Microclimate Analysis As a Design Driver of Architecture

Technical Note Microclimate Analysis as a Design Driver of Architecture

Jonathan Graham 1, Umberto Berardi 2,* , Geoﬀrey Turnbull 1 and Robert McKaye 1

1 KPMB Architects, Toronto, ON M5A 0L6, Canada; [email protected] (J.G.); [email protected] (G.T.); [email protected] (R.M.) 2 Faculty of Engineering and Architectural Science, Ryerson University, Toronto, ON M5B 2K3, Canada * Correspondence: [email protected]; Tel.: +1-416-979-5000 (ext. 553263)

Received: 20 April 2020; Accepted: 1 June 2020; Published: 3 June 2020

Abstract: In the context of global climate change, it is increasingly important for architects to understand the effects of their interventions on indoor and outdoor thermal comfort. New microclimate analysis tools which are gaining appreciation among architects enable the assessment of different design options in terms of biometeorological parameters, such as the Universal Thermal Climate Index (UTCI) and the Outdoor Thermal Comfort Autonomy. This paper reflects on some recent experiences of an architectural design office attempting to incorporate local climatic considerations as a design driver in projects. The investigation shows that most of the available tools for advanced climatic modelling have been developed for research purposes and are not optimized for architectural and urban design; consequently, they require adaptations and modifications to extend their functionality or to achieve interoperability with software commonly used by architects. For this scope, project-specific Python scripts used to extract design-consequential information from simulation results, as well as to construct meteorological boundary conditions for microclimate simulations, are presented. This study describes the obstacles encountered while implementing microclimate analysis in an architectural office and the measures taken to overcome them. Finally, the benefits of this form of analysis are discussed.

Keywords: urban microclimate; outdoor thermal comfort; universal thermal climate index; architecture; urban design

1. Introduction The forecasts of global climate change have made it clear that the climatic conditions to which buildings are exposed are becoming increasingly dynamic, and that these variabilities require thorough consideration in the architectural design practice [1]. An additional source of uncertainty exists in the micro-scale variation in climate in urban areas. The microclimatic conditions experienced in outdoor urban spaces are inextricably linked to the form and materiality of buildings and hardscape. As such, early decisions in the architectural design process can enhance or compromise thermal comfort in outdoor spaces such as courtyards, streets, and patios. For example, a tall structure with no podium can cause a down washing of wind at ground level, making al fresco dining feel intolerably cold on an otherwise pleasant day [2]. On the other hand, a courtyard with an overhang can offer shade during the summer or appropriate solar exposition during the shoulder seasons, effectively extending the annual period of use [3]. Given the increasing evidence of the relationship between human wellbeing and access to urban greenspace [4], it is of increasing value to architects and their clients to understand and design thermally comfortable outdoor urban spaces through microclimate analyses [5]. Microclimate analyses involve analyzing the effects of architectural interventions on local wind flow and radiative fluxes at high spatial and temporal resolutions [6].

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In this paper, the experiences of a large Canadian architectural firm are used as a case study to show how microclimate analysis can be used in today’s practice. Central to the workflow is an assortment of software and plugins for simulating solar radiation, wind, and greenery evapotranspiration among others. In this regard, comprehensive tools such as ENVI-met [7] are geared more toward scientific applications rather than architectural design, and as such, ways to adapt their use in professional office environments are required. The purpose of this paper is to highlight the unique demands placed on holistic microclimate simulation tools when deployed in a design office context through the recollections of challenges and experiences. Firstly, a survey of environmental and microclimate analysis tools is provided. Then, different stages of the microclimate simulation process are discussed, focusing on ways to assemble meteorological boundary conditions, extract graphical results, and develop human-centric metrics for thermal comfort. In addition, this paper describes approaches for synergizing environmental analysis tools to leverage their strengths. Finally, the process of linking simulation results to design interventions is discussed.

