Abstract
The present paper provides a novel hybrid computational framework that integrates Computational Fluid Dynamics (CFD) with advanced machine learning techniques to optimize solar thermal collectors employing micro-heat pipe arrays (MHPA) for food dehydration applications. The methodology addresses the fundamental challenge of balancing computational efficiency with prediction accuracy in thermal system design. A validated CFD model generated 935 numerical cases across diverse operational and design parameters, which were used to train and evaluate three machine learning algorithms: linear regression (LR), support vector regression (SVR), and artificial neural networks (ANN). While baseline LR achieved R² = 0.61, optimized SVR and ANN models demonstrated superior performance with R² values of 0.96 and 0.94 respectively. The study identifies a critical transition at 100-300 samples where error rates drop sharply, with optimal performance requiring more than 600 samples. Entropy analysis quantified information transfer between input parameters and thermal efficiency, identifying MHPA thermal conductivity as the most influential parameter (~ 20% mutual information), followed by air inlet temperature (~ 17%) and air velocity (~ 14%). This information-theoretic approach provided clear design priorities by measuring entropy reduction potential of each parameter. Interpretability analysis established optimal operating ranges for key parameters including MHPA equivalent thermal conductivity, power density, glass cover heat transfer coefficient, and air inlet temperature. The hybrid methodology shows promise for efficiently optimizing solar thermal collector designs at lower computational costs than traditional methods to provide valuable insights for solar food drying systems.