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

Thermal characteristics of phase change material (PCM) are important in design and utilization thermal energy storage or other applications. PCMs have great latent heat but suffer from low conductivity. Then, recent years, nano particles been added to PCM improve their thermophysical properties such as Effect this on has a question many experimental numerical studies done investigate them. Artificial intelligence-based approach can be good candidate predict enhance (NEPCM). study an artificial neural network (ANN) developed the NEPCM. A comprehensive literature search was conducted acquire data various NEPCM train test model. Twenty different types Nano particle paraffin based were used ANN development. The most which input for model NP size, density NP, PCM, concentration range 1–60 nm, 100–8960 kg/m3, 89.69–311 kJ/kg, 760 1520 0.02–20 wt% 60.72–338.6 respectively. output variable result indicates that applied enhanced satisfactory. correlation coefficient created 0.97. This shows ability