2. Overview of Microclimate Analysis Tools Climate is an important environmental factor that architects and engineers should consider when designing a building or a community. While the majority of microclimate analysis software remains proprietary and is not integrated into mainstream design software, early efforts are underway towards the integration of new tools with design in data-driven and computational design spaces. Open source platforms such as Grasshopper and Dynamo (and their various plugins) have emerged as an interface between the architecture engineering construction (AEC) products Rhino and Revit, respectively. These new tools have made experimentation and design with modern biometeorological parameters more accessible. In fact, although granting the parent software to the new plugins still requires a commercial license to operate (so it is not always possible), these tools provide the benefit of conducting microclimate modelling at a fraction of the cost of heavier and research-oriented software such as ENVI-met. In the conception of urbanism and architecture, a better understanding of climate data and human thermal comfort indices (HTCIs) would impact the process of designing for resiliency and energy efficiency. Architectural firms have hence started to deploy new tools and workflows as a means to increase the level of understanding in staff and design processes. This allows teams to base their formal and material decisions in response to real-time problems in community design, connecting the conceptual design to the post-construction comfort outcome. The workflows aim to connect design space parameters such as building height, angle, and orientation to phenomena such as wind velocity and direction to human comfort in the resulting physical space. It can also return information about material selections and their impacts on these less tangible metrics [8]. There is an increased capacity for designers to predict climatic measurement, anticipating what was not perceivable before, in a digital design space of floors, walls, and roofs. The feedback loop also works in both directions; not only is the integration of microclimate analysis affecting the way architects think about design, but it is also affecting how they operate, project, and simulate. A range of software and plugins are available for modelling microclimate variables (Table1). Commonly used tools include Ladybug, ENVI-met, SimScale, and Eddy3D [9], whose outputs are presented in Figure1. A brief description of these software follows. Ladybug is an open-source environmental analysis plugin for Grasshopper, the visual scripting interface for Rhino [10]. Ladybug includes several components for weather data visualization and solar radiation simulations. The latter functionality is powered by the Radiance program, one of the most widely adopted and appreciated tool for lighting modelling, which was developed at the Lawrence Berkeley National Laboratory (LBNL) [11]. By merit of it being a plugin, the information produced by Ladybug can directly inform the 3D geometry of the design. ENVI-met is a holistic, computational fluid dynamics (CFD) microclimate simulation software for modeling surface–plant–air interactions in urban environments. Among the current list of tools, it is Climate 2020, 8, 72 3 of 13

Climate 2020, 8, x FOR PEER REVIEW 3 of 13 the only one capable of simulating the wind flow around buildings; heat transfer at building surfaces; evapotranspiration;surfaces; evapotranspiration and the; reflection,and the reflection, transmission, transmission, absorption, absorption, and emission and ofemission solar radiation of solar altogether.radiation altogether. Wind flow Wind is modelled flow is modelledusing Reynolds using Averaged Reynolds Navier–StokesAveraged Navier (RANS)–Stokes equations, (RANS) andequations, radiative and fluxes radiative are resolved fluxes are using resolved an Indexed using Viewan Indexed Sphere View (IVS) Spher scheme.e (IVS) The scheme. spatial resolution The spatial of simulationsresolution of in simulations ENVI-met typicallyin ENVI- rangesmet typically from 1 ranges m to 5 m,from and 1m the to temporal5m, and the resolution temporal of theresolution output isof bythe defaultoutput hourly,is by default although hourly turbulence, although and turbulence radiative and fluxes radiative are updated fluxes are at theupdated scale at of the seconds. scale Theseof seconds. capabilities These havecapabiliti beenes tested have andbeen validated tested and in validated the literature in the [12 literature,13], making [12, ENVI-met13], making a widelyENVI- selectedmet a widely software, selected especially software, within the especially research within community. the research A trade-o communityff of its comprehensiveness. A trade-off of is the its ensuingcomprehensiveness compute times—in is the ensuing fact, a whole-daycompute times simulation—in fact, can a easilywhole take-day 24simulation hours or can more easily to complete take 24 evenhours on or a more high-performance to complete even computer. on a high Two-performance Grasshopper computer. plugins, lb_envimet Two Grasshopper and df_envimet, plugins, enhancelb_envimet the and feasibility df_envimet, of ENVI-met enhance the as feasibility a complement of ENVI to- themet architectural as a complement design to the process architectural [14,15], butdesign their process diffusion [14, is15] still, but limited. their diffusion is still limited. SimScale is a cloud cloud-based-based CFD platform capable of rendering annual wind comfort and transient wind flowflow around buildings. buildings. While While it it can can load geometry from CAD programs, results cannot be projected backback intointo them,them, sosothis this software software also also remains remains limiting. limiting Among. Among the the advantages advantages of of SimScale SimScale is theis the fact fact that that the the computing computing times times for for annual annual simulations simulations are are fast fast enough enough toto permitpermit multiplemultiple studies in a day. This allows thethe identificationidentification of several critical conditions over the year to allow a deeper understanding of the implication of multiple design options over longer periodsperiods ofof time.time. Eddy3D is anan airflowairflow andand microclimate microclimate simulation simulation plugin plugin for for Grasshopper Grasshopper [16 [16]]. It. isIt poweredis powered by theby the OpenFOAM OpenFOAM solver solver and and uses uses EPW EPW weather weather files files to predictto predict annual annual wind wind comfort. comfort. The The analysis analysis is acceleratedis accelerated by by “binning” “binning” the the EPW EPW wind wind directions directions into into larger larger sectors sectors and interpolatingand interpolating the local the windlocal velocitieswind velocities based based on wind on wind reduction reduction factors. factors The.results The results are highly are highly customizable customizable in Grasshopper in Grasshopper and Rhino,and Rhino, enabling enabling fine controlfine control over over visualizations. visualizations.

Figure 1. Screenshots of the outputs of environmental modelling tools: (a) ENVI-met, (b) Eddy3D, (Figurec) Ladybug, 1. Screenshots and (d) SimScale. of the outputs of environmental modelling tools: a) ENVI-met, b) Eddy3D, c) Ladybug, and d) SimScale.

