Numerical Simulation and ANN Prediction of Nano-Encapsulated PCM Slurry in a Microchannel: A Thermodynamic Analysis

Abstract

Thegrowingdemand forpreciseand efficientthermalmanagement inmicrofluidic heatexchangesystemshas ledtoincreasing interest in nano-encapsulated phase change materials (NEPCMs) for enhanced heat transfer and thermal energy storage. This study performs a comprehensive numerical simulation and machine learning-based prediction of the thermo-hydrodynamic behavior of NEPCM slurries in microchannels with secondary flow passages. The research aims to quantify the influence of microchannel geometry, flow conditions (Reynolds number: 100–200), and NEPCM concentration (0–10%) on heat transfer and pressure drop characteristics. Energy and entropy analyses are conducted by applying the first and second laws of thermodynamics to assess system efficiency. Furthermore, an artificial neural network (ANN) model is trained to predict the Nusselt number and performance evaluation criterion (PEC) based on input parameters with high accuracy. The simulation results indicate that incorporating NEPCMs enhances heat transfer performance, increasing the average Nusselt number by up to 50% compared to a simple microchannel. However, this improvement comes at the cost of higher pressure drop, with the friction factor showing a variation of up to 100% across different configurations. Entropy generation analysis reveals that thermal entropy generation dominates at lower Reynolds numbers, whereas frictional entropy generation becomes significant at higher Reynolds numbers. The ANN model achieves an R2 value of 0.98, with a prediction error of less than 1.5%, demonstrating its effectiveness. These findings provide quantitative insights for optimizing microchannel-based thermal management systems, balancing heat transfer enhancement, pressure drop, and entropy generation for improved performance in microfluidic applications