ENVIRONMENTAL PARAMETRIC MULTI-OBJECTIVE OPTIMIZATION FOR HIGH PERFORMANCE FACADE DESIGN XIAOFEI SHEN 1 AECOM 1 shenxiaofei.arch@gmail.com 1. Challenges Abstract. This paper demonstrates the applicability of a data-integrated and user-friendly Multi-Objective Optimization (MOO) method within the Grasshopper (GH) parametric design interface which supports early stage design decision making for High Performance Building (HPB) façade. With multiple environmental objectives optimized and multiple geometric parameters adjusted in the same intuitive design space, designers with limited knowledge on scripting could easily set up the nodes simultaneously when the design is carried out to achieve the efficiency in HPB design optimization. An experiment utilizing the method, with DIVA as the environmental simulator and Octopus as the MOO solver, is demonstrated for rational daylight distribution, balanced solar heat gain and reduced energy use intensity. The findings show both potentials and limitations of the proposed method. Keywords. Multi-Objective Optimization; Environmental Parametrics; Generative Design; High Performance Facade. High performance facade is of critical importance to buildings. Designers often seek help from computational Building Performance Simulations (BPS), such as lighting analysis and energy modeling, to evaluate the proposed façade s performance and optimize the design. However, the traditional BPS process can be time-consuming when repeating evaluation iterations, and error-prone when transferring data between design modeling software and BPS software. Therefore, due to the time limitations, BPS is usually less involved in the early design stage. Pairs of environmental design criteria are often conflicting against each other when designing high performance façade, such as daylighting adequacy and energy efficiency. Many academic studies introduced Multi-Objective Optimization (MOO) tools to HPB design which enables the quick design exploration and optimization according to pre-determined design criteria (Attia et al, 2013). However, they are mostly demanding in scripting and not generally integrated with modeling and simulation software. The efficiency of BPS and MOO depends on the computational power of the tools and the designers abilities to manipulate data. For the lack of easy-to-use integrated simulation and optimization software, the design exploring is inefficient and inaccurate in most cases. The rapid development of Genetic Algorithms (GA) T. Fukuda, W. Huang, P. Janssen, K. Crolla, S. Alhadidi (eds.), Learning, Adapting and Prototyping, Proceedings of the 23 rd International Conference of the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA) 2018, Volume 2, 103-112. 2018 and published by the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA) in Hong Kong.
104 X. SHEN tools such as Grasshopper (GH) is filling in the gaps between modeling, simulating and optimizing. 2. Background The application of MOO is already well known in the fields of economics, engineering, and medical treatments(theodor Stewart et al. 2008). A typical MOO process requires one or more simulators connecting to multiple variables as the inputs and multiple objectives as the outputs. The optimum set of results lies on the pareto frontier. Many cases demonstrated the MOO method being leveraged in HPB design achieving efficiencies in design decision making. The most commonly optimized variables are either energy-related or economic-related including building forms, envelope properties, and building system types. The objectives are typically energy performance and construction cost (Attia et al, 2013). Two mentionable studies focused on the façade optimization utilizing MOO were conducted by Gagne and Andersen in 2010, and Rahmani Asl et al in 2014. Both evaluated and optimized facade form for optimum daylight distribution and glare control. The author of the former ran the process in SketchUp and the latter developed scripts in Dynamo for Revit. Both required designers to export data to standalone BPS engines such as Lighsolve Viewer or Green Building Studio Run and conduct MOO separately. Data conversion from one software to another and complex scripting languages are the two biggest challenges. 3. Objectives The objective of the paper is to present a data-integrated and user-friendly method - Environmental Parametric Multi-Objective Optimization (EPMOO) - to generate, evaluate and optimize facade design efficiently in the early design stage based on its daylight and thermal performance. With multiple variables and multiple objectives involved, the proposed workflow integrates BPS and MOO tools into a parametric and generative building modeling program. 4. Methodology Figure 1. Methodology Workflow.
