Machine Learning Applications in Fluid Mechanics

Machine learning (ML) has become a game-changing technique in fluid mechanics, providing fresh perspectives on analyzing and forecasting intricate phenomena. This chapter examines how ML may be integrated with fluid mechanics, emphasizing how it can transform tasks related to flow prediction, turbulence modeling, and optimization. An introduction to ML encompassing supervised, unsupervised, and semi-supervised learning techniques opens the chapter. The use of important algorithms in fluid mechanics is examined, including support vector machines, neural networks, and clustering approaches. The topic then shifts to physics-informed ML, which combines data-driven models with physical rules. Examples show how these techniques improve simulation and comprehension of fluid dynamics in terms of efficiency and accuracy. The chapter also discusses turbulence modeling, going over conventional methods such as direct numerical simulation (DNS), Reynolds-averaged Navier– Stokes (RANS), and large eddy simulation (LES), emphasizing how ML may either replace or enhance them. In addition to outlining prospects for further study, the concluding parts address the difficulties in integrating ML into fluid dynamics, including data scarcity, interpretability, and processing cost. To fully utilize ML in fluid mechanics, this chapter highlights the necessity of multidisciplinary cooperation.