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Table 1. Summary of the software discussed in this paper and their capabilities.

Software Capabilities Example Outputs Ladybug v0.0.68 Weather data visualization, sun paths, Hours of direct sunlight, (www.ladybug.tools) solar irradiance simulations solar irradiance Computational fluid dynamics (CFD) ENVI-met v4.4.3 Air temperature, wind velocity, microclimate simulations, outdoor (www.envi-met.com) humidity, mean radiant temperature thermal comfort quantification SimScale CFD wind flow simulations, wind Wind velocity, Davenport wind (www.simscale.com) comfort simulations comfort criteria Wind velocity, Universal Thermal Eddy3D v0.3.6.3 CFD wind flow simulations Climate Index (UTCI) pressure (www.eddy3d.com) coefficients on building facades

3. Methods for the Microclimate Analysis of Architecture Projects

3.1. Boundary Conditions The meteorological boundary conditions of a simulation refer to the set of physical variables used to initialize or force the numerical model. For example, a list of air temperatures might describe the hourly condition at the inflow boundary of a microclimate simulation. In a research paper, it is common to test an intervention against a single meteorological boundary condition, such as a hot summer day [17,18]. In contrast to scientific studies, in design applications it is critical that the boundary conditions used for architectural microclimate analyses are representative of the project location throughout the year, and all the conditions a site may experience in a year should be accounted for and presented to the client. This means that testing the multiple diurnal temperature and humidity cycles, wind speeds, wind directions, and cloud conditions which are typical for the location being studied becomes fundamental, while the resources (both temporal and economical) for slow simulations are rarely available. Common choices for validated typical weather data are Typical Meteorological Year (TMY) files, which are produced by several government agencies and made available in the universal Energy Plus Weather File (EPW) format. These hourly, year-long datasets are made by concatenating the individual months of weather station observations chosen for their representativity. While TMY files can be used unabridged in building energy models, the computational intensity of CFD microclimate models usually prohibits year-long simulations. Thus, it is necessary to parse these files for shorter yet still representative periods. Moreover, CFD simulation require user skills and the use of adequate mesh, numerical schemes, turbulence models, and many other details to provide solid information and allow the critical interpretations of the results. A tool that helps streamline this process is the Read EPW component of Ladybug, which can be used to scrub through the lines in an EPW file while dynamically plotting the relevant meteorological variables. Through this approach, typical fall, winter, spring, and summer days can be quickly distinguished from anomalies. More importantly, the selected hourly data for which to run a simulation can be written directly to an ENVI-met configuration file through df_envimet. This makes it possible to queue up dozens of combinations of boundary conditions in an efficient semi-supervised fashion way. For example, using these components, Figure2 shows a Grasshopper script, which generates an ENVI-met configuration file for eight wind directions for each season in the location of interest. Further parameters that can be manipulated include the assumed roughness length of the site, which is used to construct the vertical wind profile at the model inflow boundary according to the EPW wind velocity measured at a 10 m height. The script is quicker than negotiating the default ENVI-met user interface, thereby encouraging the consideration of all possible boundary conditions. ClimateClimate2020 20,208,, 728, x FOR PEER REVIEW 5 of5 13 of 13

Figure 2. A Grasshopper script which uses Ladybug and df_envimet components to efficiently set up meteorological boundary conditions for ENVI-met. Figure 2. A Grasshopper script which uses Ladybug and df_envimet components to efficiently set up 3.2. Extractingmeteorological Results boundary conditions for ENVI-met. Complementary to the need to set up simulations rapidly is the ability to handle their results 3.2. Extracting Results efficiently. When several iterations of a model are being compared, it becomes extremely useful in daily consultingComplementary practice to to the have need an to automated set up simulations means of rapidly parsing is the the results. ability to For handle this, ittheir is possible results to benefitefficiently from. theWhen use several of NetCDF, iterations Python, of a andmodel Jupyter are being Notebook. compared, it becomes extremely useful in dailyNetCDF consulting (Network practice Commonto have an Dataautomated Form) means is a of self-describing parsing the results. data For format this, it is developed possible to by UCARbenefit/Unidata, from the which use of is NetCDF, used for accessingPython, and and Jupyter storing Notebook. array-orientated scientific data. It is one of the NetCDF (Network Common Data Form) is a self-describing data format developed by output formats supported by ENVI-met and has a Python programming interface. Using this module, UCAR/Unidata, which is used for accessing and storing array-orientated scientific data. It is one of it is possible to load the hourly 4D (x, y, z, meteorological variable) results from ENVI-met into a 3D the output formats supported by ENVI-met and has a Python programming interface. Using this NumPy array. From this array, locations of interest can be extracted (i.e., vertical slice at 2 m height) module, it is possible to load the hourly 4D (x, y, z, meteorological variable) results from ENVI-met and a range of specialized graphs and statistics can be produced with a few lines of code. into a 3D NumPy array. From this array, locations of interest can be extracted (i.e., vertical slice at 2 For example, the location of the maximum simulated air temperature can be reported for each m height) and a range of specialized graphs and statistics can be produced with a few lines of code. scenarioFor (Figure example,3), or the a time location series of plot the ofmaximum air temperature simulated at theair sametemperature location can across be reported multiple for scenarios each canscenario be produced (Figure (Figure 3), or 4a ). time Furthermore, series plot ofif additional air temperature scenarios at the are same simulated, location minimal across multiple e ffort is requiredscenarios to incorporatecan be produced their (Fig resultsure 4 into). Further existingmore graphs, if additional and statistics. scenarios This are encourages simulated, aminimal recursive approacheffort is torequired microclimate to incorporate simulations. their results into existing graphs and statistics. This encourages a recursive approach to microclimate simulations.

