Machine learning (ML) has been increasingly used to aid aerodynamic shape optimization (ASO), thanks to the availability of aerodynamic data and continued developments in deep learning. We review the applications of ML in ASO to date and provide a perspective on the state-of-the-art and future directions. We first introduce conventional ASO and current challenges. Next, we introduce ML fundamentals and detail ML algorithms that have been successful in ASO. Then, we review ML applications to ASO addressing three aspects: compact geometric design space, fast aerodynamic analysis, and efficient optimization architecture. In addition to providing a comprehensive summary of the research, we comment on the practicality and effectiveness of the developed methods. We show how cutting-edge ML approaches can benefit ASO and address challenging demands, such as interactive design optimization. Practical large-scale design optimizations remain a challenge because of the high cost of ML training. Further research on coupling ML model construction with prior experience and knowledge, such as physics-informed ML, is recommended to solve large-scale ASO problems.
J. Li et al., "Machine Learning in Aerodynamic Shape Optimization," Progress in Aerospace Sciences, vol. 134, article no. 100849, Elsevier, Oct 2022.
The definitive version is available at https://doi.org/10.1016/j.paerosci.2022.100849
Mechanical and Aerospace Engineering
Keywords and Phrases
Aerodynamic shape optimization; Airfoil design; Computational fluid dynamics; Machine Learning; Neural networks
International Standard Serial Number (ISSN)
Article - Journal
© 2023 Elsevier, All rights reserved.
01 Oct 2022
Advanced Research Projects Agency - Energy, Grant DE-FOA-0002107