermal model appears to bejustified and reasonable.114F.2. Baseline Calibration Results and Heat FluxesFigure F.1: The 4 individual HOBO panel surface temperature probe tem-peratures between 0900 and 1900 on July 20.F.2 Baseline Calibration Results and Heat Fluxes115F.2. Baseline Calibration Results and Heat FluxesFigure F.2: (top) A comparison of the model predicted panel temperaturesand the known (non-PCM) panel temperatures for August 03. (bottom)The accompanying modelled heat fluxes.116F.2. Baseline Calibration Results and Heat FluxesFigure F.3: (top) A comparison of the model predicted panel temperaturesand the known (non-PCM) panel temperatures for August 10. (bottom)The accompanying modelled heat fluxes.117
Thesis/Dissertation
eng
Mechanical Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Rysanek, Adam
Rogak, Steve
Achieving daytime thermal comfort with radiant sky-cooling
Text