ENVIRONMENTAL PARAMETRIC MULTI-OBJECTIVE OPTIMIZATION FOR HIGH PERFORMANCE FACADE DESIGN 105 The proposed EPMOO utilizes DIVA-for-Rhino (DIVA) and Ladybug Tools (Ladybug) as the BPS tools and Octopus as the MOO tool within the GH parametric design modeling space. The paper details the five steps of the method as Analyze, Prototype, Evaluate, Evolve, and Select (see Figure 1). 4.1. ANALYZE: OBJECTIVE DEFINITION To understand the basic context and formulate design objectives, the method starts with an analysis of the local environmental condition, human comfort needs, and pre-determined design drivers, such as client vision, architectural aesthetics, and project budget. The conclusions include design constraints tying into Prototyping and weighting criteria used for Selection. The multiple objectives for simulation and optimization are defined accordingly. All objectives must be quantifiable and measurable for the BPS tools and the MOO tools to compute. 4.2. PROTOTYPE: VARIABLE DEFINITION Prototyping is to parametrically setup a geometric model in GH as the baseline for simulation and optimization. Both the fixed parameters and the variables are defined to control the base model. The fixed parameters are given by the basic context (e.g. site location), design assumptions (e.g. preferred window area), architectural principles (e.g. room size), and energy codes (e.g. internal gains). The variables can include geometric dimensions (e.g. window size) and material properties (e.g. insulation thickness). The range of all variables is defined based on design constraints. 4.3. EVALUATE: ENERGY PERFORMANCE SIMULATION Typical BPS includes daylight and dynamic thermal simulations. Two simulation types are the focus of EPMOO: The Environmental Performance Evaluation (EPE) and the Building Energy Modeling (BEM). EPE is the primary type of the simulation for EPMOO in developing facade morphology which is impacted by daylight, solar, and thermal. BEM usually starts at the design development phase by testing how the facade design affects the energy consumption. Traditional BPS tools, such as IES and equest, are mostly stand-alone and engineering-oriented. Parametric BPS tools, running parametric evaluations in GH, are more integrated with the design process and more intuitive to designers. DIVA and Ladybug are two major simulators of EPE in daylight simulation, glare study, and thermal analysis. Honeybee, another parametric tool under rapid development, is capable of conducting BEM through OpenStudio. To run the parametric simulations, variables and fixed parameters are connected to the simulators as inputs. The outputs are usually heat map meshes for visualization and editable data lists which can be further analyzed through a variety of graphs and diagrams customized in GH. 4.4. EVOLVE: SOLUTION GENERATION AND OPTIMIZATION To enhance the optimization, designers are required to evaluate as many solutions as possible. Therefore, parametric MOO tools need to be involved earlier in the
106 X. SHEN process to automatically explore every single design iteration in GH. Octopus is proposed as the MOO solver in the workflow. As a GH plug-in which applies evolutionary principles and multi-objective optimizations to parametric design, Octopus explores trade-offs between multiple objectives and preferred solutions (Vier et al. 2012). A typical GA process for EPMOO utilizing Octopus includes the following steps: 1. Variables defined in Prototyping are fed into Gene through NumberSliders or GenePool; Functional outcomes from the simulators connect to Objective. Octopus can automatically adjust variables, generate design iterations, trigger simulation toggles, and records all solutions. 2. A set of initial solutions are generated randomly with the performance evaluated. The number of generations - defined as max generation and the number of solutions in each generation - defined as population size - are setup in octopus. 3. The solutions with better performance results are counted as the parents for a new generation and those with poorer performance are discarded. Only the most favorable solutions enter the next generation. The rate of the better performance is defined as mutation rate in Octopus. 4. The loop will continue until the number of generations reaches max generation, or until the user stops the process. 4.5. SELECT: SOLUTION SORTING AND FINDING The MOO process will never end up with a single optimal answer, but a set of dominated solutions on the pareto frontier. To determine the individual solution for the design delivery, the final step is to filter and sort the optimal solutions set based on the weighting criteria customized in Analyzing. Two visualization approaches assisting with data sorting and finding are 3-D Coordinates and Parallel Coordinates (PCP). Octopus automatically visualizes the results of all the generations in its own solution space. A maximum five objectives can be visualized in the space as 3-D Coordinates, color, and size. While all the solutions are distributed in the coordinate axis view cube, the best-fitted instances are shown on the pareto front mesh (see Figure 2). Results can also be exported back to GH to customize other types of charts such as PCP(see Figure 7). PCP is a common way to visualize high-dimensional data including both variables and objectives in the same chart by adding multiple axes. The multi-dimensional problem can, therefore, be collapsed into a 2-D visual assessment.
