A robust metamodel-based optimization design method for improving pedestrian wind comfort in an infill development project. (September 2021)

• Record Type: Journal Article
• Authors: Wu, Yihan; Zhan, Qingming; Quan, Steven Jige
• Is Part Of: Sustainable cities and society. Volume 72(2021)
• Publication Date: 2021-09
• Subjects: ACR air change rate -- ANN artificial neural network -- AVA Hong Kong's technical guide for air ventilation assessment -- BBD Box-Behnken design -- CCRD central composite rotatable design -- CFD computational fluid dynamics -- DoE Design of experiment -- FFD full factorial design -- GCI grid convergence index -- MOEA/D Multi-objective Evolutionary Algorithm based on Decomposition -- MaOPSO Many-Objective Particle Swarm Optimization -- NSGA-II Non-dominated Sorting Genetic Algorithm II -- PBD Plackett-Burman design -- RMSE root mean square error -- RS response surface -- RSM response surface methodology; Environment impact assessment -- Pedestrian-level wind comfort -- Response surface methodology -- Desirability function -- Multiple response optimization; Sustainable urban development -- Periodicals; Sustainable buildings -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.76
• DOI: 10.1016/j.scs.2021.103018
• Languages: English
• ISSNs: 2210-6707

Abstract

Highlights: This study proposes a robust metamodel-based optimization scheme for building and urban design. Simultaneous improvement pedestrian-level wind comfort in different seasons are achieved. Optimal designs can be obtained through minimal experimental effort. The design scheme encompasses RSM, DoE, CFD simulations and desirability functions. Lower family error rates lead to more conservative optimal design plans. Abstract: Constructing a new building inevitably modifies the microclimate in its vicinity. We propose a metamodel-based optimization method to devise near optimal designs that minimize building's adverse impacts on nearby pedestrians' wind comfort in hot and cold seasons. Four design parameters, i.e., building width, building depth, building height, and building orientation, are considered for a greatly simplified building design task. Specifically, computational fluid dynamics (CFD) analyses are performed to calculate the mean wind velocity, while all CFD experiment samples are determined through the Box-Behnken design of experiment method. Based on 54 CFD experiments, the relationships between building design variables and the summer and winter mean wind velocities within a specified assessment area are learned using the response surface methodology. Finally, a genetic algorithm with the desirability function as the objective function is applied to identify near optimal design options under the robustness control (i.e., family error rate λ) for response surface models. The framework is applied to an infill development project to highlight its suitability in a real application. The experiment results show that as λ gradually decreases from 1 to 0.05, the overall desirability index dwindles from 0.33 to 0.22, consequently generating a more conservative optimal design decision.