Latent thermal energy storage, employing phase-change materials, has been traditionally
researched in several areas such solar energy, refrigeration, and electronic cooling, but less …

Background In this modeling project, employing various machine learning methods, the
thermophysical properties of phase change material (PCM) containing three nanoparticles …

Thermal conductivity is one of the crucial properties of nano enhanced phase change
materials (NEPCM). Then, in this study three different machine learning methods namely …

Nanofluids are often used as heat transfer fluids due to their good thermal and flow
properties. Nanofluids are widely used in energy systems such as solar collectors, heat …

This study systematically optimizes the synthesis parameters for nano‐enhanced phase‐
change materials (NEPCMs) based on paraffin wax and copper oxide. The objective is to …

Background In this research, five machine learning methods are employed to create the
formula and make the model for communication between the melting and solidification …

In cold regions, a better understanding of soil thermal conductivity is necessary for a variety
of earthworks and engineering applications such as ground heat exchanger piles and …

The usage of phase change materials (PCMs) in thermal energy storage (TES) systems has
been a promising approach in recent years. An accurate estimation of their thermophysical …

Progress in artificial intelligence and machine learning has significantly improved the
capability to accurately predict the properties of nano-enhanced phase change materials …

This work examines the thermal behavior and performance of a porous enclosure filled with
Nano-Encapsulated Phase Change Materials (NEPCMs) using an artificial intelligence …