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FigureFigure 3. A 3. JupyterA Jupyter Notebook Notebook for for identifying identifying the thelocation location of the ofminimum the minimum air temperature air temperature in an ENVI- in an ENVI-metmet simulation simulation (white (white crosshairs). crosshairs).

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FigureFigure 4. 4.(a a)) Time Time seriesseries plotsplots of Physiological Equivalent Equivalent Temperature Temperature (PET) (PET) at atthree three locations locations within within fourfour runsFigure runs of 4.of an a an) ENVI-met Time ENVI series-met model. plots model. of ThePhysiological The dark dark green green Equivalent band band indicates indicates Temperature the the range range(PET) of atof nothree no thermal thermal locations stress, stress, within and and the four runs of an ENVI-met model. The dark green band indicates the range of no thermal stress, and lightthe green light green band band indicates indicates the region the region of slight of slight heat heat/cold/cold stress. stress (b.) b Heat) Heat map map of of the the summersummer scenario at the light green band indicates the region of slight heat/cold stress. b) Heat map of the summer scenario 9 am.at 9 am. at 9 am. AnotherAnother beneﬁt benefit of of using using PythonPython toto parseparse ENVI ENVI-met-met results results is is that that plots plots can can be be created created for for a a Another benefit of using Python to parse ENVI-met results is that plots can be created for a spatially averaged subset of the model domain. In contrast, the software packaged with ENVI-met spatiallyspatially averaged averaged subset subset of of the the model model domaindomain.. InIn contrast, thethe softwaresoftware packaged packaged with with ENVI ENVI-met-met onlyonly allowsonly allows allows a singlea single a single point point point to to to be be be plotted plotted plotted atat at aa a time.time. Point Point observations observations can can can be be beproblematic problematic problematic when when when looking looking looking at variablesat variablesat variables incorporating incorporating incorporating radiation radiation radiation (i.e.,(i.e. (i.e., ,meanmean mean radiantradiant temperature), temperature), which which which vary vary vary considerably considerably considerably across across across shortshortshort distances distances distances due due due to to theto the the shading shading shading ofof of directdirect direct solar solarsolar radiationradiation (Fig(Fig (Figureureure 5 5).).). By By By taking taking taking the the the spatial spatial spatial average average average ofof these theseof these variations, variations, variations, the the the misrepresentation misrepresentation misrepresentation ofof anan outdoor outdoor space space’s’s’s hourly hourly hourly performance performance performance can can be can be avoided. beavoided. avoided. Moreover,Moreover,Moreover, spatial spatial spatial averages averages averages partially partially partially acknowledgeacknowledge acknowledge that a a person person may may may change change change their their their position position position within within within an an an outdooroutdooroutdoor amenity amenity amenity to to ﬁnd to find find better better better (or (or (or worse) worse) worse) thermalthermal thermal comfort.comfort.

FigureFigure 5. (a 5.) Hourlya) Hourly PET PET as as reported reported byby aa singlesingle point point in in an an ENVI ENVI-met-met simulation simulation and andas a asspatial a spatial Figureaverage 5. a of) Hourly a region PET centered as reported on that point.by a single The shaded point bluein an area ENVI indicates-met simulation the standard and deviation as a spatial of average of a region centered on that point. The shaded blue area indicates the standard deviation of averagepoints of within a region that centered region. b on) PET that heat point.-map The of theshaded same blue simulation area indicates at 9 am. the The standard black dot deviation indicates of points within that region. (b) PET heat-map of the same simulation at 9 am. The black dot indicates the pointsthe singlewithin point that selected. region. bThe) PET spatial heat average-map of was the derived same simulation from the whole at 9 am.courtyard. The black dot indicates singlethe single point point selected. selected. The spatialThe spatial average average was was derived derived from from the the whole whole courtyard. courtyard. 3.3. Human Thermal Comfort Indices 3.3. Human Thermal Comfort Indices 3.3. Human Thermal Comfort Indices While the meteorological outputs of a microclimate simulation are important for understanding the underlying physical processes, other metrics exist to provide more human-centric assessments of outdoor thermal comfort. HTCIs relate a given of air temperature, humidity, mean radiant temperature Climate 2020, 8, x FOR PEER REVIEW 8 of 13

