daylighting based parametric design exploration of 3d...

Daylighting Based Parametric Design Exploration of 3DFacade Patterns

Amartuvshin Narangerel1, Ji-Hyun Lee2, Rudi Stouffs31,2KAIST 3National University of Singapore1,2{amartuvshin|jihyunlee}@kaist.ac.kr [email protected]

A building façade plays an important role of reducing artificial lighting byintroducing natural light into the interior space. A majority of research andcurrent technology heavily focuses on the optimization of window properties suchas the size, location, and glazing with the consideration of external shadingdevice as well as the building wall in order to obtain appropriate natural litspace. In the present work, we propose a 3-dimensional approach that canexplore the trade-offs between two objectives, daylight performance andelectricity generation, by means of paramedic modeling and multi-objectiveoptimization algorithm. The case study was simulated under the environmentalsetting of the geographical location of Incheon, Korea without any urban context.Using the proposed methods, 50 pareto-front optimal solutions were derived andinvestigated based on the achieved daylighting and generated electricity.

Keywords: Parametric design, façade design, daylight performance,building-integrated photovoltaics, multi-objective optimization

INTRODUCTIONDaylight is considered the best source of light thatmost closely matches human needs (Li and Tsang2008). Building fenestration is responsible for intro-ducing daylight into the indoor space, and when afaçade is designed properly, it can reduce the needfor artificial light significantly (Nabil and Mardaljevic2005; Krarti et al. 2005). Commonly, the façade ofa high-rise office building is considered as a verti-cally extruded glass envelope that consists of a num-ber of transparent and opaque glazing layers. Inaddition, shading elements may be attached in or-der to protect the indoor from direct solar radia-tion for improved indoor comfort. Furthermore, Pho-tovoltaic (PV) panels or Building-Integrated Photo-voltaic (BIPV) can be added to the building roof,

façade, or both, to further improve the sustainabilityfactor. All these elements need to be considered si-multaneously within a sophisticated design methodto achieve better design in terms of indoor comfortand sustainability.

Not only size and location of the window andthe external shading device have a significant ef-fect on the level of daylight in a given space. Avast number of studies have been carried out by re-searchers, considering these aswell as additional fac-tors such as wall thickness, glazing properties andthe integration of external shading device (David etal. 2011; González and Fiorito 2015; Sheikh and Ger-ber 2011; Gadelhak 2013) as well as BIPV (Mandalakiet al. 2012). However, very few studies take in ac-count all these factors together, and almost no stud-

SHAPE, FORM AND GEOMETRY | Grammars and Concepts - Volume 2 - eCAADe 34 | 379

ies can be found of a 3D façade replacing the con-ventional building façade. Nevertheless, the interestof more complex shapes and patterns applied to thebuilding façade is growing significantly in contem-porary architecture due to technological and fabrica-tion advancements (Rahimzadehet al. 2013). In addi-tion, while buildings which do feature a complex 3Dfaçade have been erected in some urban areas, mostof these buildings are very experimental. Therefore, asystematic exploration of 3D façades and an investi-gation of the benefits of these emerging façade pat-terns are highly significant.

The aim of this paper is to suggest a methodol-ogy to generate an enclosed 3D façade unit, whichis near optimal in terms of daylighting. The gener-ated 3D facade unit consists of mainly three compo-nents: a transparentwindowallows sunlight topene-trate into the indoor space; an opaque wall functionsas a shading device, and BIPV harvests solar energy.To achieve this goal, 3D façade units are generatedin two phases: first a basic 2D shape is generatedand, next, it is expanded into a 3D façade unit. Sub-sequently, materials are applied for daylight simula-tion. By performing all assignments parametrically,an evaluation of daylight simulation can take placein order to suggest an optimal façade.

LITERATURE REVIEWA vast number of peer-reviewed studies could befound regarding the building façade. In this section,we have categorized them into three main parts asa generation, performance assessment, and the opti-mization.

Parametric façade designTechniques for generating building façades havebeen investigated by a number of authors. One ofthemost commonmethods is parametric modelling,which is highly effective to automate the generationof a large set of architectural design instances by thecombination of pre-defined design parameters (Tur-rin et al. 2011). In designing a building façade, de-signers and researchers are highly concerned by the

placement of a rectangular fenestration on a planarbuilding envelope. These particular facades yield arange of parameters such as the size, number, andthe distribution of the windows as well as the thick-ness and the material of the wall with external shad-ing devices (Hassaan et al. 2016; Echenagucia et al.2015). For example, Echenagucia et al. (2015) stud-ied the exterior wall of an open space office's plan-ner in an urban and non-urban context at four dif-ferent locations in Europe. In contrast to parametricmodelling, a new approach suggested by J. Wright etal. (2014) generates façade patterns by dividing thesurface into small equal rectangular cells and deter-mines the optimal number of windows and distribu-tion through multi-objective optimization based onenergy performance and capital cost.

