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A review of surrogate-assisted design optimization for improving urban wind environment
Abstract Improving the urban wind climate yields substantial advantages, encompassing enhanced public health, increased pedestrian safety, improved building energy efficiency, and effective heat stress mitigation. In the past two decades, the prominence of surrogate models in urban wind environment studies has grown remarkably. These models efficiently approximate dynamic wind behaviors, expediting predictions and compressing design cycles from months to days. Constructing an accurate and reliable surrogate model requires careful consideration of four crucial aspects: problem formulation, sample selection, surrogate modeling, and uncertainty quantification. Problem formulation necessitates the selection of surrogate predictors and response variables to faithfully represent the relationship between building/urban form and wind behaviors. While sample selection can be executed through four major methods, including the classic and modern design of experiment methods, adaptive and intelligent sampling method, and generative design techniques. Furthermore, this paper conducts a comprehensive review of the latest literature to juxtapose the advantages and limitations of diverse surrogate models, and it also unveils key strategies for constructing effective surrogate models. Additionally, the study underscores an often-overlooked aspect in surrogate optimization studies—the quantification of uncertainty in surrogate predictions, an important element for ensuring design robustness. To address this, we discuss three approaches for quantifying surrogate model prediction errors: deterministic, possibilistic, and probabilistic methods. Finally, we introduce a novel rule-based approach for surrogate model construction. This approach not only prioritizes robustness of surrogate models but also provides solutions for improving the urban wind environment across varying design problem scales, design sample sizes, and levels of urban airflow non-linearity.
Highlights Advancements in surrogate models applied to improve urban wind environment are systematically reviewed. Sampling, modeling, and uncertainty quantification are summarized for surrogate construction. Deterministic, possibilistic, and probabilistic uncertainty measures are explained. A rule-based surrogate module selection scheme is proposed.
A review of surrogate-assisted design optimization for improving urban wind environment
Abstract Improving the urban wind climate yields substantial advantages, encompassing enhanced public health, increased pedestrian safety, improved building energy efficiency, and effective heat stress mitigation. In the past two decades, the prominence of surrogate models in urban wind environment studies has grown remarkably. These models efficiently approximate dynamic wind behaviors, expediting predictions and compressing design cycles from months to days. Constructing an accurate and reliable surrogate model requires careful consideration of four crucial aspects: problem formulation, sample selection, surrogate modeling, and uncertainty quantification. Problem formulation necessitates the selection of surrogate predictors and response variables to faithfully represent the relationship between building/urban form and wind behaviors. While sample selection can be executed through four major methods, including the classic and modern design of experiment methods, adaptive and intelligent sampling method, and generative design techniques. Furthermore, this paper conducts a comprehensive review of the latest literature to juxtapose the advantages and limitations of diverse surrogate models, and it also unveils key strategies for constructing effective surrogate models. Additionally, the study underscores an often-overlooked aspect in surrogate optimization studies—the quantification of uncertainty in surrogate predictions, an important element for ensuring design robustness. To address this, we discuss three approaches for quantifying surrogate model prediction errors: deterministic, possibilistic, and probabilistic methods. Finally, we introduce a novel rule-based approach for surrogate model construction. This approach not only prioritizes robustness of surrogate models but also provides solutions for improving the urban wind environment across varying design problem scales, design sample sizes, and levels of urban airflow non-linearity.
Highlights Advancements in surrogate models applied to improve urban wind environment are systematically reviewed. Sampling, modeling, and uncertainty quantification are summarized for surrogate construction. Deterministic, possibilistic, and probabilistic uncertainty measures are explained. A rule-based surrogate module selection scheme is proposed.
A review of surrogate-assisted design optimization for improving urban wind environment
Wu, Yihan (author) / Quan, Steven Jige (author)
Building and Environment ; 253
2023-12-29
Article (Journal)
Electronic Resource
English
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