Machine Learning-Driven Thermal Analysis for Enhanced Energy Efficiency in Heat Transfer Systems

Energy efficiency is essential in heat transfer systems to reduce expenses and environmental impact. Traditional thermal analysis methods typically do not adequately address complex, dynamic, and multi-variable scenarios in these systems. This research presents a novel approach for enhancing the energy efficiency of heat transfer systems through the application of machine learning and thermal analysis. The suggested system evaluates temperature gradients, heat flux, and additional thermodynamic parameters in real time through supervised learning techniques. The technology consistently predicts system behavior, identifies inefficiencies, and provides energy-efficient designs by training models on historical and operational data. Gradient Boosting surpassed other machine learning models in accuracy (93.8%), precision (92.5%), recall (93.0%), and F1 score (93.2%). The results indicate significant enhancements in energy efficiency and establish a robust foundation for the design, operation, and optimization of energy systems, yielding advantages for both the environment and the economy.