Machine learning-based prediction of transient latent heat thermal storage in finned enclosures using group method of data handling approach: A numerical simulation

Abstract

This paper presents the numerical simulations and machine learning-based prediction of the transient melting process of phase change material (PCM) in latent heat thermal storage (LHTS) units. The storage units are rectangular enclosures equipped with fins of different heights and numbers. For all enclosures, the volume of fins and PCM are kept constant. Melting processes of PCM in different storage units are simulated using computational fluid dynamics (CFD) to determine the impacts of fin parameters on the thermal behavior of the LHTS unit. Transient variation of liquid fraction and stored energy in the different storage units are obtained. Then, the group method of data handling (GMDH) type of artificial neural networks (ANNs) is employed and trained through numerical findings to develop correlations for predicting the instantaneous liquid fractions and stored energy in the finned enclosures. To evaluate the effectiveness of the prediction model, mean square, root mean square, and standard deviation errors as well as correlation coefficient have been calculated and proved the accuracy of the proposed correlations.