Machine Learning in Aircraft Design: A Comprehensive Review of Optimization, Aerodynamics, and Structural Applications
Shima Mohaghegh, Ali Mohaghegh
Abstract
Machine learning (ML) with approximation and numerical simulations plays an important role in aircraft design. ML techniques, such as deep learning and reinforcement learning, are increasingly being adopted to solve complex, nonlinear problems in aircraft design, offering new opportunities for optimization and innovation. This technology is used in various topics of aerodynamics, fluid dynamics, acoustics, penetration, health monitoring, automatic control, etc. This article reviews recent studies related to different areas of ML application in aircraft design. The review highlights how ML can reduce computational costs while improving the precision of simulations, ultimately accelerating the design cycle and enhancing aircraft performance. The results show that ML is able to improve in all areas of application in aircraft design and the development of techniques in future applications will have a significant impact on the design of modern aircraft.