Heat transfer analysis of phase change process in a finned-tube thermal energy storage system using artificial neural network
Heat transfer analysis of phase change process in a finned-tube thermal energy storage system using artificial neural network
复制标题
DOI：
10.1016/j.ijheatmasstransfer.2006.12.017
复制
发表时间：
2007-07-01
影响因子：
5.2
通讯作者：
Dincer, Ibrahim
中科院分区：
文献类型：
--
作者：
Ermis, Kemal;Erek, Aytunc;Dincer, Ibrahim
在这项研究中，前馈反向传播人工神经网络（ANN）算法提出的翅片管相变过程的传热分析，潜热热能储存系统。对相变材料在翅片管周围的蓄热进行了实验研究。通过求解传热流体、管壁和相变材料的控制方程，研究了翅片和流动参数的影响。采用学习过程关联不同翅片型式、不同雷诺数和不同进口温度的换热管总热量。通过对神经网络隐层数的训练，得到了蓄热器的最佳输出预测值。预测的总热存储值与广泛的非训练实验数据集的人工神经网络模型，然后与实验测量和数值计算结果进行比较。训练后的神经网络模型的绝对平均相对误差为5.58%，表现出良好的性能，预测总的热量存储。人工神经网络的结果被发现是更准确的数值模型的结果。本研究采用人工神经网络方法对相变储热过程进行传热分析，对实际的储热应用具有重要意义。(C)2007爱思唯尔有限公司版权所有。
In this study, a feed-forward back-propagation artificial neural network (ANN) algorithm is proposed for heat transfer analysis of phase change process in a finned-tube, latent heat thermal energy storage system. Heat storage through phase change material (PCM) around the finned tube is experimentally studied. A numerical study is performed to investigate the effect of fin and flow parameter by the solving governing equations for the heat transfer fluid, pipe wall and phase change material. Learning process is applied to correlate the total heat stored in different fin types of tubes, various Reynolds numbers and different inlet temperatures. A number of hidden numbers of ANN are trained for the best output prediction of the heat storage. The predicted total heat storage values obtained by an ANN model with extensive sets of non-training experimental data are then compared with experimental measurements and numerical results. The trained ANN model with an absolute mean relative error of 5.58% shows good performance to predict the total amount of heat stored. The ANN results are found to be more accurate than the numerical model results. The present study using ANN approach for heat transfer analysis in phase change heat storage process appears to be significant for practical thermal energy storage applications. (C) 2007 Elsevier Ltd. All rights reserved.