MACHINE LEARNING–BASED MODELING OF FLUID FLOW AND HEAT TRANSFER IN ENGINEERING SYSTEMS

The integration of machine learning (ML) techniques into computational fluid dynamics and heat transfer analysis represents a paradigm shift in engineering modeling. This paper presents a comprehensive review and analysis of ML-based approaches for modeling fluid flow and heat transfer phenomena in various engineering systems. Traditional computational methods, while accurate, often require substantial computational resources and time. Machine learning algorithms, including neural networks, support vector machines, and ensemble methods, offer promising alternatives for real-time prediction, optimization, and control of thermal-fluid systems. This study examines the application of supervised and unsupervised learning techniques across different engineering domains, including heat exchangers, electronic cooling systems, and renewable energy applications. We analyze the performance metrics of various ML models, discuss implementation challenges, and provide insights into future research directions. Results demonstrate that ML-based models can achieve prediction accuracies exceeding 95% while reducing computational time by orders of magnitude compared to traditional CFD simulations.