Machine learning in aerodynamic shape optimization
National University of Singapore · University of Michigan
Abstract
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…
Citation impact
- FWCI
- 34.02
- Percentile
- 100%
- References
- 591
Authors
3Topics & keywords
- Aerodynamics
- Computer science
- Enhanced Data Rates for GSM Evolution
- Scale (ratio)
- Architecture
- Perspective (graphical)
- Artificial intelligence
- Machine learning
- Industry, innovation and infrastructure