Exploring the Application of Machine Learning in Computational Fluid Dynamics

Computational fluid dynamics (CFD) is an important tool for understanding and predicting the behavior of fluids in various systems. Machine learning techniques have the potential to significantly improve the efficiency and accuracy of CFD simulations, and have been applied to a wide range of tasks, including turbulence modeling, boundary layer prediction, flow separation prediction, and optimization of system design. In this review, we provide an overview of the main machine learning methods that have been applied in CFD, including neural networks, support vector machines, evolutionary algorithms, and reinforcement learning. We also discuss the benefits and limitations of these methods and their potential applications in different fields, such as aerospace engineering, automotive engineering, biomedical engineering, and environmental engineering. Finally, we identify future research directions and provide recommendations for the effective use of machine learning techniques in CFD.