| Abstract |
Intelligent aerodynamics, as an interdisciplinary field combining aerodynamics and artificial intelligence (AI), has achieved remarkable progress in recent years. Traditional aerodynamic methods face challenges in analyzing complex flow fields, turbulence prediction, and aerodynamic optimization due to high computational costs and lengthy experimental cycles. In contrast, AI techniques, particularly machine learning and deep learning, offer data-driven solutions to these challenges. This paper systematically reviews the research background and core issues of intelligent aerodynamics, discussing the applications and advancements of machine learning, deep learning, and reinforcement learning in flow simulation, turbulence control, aerodynamic design optimization, and flight trajectory planning. Through case studies, the research demonstrates significant improvements in prediction accuracy and computational efficiency when addressing nonlinear problems in fluid mechanics using AI. Finally, this paper outlines future directions for intelligent aerodynamics, emphasizing efficient computation, integration of complex systems, and interdisciplinary collaboration.
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