Latent Heat Prediction of Nano Enhanced Phase Change Material by ANN Method

Abstract

Thermal characteristics of phase change material (PCM) are important in design and utilization of thermal energy storage or other applications. PCMs have great latent heat but suffer from low thermal conductivity. Then, in recent years, nano particles have been added to PCM to improve their thermophysical properties such as thermal conductivity. Effect of this nano particles on thermophysical properties of PCM has been a question and many experimental and numerical studies have been done to investigate them. Artificial intelligence-based approach can be a good candidate to predict thermophysical properties of nano enhance PCM (NEPCM). Then, in this study an artificial neural network (ANN) has been developed to predict the latent heat of the NEPCM. A comprehensive literature search was conducted to acquire thermal characteristics data from various NEPCM to train and test this artificial neural network model. Twenty different types of Nano particle and paraffin based PCMs were used in ANN development. The most important properties which are used as the input for the developed ANN model are NP size, density of NP, latent heat of PCM, density of PCM, concentration and latent heat of NEPCM in the range of 1-60 nm, 100-8960 kg/m 3 , 89.69-311 kJ/kg, 760 to 1520 kg/m 3 , 0.02-20 wt% and 60.72-338.6 kJ/kg, respectively. The output variable was latent heat of NEPCM. The result indicates that the ANN model can be applied to predict the latent heat of nano enhanced PCM satisfactory. The correlation coefficient of the created model was 0.97. This result shows ability of ANN to predict the latent heat of NEPCM.

Experimental Investigation and Artificial Neural Network-Based Prediction of Thermal Conductivity of Metal Oxide-Enhanced Organic Phase-Change Materials

“…It was reported that the trained neural network was sufficient to predict the experimental data, achieving a coefficient of determination (R 2 ) of 0.9985 and 0.9973 during the sampling of the training and testing datasets, respectively. Jaliliantabar et al [25] collected a dataset comprising twenty different nanoparticles to develop an AI-based model for the prediction of latent heat of an NEPCM. The training input layer consisted of nine neurons including a density of dispersed nanoparticles, particle size, latent heat value of PCM, density of pure PCM, and latent heat of the NEPCM.…”