Role of Machine Learning in Computational Fluid Dynamics

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Author(s): G. Prasad (Chandigarh University, Punjab, India), Snehal Malik (Chandigarh University, Punjab, India), Aadya Gupta (Chandigarh University, Punjab, India)and Yash Nigam (Chandigarh University, Punjab, India)
Copyright: 2026
Pages: 26
Source title: Innovative Machine Learning Applications in the Aerospace Industry
Source Author(s)/Editor(s): Venkata Tulasiramu Ponnada (Collins Aerospace, USA)
DOI: 10.4018/979-8-3693-7525-9.ch006

Keywords: Aerospace Engineering / Engineering Science Reference / Knowledge Management and Library Science / Machine Learning

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Abstract

The integration of machine learning (ML) with computational fluid dynamics (CFD) marks a significant advancement in the simulation and analysis of fluid flows. This chapter explores the synergistic role of machine learning in enhancing CFD methodologies, focusing on applications in modeling, optimization, and real-time analysis. Machine learning algorithms, particularly deep learning, offer powerful tools for identifying patterns and correlations within large datasets generated by CFD simulations. These algorithms can be trained to predict fluid behavior, accelerate simulation processes, and improve the accuracy of models by learning from empirical data. In modeling, ML techniques reduce the reliance on traditional empirical models, offering more precise and computationally efficient alternatives. Furthermore, ML-driven optimization techniques enhance the design process of fluid systems by enabling rapid evaluation of multiple design variables. Real-time data processing and analysis facilitated by ML also support adaptive control and decision-making in dynamic fluid environments.

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