ENVIRONMENTAL PARAMETRIC MULTI-OBJECTIVE OPTIMIZATION FOR HIGH PERFORMANCE FACADE DESIGN 107 Figure 2. The Solution Space in Octopus. 5. Experiment To demonstrate the capacity of the proposed EPMOO workflow, an experiment was conducted to explore an optimal HPB facade form. The typical bay of a new office building in New York City (NYC) was prototyped in GH for simulation through DIVA and optimization through Octopus. 5.1. ANALYZE The study started with local climate and comfort analysis. NYC is heating dominated and adequate solar resource is available. Consequently, natural daylight, passive solar heat gain, seasonal solar shades, and Building Integrated Photovoltaic (BIPV) were primarily considered as the design metrics. Three objectives to optimize were correspondingly determined as follows (see Figure 5): Maximize Daylight Penetration: The annual Daylight Factor (DF) on the working plane at the level of 0.7m should be maximum; Maximize BIPV Solar Catchment: The accumulated annual Solar Radiation (SR) on the BIPV panels should be maximum; Minimize potential for overheating in summer: The Solar Irradiation (SI) on the floor should be minimum in summer between May to September; 5.2. PROTOTYPE The initial design concept was to improve the environmental performance by adjusting dimensions and tilted angles of the three panels attaching to the façade (see Figure 3). Even if a wide range of parameters are related to the building performance, to simplify the process, the objective of the experiment was to only explore a set of optimal geometric solutions for the three panels: shading, light shelf, and deck. Proper shadings provide seasonal shadows; Light shelves bounce diffuse light into space and collect solar, if integrated with BIPV, to generate energy; Solar-protected decks make this perimeter zone comfortable to stay. All
108 X. SHEN other geometric parameters, such as window dimensions, and non-geometric parameters, such as façade material, were only taken as fixed parameters. Figure 3. 3 Panels. The 3-D typical bay was constructed in Rhinoceros, the dimension of which was set as 5.6m X 8m X 4m facing south. The Window-to-Wall Ratio was taken as 60%. The fixed parameters including surface material and internal loads were determined according to ASHRAE 90.1. The geometries of the three panels were parametrically modeled in GH. The controllable parameters were set up as variable inputs with limits as follows (see Figure 4): Shading Depth (0.4m - 2.0m) Shading Angle (0deg - 60deg) Light Shelf Depth (0.5m - 2.5m) Light Shelf Elevation from the Deck (2.2m - 3.0m) Light Shelf Angle (-30deg - 30deg) Deck Depth (1.5m - 3.5m) Figure 4. 6 Variables. 5.3. EVALUATE With the initial design solution connected to DIVA and Ladybug, three types of BPS were carried out to get the annual DF on the working plane, the annual SR on the light shelf, and the summer SI on the deck. Sensor grid size was defined as 0.5m X 0.5 on the three sensor surfaces.
ENVIRONMENTAL PARAMETRIC MULTI-OBJECTIVE OPTIMIZATION FOR HIGH PERFORMANCE FACADE DESIGN 109 Figure 5. 3 Objectives. 5.4. EVOLVE Octopus was the MOO solver of the experiment. The six variables controlled by the NumberSliders and the results from the three simulators were connected to Octopus. The 3-D geometric solution meshes were also fed into Octopus for visualization. Octopus automatically explored the optimal trade-offs between the three objectives and provided a pareto frontier in its own solution space. Figure 6. EPMOO Visualized in 3D Cubic Coordinates. 5.5. SELECT The optimization was terminated after running 10 generations when totally 500 solutions were generated. The solutions were shown in a 3-D Coordinates with each objective standing on one axis and a set of the dominated results were shown on the pareto frontier (see Figure 6). To manually select the optimal solutions based on different weighting criteria pre-determined in Analyzing, the results were computationally transferred into 2-D graphs to be filtered and sorted. The cube was collapsed by surfaces indicating
110 X. SHEN two objectives on each. PCP was also created to establish the relationships between variables and objectives (see Figure 7). Some trends were observed: Shorter and tilt-up shadings prevent summer solar but introduce winter sunlight which provide more balanced daylight distribution. Larger horizontal light shelves installed closer to the deck cast good shadow and provide acceptable area for BIPV. Shallower and horizontal decks provide functional space which has less chance to be overheated in summer. Figure 7. EPMOO Visualized in 2D. Figure 8. Preferred Options. 5.6. CLIMATE ADAPTATION The Climate Adaptation with the same method applied optimized the south façade in 3 cities with contrasting climate conditions. In Minneapolis, high heating demand requires winter solar gain to be maximized primarily. In New York, exposure to winter solar and the proper shadow in summer are both considered.