While the meteorological outputs of a microclimate simulation are important for understanding Climate 2020, 8, 72 8 of 13 the underlying physical processes, other metrics exist to provide more human-centric assessments of outdoor thermal comfort. HTCIs relate a given of air temperature, humidity, mean radiant (MRT),temperature and wind (MRT) speed, and to wind a human speed physiological to a human response, physiological which response is subsequently, which is subsequently transposed to a singletransposed perceived to a temperature.single perceived HTCIs temperature. also consider HTCIs clothing also consider insulation clothing and the insulation metabolic and rate the of metabolic rate of an individual. Two commonly used HTCIs are the Universal Thermal Climate Index an individual. Two commonly used HTCIs are the Universal Thermal Climate Index (UTCI) and (UTCI) and Physiological Equivalent Temperature (PET) [19,20]. UTCI is well suited to high-level Physiological Equivalent Temperature (PET) [19,20]. UTCI is well suited to high-level assessments assessments because it assumes a fixed metabolic rate (walking, 4 km/h) and automatically adjusts because it assumes a fixed metabolic rate (walking, 4 km/h) and automatically adjusts the thermal the thermal resistance of clothing based on the provided meteorological conditions. A limitation of resistance of clothing based on the provided meteorological conditions. A limitation of UTCI is that it UTCI is that it does not accept wind speeds slower than 0.5 m/s, which occasionally results in null does not accept wind speeds slower than 0.5 m/s, which occasionally results in null values around values around buildings. PET does not suffer from this limitation, making it preferable for buildings.microclimate PET doessimulations. not suff Morer fromeover, this PET limitation, allows for making the customization it preferable of for an microclimate individual’s simulations.metabolic Moreover,rate and PETclothing, allows thereby for the encouraging customization analyses of an that individual’s are tailored metabolic to the intended rate and use clothing, of space. thereby The encouragingformulas for analyses PET and that UTCI are have tailored been to translated the intended to Python use of in space. the source The formulas code for forLadybug PET and. These UTCI havescripts been can translated be used toindependently Python in the of source microclimate code for simulations Ladybug. These to gain scripts a sense can of be the used consequences independently of of microclimatean architectural simulations intervention togain quickly a sense. For of example, the consequences Figure 6 shows of an architecturala graphical way intervention to answer quickly. the Forquestion example, “what Figure would6 shows the UTCI a graphical be if this way canopy to answer is expected the question to lower “what the MRT would by 17 the °C?” UTCI. The be figure if this canopyhelpsis with expected understanding to lower thethe MRTeffects by of 17 the◦C?”. assumed The figure interventions helps with through understandingout the year, the provides effects of a the assumedgraphical interventions indication throughoutof how the theUTCI year, evolves provides, and a, graphicalfinally, shows indication how the ofhow annual the UTCIcomfortable evolves, and,duration finally, is shows increased how by the 5%. annual comfortable duration is increased by 5%.

Figure 6. Annual UTCI heat maps for Baltimore. The vertical dimension indicates the time of day and Figure 6. Annual UTCI heat maps for Baltimore. The vertical dimension indicates the time of day and the horizontal dimension indicates time of year. (a) is derived from the baseline weather station data the horizontal dimension indicates time of year. a) is derived from the baseline weather station data and (b) assumes a 17 C reduction in mean radiant temperature (MRT) and 0.5 m/s increase in wind and b) assumes a 17◦ °C reduction in mean radiant temperature (MRT) and 0.5 m/s increase in wind speedspeed during during the thesummer summer only. only. With With these these interventions, interventions, the annual the annual comfortable comfortable duration duration is increased is byincreased 5%. by 5%.

ToTo deﬁnitively definitively assess assess a a design design in in terms terms ofof microclimate,microclimate, it it is is important important to to consider consider the the thermal thermal comfortcomfort of of space space for for the the entire entire period period of of use use asas opposedopposed to just just a a point point in in time. time. This This is ismet met by bydynamic, dynamic, time-integratedtime-integrated metrics. metrics. For For example, example, inin climate-basedclimate-based daylight daylight modelling, modelling, daylight daylight autonomy autonomy (DA) (DA) is ais metrica metric used used to to describe describe the the percentage percentage ofof thethe occupiedoccupied times times of of the the year year when when the the minimum minimum illuminanceilluminance requirement requirement is is met met by by daylight daylight alone alone [[21]21].. By By the the same same token, token, Outdoor Outdoor Thermal Thermal Comfort Comfort AutonomyAutonomy (OTCA) (OTCA) is i as a metric metric used used to todescribe describe thethe percentage of of occupied occupied times times of of the the year year during during whichwhich a designateda designated area area meets meets a a set set of of thermal thermal comfortcomfort acceptability criteria criteria [22] [22,], as as per per the the formula: formula:

