Advancing Computational Fluid Dynamics through Machine Learning: A Review of Data-Driven Innovations and Applications

Abstract

This review explores Machine Learning (ML) integration with Computational Fluid Dynamics (CFD) to enhance simulation accuracy and efficiency. CFD has long been the standard for fluid flow analysis, but its computational cost and limitations in handling complex flows have spurred interest in data-driven approaches. With its ability to model nonlinear relationships, machine learning offers a promising solution. The review covers vital methodologies, including data acquisition from CFD simulations, feature extraction, and the selection of ML models such as neural networks and supervised learning algorithms. Furthermore, it examines the application of ML in accelerating CFD simulations and improving turbulence modeling. While promising, challenges remain, particularly in ensuring ML models respect the underlying physical laws governing fluid dynamics. The review concludes by discussing future research directions, particularly in physics-informed machine learning, which seeks to integrate physical constraints into the ML process. This combination of data-driven and physics-based approaches can transform CFD applications across industries.