Numerical simulation, ANN training and predictive analysis of phase change material with 3D printing lattice structures

Abstract

This study simulated and analysed the performance of phase change material with three lattice structures, i.e., simple cubic, body-centered cubic, and face-centered cubic, at various porosities and heat fluxes. Three parameters are analysed, including the maximum temperature of the heated wall, melting and solidifying times of PCM. The findings suggest that as the porosity rises, the maximum temperature decreases, while the increase in heat flux leads to a higher maximum temperature. Both the melting and solidifying times extend as porosity increases. An artificial neural network trained by the Levenberg-Marquardt algorithm is used to predict these three parameters. The lattice structure type, porosity, and heat flux are established as the input parameters for the network. The ANN predictions demonstrated outstanding performance in estimating these parameters with a minimum MSE of 0.00015 and a maximum R of 0.99899. The trained ANN is used to predict the crucial parameters of PCM with 82% SC, BCC, and FCC lattice structures. An excellent agreement is observed between the ANN predicted results and the simulation outcomes with a maximum relative error of 9.43%.

Keywords

• Numerical simulation
• Phase change material
• 3D printing
• Lattice structure
• Artificial neural network