Assessment of a building façade perfor-manceIn this study, we measured the amount of naturallight using the "Useful daylight luminance" (UDI) pre-dictive method. The UDI method is first coined byNabil andMardaljevic (2005), anddivides annual day-light illuminance at the workplace into three bins.The first bin includes areas that receive under 100 lux,which is not suitable and thusdemands additional ar-tificial lightning; the second bin corresponds to therange of 100 to 2000 lux, which is suitable for workactivity; the third bin includes illuminance that ex-ceeds 2000 lux and which results in potentially vi-sual discomfort (Nabil and Mardaljevic 2005). Thismethod is more realistic than the conventional "day-light factor approach" which only considers a singleovercast sky. When natural lighting cannot supply asufficient amount of light into the indoor space, ar-tificial lighting would be required in the space. Todecrease this electrical demand, building integratedphotovoltaic panels could be attached at the outerside of the façade for electricity harvesting. This prac-tice is one of the sustainable features in the buildingdomainwhich could potentially covermore than halfof the daily energy needs (Berkel et al. 2014). Man-dalaki et al. (2012) examine the thirteen most com-

380 | eCAADe 34 - SHAPE, FORM AND GEOMETRY | Grammars and Concepts - Volume 2

Figure 1Facade 3D unitgeneration andoptimizationsystem diagram.

monly used types of fixed shading devices as a PVpanel for an office building. Among them, the singleinclined canopy showed the most efficiency whencomparing the area of PV with the generated elec-tricity. Vartiainen et al. (2000) analyzed the optimalsize and orientation of a single rectangular fenestra-tion in a fully covered PV integrated building façadeunit. A very lowpercentageofwindowarea, ten tofif-teen percent of the whole façade, proved to be idealwhen considering energy harvesting through PV andthe replacement of artificial lighting by daylight inthe specific location of Europe.

FaçadeOptimizationIn order to achieve a better performance with re-spect to daylighting, the building façade needs tobe optimized. There is a large pool of variablesthat controls the design and the overall performanceof the building façade, which could be effectivelycontrolled parametrically to yield a number of alter-

natives for performance assessments. The optimalbuilding façade design can be achieved effectivelyby means of building performance simulation cou-pledwith an evolutionary algorithm tool (Turrin et al.2012; Evins et al. 2011) (Figure 1). Especially, the tech-nique of multi-objective optimization is highly prac-tical (Wang et al. 2005) in that it provides visual infor-mation of the trade-offs between contrasting designobjectives (Mela et al. 2012). Several studies havefocused on the window-to-wall ratio (WWR) and en-ergy performance (Goia et al. 2013; Echenagucia etal. 2015); while other researchers optimize windowsize and external shading types by the means of ge-netic algorithms (Torres and Sakamoto 2007). Yi andMalkawi (2009) investigated theoverall building formcontrolled by a hierarchical relation of geometry andform optimization and the method was able to finda particularly complex shape rather than the simpleboxy one.


PROPOSED APPROACHThe proposed approach section describes the sug-gested methodology specifically, interpreting 3Dfaçadegeometry generation that followedby the op-timization part in two sections.

Generation of the façade geometryThe façade generation process is divided into twoparts. Thefirst part divides the façade and the secondpart formalizes the unit. Theworkflowof second partis explicitly explained in Figure 1. The division of thebuilding façade is based on a 2D tessellation (Figure2, A and B); hereto, we only consider the equilateraltriangle, the square and the regular hexagon (Figure2). This restriction is inspired by the regular tessel-lations of the plane, though, obviously, other non-regular polygons can also be considered to cover theplane with a single element. In fact, the explorationcan be easily extended to other unit shapes by aug-menting the number of parameters considered.

Once the façade base surface is divided, extranodes are parametrically added to the façade unit(Figure 2 C). The location of these point(s) is limitedby the unit's perimeter, and lays either inside or onthe perimeter as defined by the edges and vertices.The number of the additional points and the locationof these points is to be determined by the designer,in order to give the designer more control over thebasic pattern of the façade. After placing the addi-tional points on the façade unit, the additional nodesare extruded into a direction perpendicular to the ini-tial façade plane. The extrusion length serves as oneof the parameters for the fitness function. The façadeunit's vertices and additional points are clustered andconnected to each other by means of a Delaunay tri-angulation (Figure 2D). TheDelaunay triangulation isa commonly used method in the computational de-sign domain to maximize all the angles in the gen-erated triangles. We have chosen this method to re-duce very thin and sharp fractured surfaces that arenot ideal for the fabrication and manufacturing pro-cess in the façade design.

