Enhancing Computational Fluid Dynamics Simulations with Machine Learning: Techniques, Challenges, and Future Prospects

Abstract

Recent studies have shown that the combination Machine Learning (ML) with the Computational Fluid Dynamics (CFD), can be considered as a revolutionary solution for the resolution of the well-known difficulty in fluid simulation such as the high computational costs and a complexity related to the use of traditional solvers. This study examines the levels of accuracy, efficiency, and scalability of CFD simulations that can be obtained from different ML models such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Physics-Informed Neural Networks (PINNs). We evaluate each model in terms of mean squared error, structural similarity, inference time, and physical consistency, such as drag and lift coefficient prediction based on the benchmark datasets for steady and unsteady flows. CNNs achieved the highest balance between speed and accuracy for steady flows, but LSTMs evidenced the capacity of capturing temporal dynamics though they accumulated error over time. PINNs, although slower, offered long-term stability and generalization by incorporating physical laws in the learning process. The results suggest that although ML is not a complete substitute for traditional CFD, it provides significant tools for speeding up simulations and making possible real-time applications when used appropriately. Building upon the aforementioned discussion, this paper further explores the implications, limitations, and future directions of ML enhanced CFD presenting insights into the requirement of hybrid architectures, interpretability, and how data management strategy would be needed to implement these models in the mainstream engineering practices.