Wind and Structures

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Machine learning-enabled parameterization scheme for aerodynamic shape optimization of wind-sensitive structures: A-proof-of-concept study

  • Shaopeng Li (University of Louisiana at Lafayette) ;
  • Brian M. Phillips (University of Florida) ;
  • Zhaoshuo Jiang (San Francisco State University)
  • Received : 2024.02.16
  • Accepted : 2024.08.06
  • Published : 2024.09.25
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Abstract

Aerodynamic shape optimization is very useful for enhancing the performance of wind-sensitive structures. However, shape parameterization, as the first step in the pipeline of aerodynamic shape optimization, still heavily depends on empirical judgment. If not done properly, the resulting small design space may fail to cover many promising shapes, and hence hinder realizing the full potential of aerodynamic shape optimization. To this end, developing a novel shape parameterization scheme that can reflect real-world complexities while being simple enough for the subsequent optimization process is important. This study proposes a machine learning-based scheme that can automatically learn a low-dimensional latent representation of complex aerodynamic shapes for bluff-body wind-sensitive structures. The resulting latent representation (as design variables for aerodynamic shape optimization) is composed of both discrete and continuous variables, which are embedded in a hierarchy structure. In addition to being intuitive and interpretable, the mixed discrete and continuous variables with the hierarchy structure allow stakeholders to narrow the search space selectively based on their interests. As a proof-of-concept study, shape parameterization examples of tall building cross sections are used to demonstrate the promising features of the proposed scheme and guide future investigations on data-driven parameterization for aerodynamic shape optimization of wind-sensitive structures.

Keywords

Acknowledgement

This material is based upon work supported by the National Science Foundation (NSF) under Grants No. 2028762 & 2028647. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of NSF.

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