Figure 2Facade geometrygeneration.


Subsequently, materials are applied to the spatialspace frame wires resulting from the Delaunay trian-gulation (Figure 2 F).

Three main material types are being considered:glazing, wall, and a totally opaque PV panel. For thedaylight simulation, these threematerials can be cat-egorized based on two general properties, whetheropaque (the PV panel and the wall) or transparent(the glazing). This binary surface option serves as an-otherparameter for theoptimizationfitness function.

Thismethod canbe applied to any type of geom-etry with the appropriate base 2D tessellation and asufficient number of additional vertices on each fa-cade unit. In the case of a conventional façade de-sign, with a traditional rectangular window withinthe façade unit, it is sufficient to consider four addi-tional points with a zero extrusion length.

Parametric modeling and Multi-objectiveoptimizationWe used the Grasshopper parametric modeling toolas a platform for the entire process, including bothdesign exploration and simulation.

Figure 3Testing room is inthe middle of a 3 x 3units. In thispicture, planarfaçade withrectangular unit issimulated.

The 3D façade units were made parametrically; theplug-ins Ladybug and Honeybee are adopted toperform the dayligting simulations using Radiance

(Roudsari et al. 2013).An adequate amount of daylighting requires an

appropriate window-to-wall ratio, while solar energyharvesting increases when the PV surface area ex-pands. These two characteristics are highly depen-dent on the direction of the façade and the loca-tion of the building. Furthermore, these two objec-tives contrastwith one another: whendesigners set agoal tomaximize the amount of electricity fromBIPV,it will affect the size of the window, consequentlydeteriorating the daylighting potential. Therefore,we employed evolutionary computation for multi-objective optimization, using the Octupus plug in.

Optimization strategy and Fitness functionThe objective function maximizes the area which iscorrespondent to the range of UDI100-2000 in thegiven space,while alsomaximizing the amountof an-nual energy which obtained by BIPV on the façadeunit.

The evolutionary algorithm inputs are classifiedinto twomain categories. The first category of inputsare the extrusion lengths of the additional points andthe binary material selection of the triangular facesthat are generated from the Delaunay triangulation.In our case study, four additional points make tentriangular façade geometries in 3D space, thus tencombinations of façade material and four extrusionlengths, or a total of fourteen input genes for the op-timizing algorithms.

In the multi-objective search, HypE mutationand reduction method (Bader and Zitzler 2011) wasadopted to reduce the evaluation time of the multi-objective optimization.

IMPLEMENTATIONIn this section, the simulation environment, such asthe location and the material properties of test roomis briefly explained. Furthermore, the implementa-tion of two case studies is presented.


Simulation environmentA square division is selected for the basic tessella-tion of the building façade, and we added identicalone-person office rooms to each square unit. Thebuilding is located in Incheon, Korea, and the test-ing façade is facing the south side. A typical officeroom is selected for the daylight simulation as a casestudy. The room dimension is 6m by 4.2m, depthand width respectively, and 3.2m in height (floor toceiling). The reflectiveness of the materials takenfrom Nabil and Mardaljevic (2005)'s experiment en-vironment are wall 0.7, ceiling 0.8, floor 0.2 and thewindow transmittance is 0.76 (Nabil and Mardalje-vic 2005). The office model is generated in a non-contextual environment. However, to take into ac-count the shading from the remainder of the façade,we applied the same façade geometry surroundingthe case room as shown in Figure 3. According tothe basic tessellation, the additional eight units arelocated one at the east and on at the west side ofthe case room and three positioned above as wellas three at the lower level. Consequently, nine unitshaving the identical façade geometry are generated3 x 3 where the center of the middle level is present-ing the testing room.

Case studyIn order to compare the daylighting ability resultsfrom conventional façade with our 3D façade, we ex-ecuted two sets of case studies. For the first casestudy, we simulated daylighting of a conventionalfaçade, and for the second case study, simulated day-lighting of our 3D façade created through optimiza-tion.

In the first case study, four additional points areplaced on the case room façade for the basic 2D tes-sellation unit. These four points are co-planar to theflat façade placed to create a rectangular windowin the horizontal center with the dimension of 2.8meters by 2 meters, and 1.2 meters above the test-ing floor (See Figure 3). In this simulation environ-ment, the façade had 46.5% window-to-wall ratio,UDI (100-2000) covered 74.3% of the floor area, and

1012.5 kWh annual electricity was generated fromthe opaque façade area.

