Artificial neural network for melting enhancement of nano-enhanced PCM in triplex tubes

Phase change materials (PCMs) are becoming increasingly popular in thermal energy storage (TES) systems due to their ability to effectively regulate temperature, provide compact storage, and have a high latent heat capacity. They store excess thermal energy during charging cycles and release it as demand rises, effectively balancing energy fluctuations. PCMs are widely used in a variety of applications, including waste heat recovery, HVAC systems, solar collectors, electronic cooling, and residential heating. However, their low thermal conductivity affects melting efficiency and system performance. To solve this restriction, current research focuses on improving heat transmission through structural and material innovations such as fin-assisted enclosures and nanoparticle-doped PCMs. In the current investigation, paraffin wax served as the primary PCM, with nanoparticles added to improve heat conductivity and melting homogeneity. To predict and optimize the thermophysical behavior of nano-enhanced PCMs, we applied an Artificial Neural Network (ANN) model, a machine learning approach that learns from experimental and simulation data. Unlike traditional modelling methods, the ANN model can quickly assess the performance of various nano-enhanced PCMs by training on known experimental data and predicting the melting behavior of new samples. The model was very accurate (R 2 > 0.998) and had a mean square error of less than 1.5 × 10 −3 , indicating its reliability for rapid material assessment and performance prediction. The influence of V-shaped fins in a triplex-tube TES unit was investigated using numerical simulations utilizing the enthalpy-porosity approach. The findings revealed case 2 and case 3 gives the better melting performance, above 98% PCM is melted, where as 84.86% melting for case 5 gives the lowest melting rate with in 3600 s. These findings suggest a viable pathway for creating efficient TES devices for renewable energy and industrial applications.