Work

Prediction on effective thermal conductivity of composite phase change materials from mesoscale structure

Year: 2025

Type: article

Abstract: This study proposes a numerical approach combined Quartet Structure Generation Set (QSGS) method with machine learning to investigate the effective thermal conductivity of composite phase change materials (CPCM). The QSGS method is utilized to construct the mesoscale structure of the CPCM, enabling the determination of the effective thermal conductivity (k eff ) through finite element simulations . The generated structures reveal the formation of three-dimensional thermal conduction network, influenced by key structural parameters such as porosity, solid diameter, and core growth probability. Machine learning-based predictive models are developed to estimate k eff and are compared against finite element simulation results. The results showed that a 10 % increase in porosity decreased the k eff by about 8.45 %. The k eff of CPCM increases with increasing temperature. The k eff can be increased by increasing the solid diameter, and the k eff decreases with the decrease of C dd . Among the machine learning models tested, the extreme gradient boosting model demonstrated superior performance, achieving a prediction accuracy with a correlation coefficient of 96 % relative to finite element results . These findings provide valuable insights into the thermal conductivity behavior and heat transfer mechanisms in composite phase change materials .

Source: Materials Today Communications

DOI: 10.1016/j.mtcomm.2025.112555

Authors: Hui Wang, Changnv Zeng, Xiaofei Qin

Institution: Henan University of Technology

Language: English

Topic: Phase Change Materials Research

Subfield: Mechanical Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,830

Funder: Henan University of Technology