Intelligent Parametric Optimization of Building Atrium Design: A Case Study for a Sustainable and Comfortable Environment

Yunzhu Ji, Minghao Xu, Tong Zhang and Yingdong He ()
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Yunzhu Ji: School of Architecture, Southeast University, Nanjing 210096, China
Minghao Xu: College of Environmental Design, University of California, Berkeley, CA 94720, USA
Tong Zhang: School of Architecture, Southeast University, Nanjing 210096, China
Yingdong He: Center for the Built Environment, University of California, Berkeley, CA 94720, USA

Sustainability, 2023, vol. 15, issue 5, 1-25

Abstract: Building atrium design is crucial to maintaining a sustainable built environment and providing thermal comfort to occupants. This study proposes a parametric framework to optimize the atrium’s geometry for environmental performance and thermal comfort improvement. It integrates the parametric design, performance simulation, and multi-objective optimization in the Rhino and Grasshopper platform to realize automatic optimization. The atrium’s well index, shape ratio, volume ratio, position index, and inner interface window-to-wall ratio were set as optimized factors. For the optimization objectives, useful daylight illuminance (UDI), energy use intensity (EUI), and the discomfort time percentage (DTP) were chosen as metrics for the measurement of daylighting, energy use efficiency, and thermal comfort, respectively. Moreover, a geometry mapping method is developed; it can turn atrium shape into rectangular profiles. Thus, the framework can apply to general buildings. To validate the effectiveness of the proposed framework, an atrium optimization case study is conducted for a villa in Poland. According to the optimization results, the performance of the compared three objectives are improved by 43.20%, 15.52%, and 3.89%, respectively. The running time for the optimization is about 36 s per solution, which greatly reduce the human and time cost compared to the traditional working method.

Keywords: building atrium; parametric design; built environment; thermal comfort; multi-objective optimization; optimization framework (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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