1 1 푁 ℎ푓 푂푇퐶퐴 = ∑ ∑ 푇퐶 1 1, XN Xh f (1) 푁 푛 푘=1 ℎ푟=ℎ푖 푘,ℎ푟 OTCA = TCk,hr, (1) N n k=1 hr=hi where 푁 is the number of occupied days, 푛 is the number of occupied hours, ℎ𝑖 is the initial whereoccupiedN is the hour, number ℎ푓 is of the occupied final occupied days, n is hour the number, and 푇퐶 of푘,ℎ occupied푟 is the hours,result ofhi is a the thermal initial comfort occupied hour,acceptabilityh f is the ﬁnal criterion. occupied The criterion hour, and itselfTC kis,hr definedis the result by: of a thermal comfort acceptability criterion. The criterion itself is deﬁned by: ( ) 1 i f A < HTCI < A TC = lower upper , (2) k,hr else 0 Climate 2020, 8, x FOR PEER REVIEW 9 of 13

Climate 2020, 8, 72 9 of 13 1 𝑖푓 퐴 < 퐻푇퐶퐼 < 퐴 푇퐶 = { 푙표푤푒푟 푢푝푝푒푟 }, (2) 푘,ℎ푟 푒푙푠푒 0 where HTCI is the HTCI value at a given hour, Alower is the lowest value of HTCI that is considered where 퐻푇퐶퐼 is the HTCI value at a given hour, 퐴푙표푤푒푟 is the lowest value of HTCI that is considered comfortable, and Aupper is the highest value of HTCI that is considered comfortable. These upper and comfortable, and 퐴푢푝푝푒푟 is the highest value of HTCI that is considered comfortable. These upper lowerand lower bounds bounds can be can defined be defined by PET by or UTCI PET or assessment UTCI assessment scales. For scales. example, For an example, OTCA calculation an OTCA basedcalculation on PET based would on PET count would the duration count the for duration which a for space which is between a space 18is between and 23 ◦C 18 PET and (equivalent 23 °C PET to(equivalent no thermal to stress). no thermal Alternatively, stress). lessAlternatively, stringent criteria less stringent can be used criteria for outdoor can be usedcirculation for outdoor spaces wherecirculation the duration spaces where of exposure the duration is shorter. of exposure is shorter. OTCA is not currently a feature offered offered by ENVI-met,ENVI-met, so the authors created a Python script that post-processespost-processes itsits resultsresults viavia the NetCDF library forfor Python [[23]23].. In addition to producing OTCA heat maps, the script is also capable of reporting Spatial Outdoor Thermal Comfort Autonomy (sOTCA), (sOTCA), which describes the percentage of a designated area which meets the thermal comfort acceptability criteria forfor atat leastleast 50%50% of of the the occupied occupied period period [22 [22]]. Used. Used in in combination, combination, OTCA OTCA and and sOTCA sOTCA form form the basisthe basis for thefor the rapid rapid and and comprehensive comprehensive assessment assessment of outdoorof outdoor space space (Figure (Figure7). 7 Further,). Further, they they o ffofferer a simplifieda simplified means means to trackto track the incremental the incremental improvement improvement of design. of design. A limitation A limitation of the current of the approach current toapproach calculating to calculating OTCA is that OTCA representative is that representative seasonal days seasonal are used days in place are used of an in actual place year’sof an worthactual ofyear simulations.’s worth of Recent simulations. studies R haveecent suggested studies havethat the suggested results of that annual the daylight results of simulations annual daylight can be adequatelysimulations capturedcan be adequately by as few captured as three representative by as few as three conditions representative [24], and conditions likely the same [24], conceptand likely of principalthe same concept components of principal can beextended components to microclimate can be extended analysis. to microclimate analysis.

Figure 7. Outdoor Thermal Comfort Autonomy (OTCA) and Spatial Outdoor Thermal Comfort AutonomyFigure 7. Outdoor (sOTCA) Thermal for a project Comfort in Toronto Autonomy on typical (OTCA) summer and and Spatial spring Outdoor days from Thermal 6 am Comfort to 8 pm. NoticeAutonomy how (sOTCA) the shadows for a castproject by treesin Toronto contribute on typical to comfort summer during and thespring summer days butfrom reduce 6 am to comfort 8 pm. duringNotice how the spring. the shadows cast by trees contribute to comfort during the summer but reduce comfort during the spring. 3.4. Synergistic Applications of Tools 3.4. SynergisticExtensions Applications of analysis of tools Tools into the architectural design space have been made possible through open-sourceExtensions platforms of analysis like Grasshoppertools into the andarchitectural Dynamo, design which continuesspace have to been expand made as possible the tools through become moreopen- widelysource implementedplatforms like in Grasshopper practice. These and platforms Dynamo, add which a tertiary continues informational to expand layer as into the more tools traditionalbecome more CAD widely and implemented BIM software. in Still,practice computers. These platforms are not good add ata tertiary open-ended informational creative layer solutions into thatmore are tradi stilltional reserved CAD to and the operator. BIM software. However, Still, an computers increase in are interest not good has at emerged open-ended to expand creative the possibilitysolutions that of automation are still reserved to save to time the doing operator repetitive. However, tasks, an such increase as iterating in interest design has options emerged with to slightexpand formal the possibility or material of changes automation through to save the same time analysis. doing repetitive For increasing tasks, thesuch level as ofiterating automation design in climateoptions modelling,with slight formal tools for or generative material changes design through such as Galapagosthe same analysis. and Octopus, For increasing both of whichthe level plug of intoautomation Grasshopper in climate and modelling Dynamo,, are tools being for generative added to the design designers’ such as toolset Galapagos [25]. and Generative Octopus, design both softwareof which allows plug forinto targets Grasshopper to be set an basedd Dynamo, on the performance are being added of a model to the as changes designers' are toolset made to [25] the. input parameters. A simple example of this would be the increased area on a wall being observed as

