Rapid Simulation of Optimally Responsive Façade during Schematic Design Phases: Use of a New Hybrid Metaheuristic Algorithm

Hwang Yi, Mi-Jin Kim, Yuri Kim, Sun-Sook Kim and Kyu-In Lee
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Hwang Yi: Architectural Design & Technology Lab, Department of Architecture, Ajou University, Suwon 16499, Korea
Mi-Jin Kim: Architectural Design & Technology Lab, Department of Architecture, Ajou University, Suwon 16499, Korea
Yuri Kim: Architectural Design & Technology Lab, Department of Architecture, Ajou University, Suwon 16499, Korea
Sun-Sook Kim: Department of Architecture, College of Engineering, Ajou University, Suwon 16499, Korea
Kyu-In Lee: Department of Architecture, College of Engineering, Ajou University, Suwon 16499, Korea

Sustainability, 2019, vol. 11, issue 9, 1-28

Abstract: Operation of environmentally responsive building components requires rapid prediction of the optimal adaptation of geometric shapes and positions, and such responsive configuration needs to be identified during the design process as early as possible. However, building simulation practices to characterize optimized shapes of various geometric design candidates are limited by complex simulation procedures, slow optimization, and lack of site information. This study suggests a practical approach to the design of responsive building façades by integrating on-site sensors, building performance simulation (BPS), machine-learning, and 3D geometry modeling on a unified design interface. To this end, a novel and efficient hybrid optimization algorithm, tabu-based adaptive pattern search simulated annealing (T-APSSA), was developed and integrated with wireless sensor data communication (using nRF24L01 and ESP8266 WiFi modules) on a parametric visual programming language (VPL) interface Rhino Grasshopper (0.9.0076, McNeel, Seattle, USA). The effectiveness of T-APSSA for early-stage BPS and optimal design is compared with other metaheuristic algorithms, and the proposed framework is validated by experimental optimal envelope (window shading) designs for single (daylight) and multiple (daylight and energy) objectives. Test results demonstrate the improved efficiency of T-APSSA in calculations (two to four times faster than other algorithms). This T-APSSA-integrated sensor-enabled design optimization practice supports rapid BPS and digital prototyping of responsive building façade design.

Keywords: design optimization; optimization algorithm; adaptive building façade; building performance simulation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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