ENVIRONMENTAL PARAMETRIC MULTI-OBJECTIVE OPTIMIZATION FOR HIGH PERFORMANCE FACADE DESIGN 111 In Miami, big cooling load requires enough shadow all year around. The same prototype was consequently shifted to different results adapting to the climate. 6. Discussion The experiment demonstrated the viability of EPMOO in HPB design to generate, simulate, and optimize HPB facade design. Comparing to the traditional design process, the proposed methodology increases both the efficiency and the reliability in decision making at the early design stage, although the two characters are also the key limitations drawing from the experiment which can be further enhanced. 6.1. EFFICIENCY The efficiency of the EPMOO process is relevant to the seamless workflow, the computational power of BPS and MOO tools and their availability to the designer. The seamless workflow relies on the data integration platform. The experiment integrated the parametric BPS tools (DIVA and Ladybug) and the parametric MOO tool (Octopus) with GH. The model data transferring from one software to another was exempted so the processing time was saved. However, the final step to sort and find the solution was customized additionally. An alternative approach is to utilize Colibri, a new GH plug-in, to automatically connect data to a web-based sorting and filtering tool: Design Explorer. The computational power of the related software affects the simulation time. The experiment ran the solar simulations for 1,500 times which took about 12 hours to finish. The speed is not practical in professional practice. The rapid development of the cloud-based simulation, such as the Green Building Studio (GBS) developed by Autodesk, provides an opportunity to speed up the process. To reduce the learning curve using the parametric software, user-friendly visual programming tools such as GH are required in EPMOO. Designers with limited knowledge on scripting are able to perform analyses with easy set-ups in short time. This is efficient at the early design stage when decisions need to be made quickly and a wide range of solutions need to be explored. 6.2. RELIABILITY The reliability of the EPMOO results is dependent on the quality of the objectives and the variables. The less accurate model setup and the limited parameters selection will cause the optimized results less valuable. The three objectives used in the experiment are only related to solar and daylighting. Many other design metrics also need to be considered such as energy consumption, human comfort, and construction cost. Similarly, the six variables are only geometric-related and other non-geometric parameters such as material properties are not considered. The two primary reasons are the lack of multi-disciplinary collaborations and the limitations on the existing parametric BPS tools available. HPB design is based on a comprehensive understanding of parameters related to different disciplines. With the participation of different stakeholders such as architects, engineers, cost estimators, vendors, clients etc. at the early stage, the objectives and the variables can be better defined.
112 X. SHEN There is a wide range of BPS tools, but only a few of them are integrated with the parametric platform. DIVA and Ladybug used in the experiment can perform solar simulation, but are less capable of running energy models by manipulating envelope material selections and HVAC system controls. Honeybee, another parametric tool under rapid development, on the other hand, has the potential to bridge the gap due to its ability on both EPE and BEM. 7. Conclusion The data-integrated and user-friendly EPMOO methodology provides an automated approach to explore HPB façade, which is more efficient and more reliable comparing to traditional manual simulate-and-search process. The efficiency and the reliability of the workflow can be further enhanced by having more disciplines involved and adopting more powerful parametric software. As is discussed, in addition to HPB façade optimization, EPMOO can also be utilized to explore building massing orientation, structure, and building systems when facing conflicting criteria such as human comfort, energy consumption, capital expense, and life cycle cost. The comprehensive applications also rely on the on-going development of the related tools Building Information Modeling (BIM) is a similar, or even more promising platform for EPMOO. Revit is a powerful tool to intelligently model and design, and dynamo extends its capacity in data management. The BPS plugins - Insight and GBS - utilize cloud-based calculation engine to speed up the process. Solutions can be uploaded to web-based MOO tools - Fractal and Design Explorer. Even though the current eco-system is not as practical as GH, BIM-based EPMOO still deserves additional studies in the future References Asl, M.R., Bergin, M., Menter, A. and Yan, W.: 2014, BIM-based Parametric Building Energy Performance Multi-Objective Optimization, ecaade 2014 Conference. Attia, S., Hamdy, M., O, W. and Carlucci, S.: 2013, Assessing Gaps and Needs for Integrating Building Performance Optimization Tools in Net Zero Energy Buildings Design, Energy and Buildings, 155(2017), 439-458. Gagne, J.M.L. and Andersen, M.: 2010, Multi-Objective Façade Optimization for Daylighting Design Using a Genetic Algorithm, SimBuild 2010-4th National Conference of IBPSA-USA, New York. Han, Y., Yu, H. and Sun, C.: 2017, Simulation-Based Multiobjective Optimization of Timber-Glass Residential Buildings in Severe Cold Regions, Sustainability, 9(12), 2352. Hensen, J.L.M. and Lamberts, R.: 2011, Building Performance Simulation for Design and Operation, Routledge, FL USA. Howes, B.: 2016, Design Explorer Natural History. Available from <http://core.thorntont omasetti.com/blog/> (accessed September 2016). Nagy, D., Lau, D., Locke, J., Stoddart, J., Villaggi, L., Wang, R., Zhao, D. and Benjamin, D.: 2017, Project Discover: An application of generative design for architectural space planning, SimAUD 2017 Conference. Torres, S.L. and Sakamoto, Y.: 2007, Facade Design Optimization for Daylight with A Simple Genetic Algorithm, Building Simulation 2007.