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.
翻译:由于空气动力数据的存在和深层学习的持续发展,机器学习(ML)日益被用来帮助优化空气动力形状(ASO),因为有空气动力数据,而且不断进行深层学习;我们审查在ASO中ML的应用情况,并提供关于最新技术和未来方向的视角;我们首先引进常规的ASO和当前的挑战;接着,我们引进在ASO中成功的ML基本原理和详细的ML算法;然后,我们审查在ASO中涉及以下三个方面的ML应用情况:紧凑的几何设计空间、快速空气动力学分析和高效优化结构;除了提供研究的全面摘要外,我们还评论所开发的方法的实用性和有效性;我们展示尖端ML方法如何使ASO受益并解决具有挑战性的需求,例如互动设计优化;由于ML培训费用高,实际的大规模设计优化仍然是一项挑战;建议进一步研究与以前的经验和知识(例如物理知情的ML)相结合的ML模型的建造,以解决大规模ASO问题。