Machine learning in aerodynamic shape optimization

• Published in: Progress in aerospace sciences Vol. 134; no. C; p. 100849
• Main Authors: Li, Jichao, Du, Xiaosong, Martins, Joaquim R.R.A.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.10.2022; Elsevier BV; Elsevier
• Subjects: Aerodynamic shape optimization; Airfoil design; Algorithms; Computational fluid dynamics; Deep learning; Design optimization; Machine Learning; Neural networks; Shape optimization; Aerodynamic shape optimization; Airfoil design; Computational fluid dynamics; Neural networks; Machine Learning
• ISSN: 0376-0421; 1873-1724
• DOI: 10.1016/j.paerosci.2022.100849

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.