Computational and Data-Driven Integration for Sustainable Solar Thermal Collector Design

Abstract

Solar thermal collectors represent a critical technology for sustainable energy harvesting, yet their optimal design remains challenging due to complex multi-physics interactions and numerous design parameters. This research presents a comprehensive computational framework that integrates Computational Fluid Dynamics (CFD) with data-driven optimization techniques to enhance solar thermal collector performance. The methodology combines advanced numerical modeling with machine learning algorithms to achieve both high prediction accuracy and computational efficiency. A validated three-dimensional CFD model was developed using ANSYS Fluent to simulate heat transfer phenomena within various collector geometries, including air-based systems with transverse triangular blocks and parabolic trough concentrators. The study generated 935 numerical cases across diverse operational parameters, which were subsequently used to train artificial neural networks (ANN), support vector regression (SVR), and linear regression models. The optimized ANN model achieved coefficient of determination values of 0.94, demonstrating superior predictive capabilities compared to traditional approaches. Entropy analysis identified thermal conductivity as the most influential parameter, contributing approximately 20% to overall thermal efficiency. The integrated approach successfully reduced computational time from over 1500 seconds for full CFD simulations to approximately 10 milliseconds for ANN predictions, while maintaining prediction accuracy within 6% of experimental data. Results indicate that collector designs incorporating heat transfer enhancement features such as triangular blocks and optimized geometric parameters achieve thermal efficiencies up to 68%, representing a 15% improvement over conventional configurations. Temperature distribution analysis revealed optimal operating ranges between 298K and 340K for maximum heat transfer effectiveness. The framework demonstrates significant potential for accelerating solar thermal system development while reducing computational costs and improving design optimization capabilities.