For the second case study, we created a 3Dfaçade through extrusion of the optimization strat-egy and the fitness function. The population sizewasset to 50 and 25 generations have been conductedfor the optimization. General settings of parametersare reported in Table 1.

Table 1Multi objectiveoptimizationsettings.

A constraint was introduced with each evaluationand generation of the façade unit that surroundingunit shapes are identical to the case room façade de-sign. And extrusion lengths are limited to maximum2.0m. Other factors not considered is the façade con-struction, e.t. size and type of mullions.

RESULTS

Figure 4Pareto frontsolutions.

In Figure4, thedarkpoints connectedbyablackpoly-line represent the 50 pareto-front solutions or the


optimized design alternatives, after 25 generations.The total surface area of each non-dominant designswere 1.6 to 1.9 times larger than the initial office unitarea (17.64 m2). Thus no completely planar solutionhas been suggested from these particular case. To il-lustrate how each design alternatives performed, wechose three cases for example, two at the extremes(points A andC), andone in themiddle (point B) as in-dicated in Figure 7. The design alternative at point Ais the one that provides most daylight distribution inUDI100-2000, where type C generates the most elec-tricity using its BIPV surfaces annually. More specif-ically, the design alternative at point A performedthe best in terms of daylighting covering 82.7 % ofthe UDI within the range 100-2000 lux with the high-est windows-to-surface ratio (40.54%). And the de-sign alternative at point C had the largest total sur-faceareaandelectricitygenerationannually (2199.21kWh).

In order to better understand the relationshipsbetween the variables, we graphed the relationshipbetween the window-to-opaque area and the aver-age UDI achieved (Figure 5), and the relationship be-tween the total electricity generation and the BIPV(opaque area)-to-total surface ratio (Figure 6). As canbe seen, the result shows that when the window-to-surface area increases the achieved UDI also in-creases (Figure 5) even though having a concavetopology in most of the pareto-fronts.

Overall electricity generated in per meter squaredoes not seem to increase as the PV surface increase(Figure 6). Thismainly because of the shading impactfrom surrounding unit geometries.

A finding that is worth mentioning is the materi-als applied for the Delaunay space frames. The upperspace frames mostly remained opaque as indicatedin color black in Figure 8 which includes the repre-sentative design alternatives at points A, B, and C. Apossible reason for this frame material might be be-cause the top two additional points' locations are atthe ceiling level where the ceiling blocks the daylightsignificantly. Furthermore, the overall shape of thefaçade at the pareto-front is most often includes a

concave part. It is likely that the combination of con-caveandconvex surfacesoffersbetter solutions alter-nating opaque and transparent surfaces.

Figure 5Achieved UDI.

Figure 6BIPV efficiency.

DISCUSSION AND CONCLUSIONThis paper suggests a novel approach of designinga 3D shaped building façade that replaces conven-tional design methods. The suggested method addsextra points on the rectangular building façade unitsand connects them with the corner of the vertices ofa facade unit. The number of additional points andthe location of those are predetermined by the ar-chitect as designer and decision maker. Our methodsuggests the optimal extrusion lengths at the givenpoints and the combination of materials for the sur-faces (space frames) which are generated from the


Figure 7Pareto frontsolutions of themost UDI achievedA, The mostelectricitygenerated C, and inbetween B.


Delaunay triangulation by means of multi-objectiveoptimization.

When the optimization method we created wasapplied, we found that our pareto-front solutionshave significantly higher value in both daylightingand electricity generation in comparison to the con-ventional flat façade where the achieved UDI in-creased by 9% in the best case of daylighting per-formance and a growth in electricity harvesting of asmuch as 50% in the best case electricity generation.

The advantage of implementing of multi-objective optimization in this particular case weretwofold. First thenumber of unique solutions yieldedin pareto-front. Even though the performance is al-most identical, significantly different design alterna-tives could provide important information to design-ers in early design stage. The second adventage is,the comprehensive feedback on the performance ofthe optimal solutions. The Designers can achievetheir set goals for their façade designs by consider-ing the best trade-offs between the achieved UDIand the generated electricity not completely disre-garding one of the trade-offs.

Furthermore, the suggested method showsgreat flexibility and compatibility of generating anyfaçade design with the same process. The methodcould be used effectively in the early stages of a de-sign of an office building and assess the daylightingperformance and renewable energy generation pre-diction in a given location.

3D shaped building façades showed an advan-tage of energy harvesting and daylighting perfor-mances, however energy consumption was not ad-dressed in this research. Future works will extend thescope of this study by adding energy efficiency pa-rameters into the façade generation method.

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