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GenerativeClimateClimate2020 20,208 , design, 728, x FOR softwarePEER REVIEW allows for targets to be set based on the performance of a model10 of10 13 of as 13 changes are made to the input parameters. A simple example of this would be the increased area on a wallGenerative being observed design software as the length allows of for it targetsgrows . toIn beterms set basedof optimization on the performance for microclimate of a model analysis, as onethechanges lengthcould ofareimagine it made grows. severalto the Interms input parameters ofparameters. optimization (the A height, simple for microclimate width,example and of thisdepth analysis, would of onean be opening,the could increased imagine for areaexample) several on effectingparametersa wall thebeing (theobserved observed height, solar as width, the radiation length and depth ofon it that grows of same an. opening,In wall. terms This of for optimization target example) value e forffisecting referred microclimate the to observed as theanalysis, fitness, solar whileradiationone the could oncontrol thatimagine sameparameters several wall. This parametersare the target genomes. value (the height, is A referred generative width, to as andsolver the depth fitness, combine of while ans opening,countless the control for combinations example) parameters ofare valueseffecting the genomes. for the the observed genomes A generative solar, output radiation solvers a result on combines that that same is countlesscomparable wall. This combinations target to the value baseline is of referred valuesfitness, tofor and as thethe refine genomes,fitness,s it as itoutputs solveswhile thefor a result controlsuccessive that parameters is generations comparable are the in to genomes.an the attempt baseline A togenerative either fitness, maximize solver and refines combine or minimize its as countless it solves the targetcombinations for successive; Figure 8 showsgenerationsof values the genomes for in anthe attempt genomes and fitness to, output either processs maximize a result in Galapagos that or minimizeis comparable. the target;to the baseline Figure8 fitness,shows and the genomesrefines it as and fitnessit solves process for successive in Galapagos. generations in an attempt to either maximize or minimize the target; Figure 8 shows the genomes and fitness process in Galapagos.

Figure 8. Visualization of the genomes and fitness in Galapagos. Control parameters were the angle Figure 8. Visualization of the genomes and fitness in Galapagos. Control parameters were the angle andFigure dimensions 8. Visualization of three ofwalls, the genomeswhile the and fitness fitness was in determinedGalapagos. Control by the cumulativeparameters weresolar the irradiance angle and dimensions of three walls, while the fitness was determined by the cumulative solar irradiance on on andthe threedimensions walls. ofIn threethe center walls, graphic, while the each fitness polyline was determined corresponds by to the a singlecumulative combina solartion irradiance. theon three the three walls. walls. In the In centerthe center graphic, graphic, each each polyline polyline corresponds corresponds to to a singlea single combination. combination. Another automated possibility for design generation is showed below. Here, the authors tried Another automated possibility for design generation is showed below. Here, the authors tried to to reconcileAnother a maximizedautomated possibility solar radiation for design on generation an upper- levelis showed balcony below. with Here the, the dimensions authors tried and reconcile a maximized solar radiation on an upper-level balcony with the dimensions and orientation orientationto reconcile of that a maximized terrace in solarorder radiationto extend onthe an OTCA upper into-level the balcony shoulder with seasons. the dimensions The goal was and to of that terrace in order to extend the OTCA into the shoulder seasons. The goal was to maintain maintainorientation roughly of that the terrace same location in order and to extend size for the the OTCA balcony into, while the shoulder allowing seasons. to the wallsThe goal to move was toand roughlymaintain the roughly same location the same and location size forand the size balcony, for the balcony while allowing, while allow to theing to walls the walls to move to move and and rotate rotate slightly. The top-performing options in this study increased the solar radiation by more than slightly.rotate slightly. The top-performing The top-performing options options in this studyin this increasedstudy increased the solar the radiationsolar radiation by more by more than than 50% in 50% in the shoulder season when compared to a baseline conceptual design (Figure 9). The final step the50% shoulder in the seasonshoulder when season compared when compared to a baseline to a baseline conceptual conceptual design (Figuredesign 9(Fig). Theure final9). The step final involved step involved a visual check with the designers to determine which forms would satisfy the plan a visualinvolved check a visual with the check designers with the to determine designers which to determine forms would which satisfy forms the would plan satisfy requirements. the plan requirements. requirements.

