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.

本文介绍了潜热蓄热 (LHTS) 装置中相变材料 (PCM) 瞬态熔化过程的数值模拟和基于机器学习的预测。存储单元是矩形外壳，配有不同高度和数量的翅片。对于所有外壳，散热片和 PCM 的体积保持不变。使用计算流体动力学 (CFD) 模拟不同存储单元中 PCM 的熔化过程，以确定翅片参数对 LHTS 单元热行为的影响。获得了不同存储单元中液体分数和存储能量的瞬态变化。然后，采用数据处理组方法 (GMDH) 类型的人工神经网络 (ANN)，并通过数值结果进行训练，以开发相关性，以预测翅片外壳中的瞬时液体分数和存储能量。为了评估预测模型的有效性，计算了均方、均方根和标准差误差以及相关系数，并证明了所提出的相关性的准确性。