Machine Learning-Enhanced Turbulence Prediction and Flow Optimization for Advanced Aerodynamic Design in High-Speed Regimes

Abstract

The design of high-speed aerodynamic systems is challenged by the need for accurate turbulence prediction and efficient flow optimization. Traditional Computational Fluid Dynamics (CFD) methods often fall short due to their computational intensity and limitations in resolving high-frequency turbulent structures. Recent advancements in Machine Learning (ML) have demonstrated significant potential in revolutionizing this field. By leveraging deep learning, reinforcement learning, and other advanced algorithms, ML models address the inherent complexities of turbulence with enhanced accuracy and computational efficiency.

This study explores the integration of ML techniques in turbulence prediction and flow optimization for high-speed aerodynamic applications. It identifies key challenges in traditional CFD methods, such as their reliance on Reynolds-averaged Navier-Stokes equations and large eddy simulations, which struggle with multi-scale turbulence phenomena. ML-based approaches, including Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), offer innovative solutions for subgrid-scale modeling, flow field reconstruction, and real-time monitoring.

Additionally, the research highlights ML's role in aerodynamic optimization, employing reinforcement learning and genetic algorithms to automate design processes and achieve multi-objective optimization. Case studies demonstrate a 20% improvement in prediction accuracy for supersonic flows and substantial computational time savings. These findings underscore the transformative potential of ML in high-speed aerodynamic engineering, paving the way for next-generation aerospace technologies.