FigureFigure 9. 9.Optimization Optimization of of a a terrace terrace inin termsterms of maximum solar solar exposure exposure during during a atypical typical shoulder shoulder Figure 9. Optimization of a terrace in terms of maximum solar exposure during a typical shoulder seasonseason day. day. season day.

Climate 2020, 8, 72 11 of 13

While the presence of a dynamic and automated design process increases, it is largely misconceived that these tools eliminate or replace the designer in the creativity process. In fact, the selection of which tool, metric, and support for architecture creativity is a consideration that is directly tied to design today. The capacity to rapidly generate and prototype architectural form in 3D design spaces enables architects to be both pragmatic and precise in their processes. This is achieved by a link that is established between formal decisions and human-centric evaluations based on analysis rather than mere speculation or automatic processes. It should be acknowledged that with the increased capacity to synthesize analysis software with automation, there are emerging questions around the validity and substantiating of claims made by design teams engaging in climate-sensitive design. The ability for designers to cherry-pick biometeorological data that supports their research and omit others that do not can lead to a distortion of data in the interest of a convincing architectural narrative. Currently, there is no regulatory oversight that would ensure practices in microclimate analysis integration in design processes are authentic or are being done responsibly.

3.5. Design Interventions The efficient and synergistic use of simulation tools is only part of the effort required to incorporate microclimate as a design driver of architecture. Equally relevant to this discussion is the process of linking simulation results to design interventions. This occurs in three steps: recognizing the type of thermal discomfort, zeroing in on the environmental parameters causing that discomfort, and designing interventions that manipulate those environmental parameters. For example, if a simulation reveals a prevalence of cold stress in an outdoor patio during a mostly comfortable season, possible culprits are the wind (which controls the rate of convective cooling on individuals’ skin) or the MRT (which determines the radiative heat balance between an individual and the environment). With this knowledge, an architect may choose to add wind-blocking landscape features while rearranging the building massing to allow more direct sunlight onto the patio. Alternatively, this analysis may reveal a naturally more comfortable location for the outdoor amenity. A summary of design interventions and the simulation results that may warrant them is provided in Table2. Most of the interventions listed directly influence MRT and wind speed, which have a significant impact on thermal comfort. While more strategies are currently under investigation in scientific literature, some are less efficient or practical at the scale of an architectural project. For example, water ponds must be large to create a noticeable cooling effect [26]. Green roofs, while having energy and drainage benefits, tend to have minimal impacts on the temperature at ground level [27,28].

Table 2. Simulation results and corresponding design interventions.

Environmental Simulation Result Desired Change Design Interventions Parameter Trees shading canopies/pergolas MRT decrease refine building massing/orientation Heat stress to increase shade refine building massing/orientation wind speed increase to channel prevailing winds evaporative mist-spraying systems; air temperature decrease vegetation cool materials Refine building massing/orientation Cold stress MRT increase to increase sun exposure

Recent work by designers suggests that novel opportunities for microclimate enhancement exist in the form of dynamic devices that adapt to changing climate and occupancy conditions. In Toronto, Climate 2020, 8, 72 12 of 13 a team of architects, climate engineers, and urban planners studied the effectiveness of “weather mitigation systems” consisting of various Ethylene tetrafluoroethylene (ETFE) structures designed to retract, collapse, and relocate to provide varying levels of shade and wind dampening depending on the season [29]. In London, the Elytra Filament Pavilion attempted to relate people flow to microclimate conditions using real-time measurements of movement frequency and UTCI at an array of positions beneath a robotically constructed canopy [30]. This experiment foretells of data-driven architecture which continuously refines itself to provide the greatest number of people with the highest level of thermal satisfaction. These examples showcase new opportunity for spatio-temporal variation in microclimates that is controlled using simulations with a precision that simple canopies, trees, and windbreaks are unable to match.

4. Conclusions This paper discussed the challenges and experiences while implementing microclimate analysis tools in architectural design practice. The study revealed and made evident that the existing toolset—in particular, ENVI-met—requires significant adaptation when used for such purposes. A handful of plugins and open-source code could assist with this process and was presented by the authors. The primary benefits of these add-ons are improved software interoperability, the rapid development and analysis of simulations, and the ability to calculate new and emerging metrics such as OTCA. Nonetheless, there is progress to be made before microclimate can become a thoroughly integrated design driver of architecture. It should be noted that the microclimate analysis activities described in this paper are not intended to replace services obtained for safety and compliance purposes, such as wind tunnel testing. Rather, the study proves the feasibility of unlocking microclimate as a basis for site-specific and climate-driven design and as a means to improve the year-round experience offered by outdoor amenities.

Author Contributions: J.G. and R.M. carried out the analysis of the case studies and drafted the manuscript. U.B. and G.T. supervised the work, reviewed, and edited the manuscript. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by Mitacs grant number IT4857. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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