Machine Learning-Based Performance Analysis of Solar Thermal Storage Tanks with Fin-Configured Phase Change Materials

Abstract

Solar thermal energy storage systems play a vital role in bridging the gap between intermittent solar availability and continuous energy demand; however, their efficiency is severely constrained by the inherently low thermal conductivity of phase change materials. Integrating physical heat transfer enhancements, such as radial fins, offers a practical solution, but evaluating these non-linear thermal dynamics across diverse design configurations typically incurs heavy computational costs. To address this challenge, this research investigates an artificial intelligence-based predictive framework capable of accurately modeling complex phase change dynamics in fin-configured storage tanks. Utilizing high-fidelity 2D Computational Fluid Dynamics simulation data of a stainless-steel double-tube storage tank filled with RT-50 paraffin wax across 10, 20, and 29 fin configurations, a Multi-Layer Perceptron Artificial Neural Network trained with the Bayesian Regularization algorithm was developed. The model predicts liquid fraction, latent heat distribution, and buoyancy-driven natural convection (Reynolds number) based on fin count and time. The optimal architecture, featuring 30 hidden neurons, achieved exceptional predictive precision, yielding a coefficient of determination of 0.99999, along with individual Mean Squared Error values of 1.29 × 10−3 for liquid fraction, 5.73 × 10−1 for latent heat distribution, and 2.64 × 10−5 for Reynolds number, with average prediction deviation rates consistently below 0.5%. These results demonstrate that high-precision surrogate modeling can effectively replace computationally intensive numerical simulations, offering significant practical implications for the real-time thermal monitoring, rapid design optimization, and intelligent control of advanced solar energy storage technologies.