Thermal conductivity prediction of nano enhanced phase change materials: A comparative machine learning approach

• Main Author: Jaliliantabar, F.
• Format: Article
• Language: English; English
• Published: Elsevier Ltd 2022
• Subjects: TJ Mechanical engineering and machinery
• Online Access: http://umpir.ump.edu.my/id/eprint/33890/1/Thermal%20conductivity%20prediction%20of%20nano%20enhanced%20phase%20change%20materials.pdf; http://umpir.ump.edu.my/id/eprint/33890/2/Thermal%20conductivity%20prediction%20of%20nano%20enhanced%20phase%20change%20materials_FULL.pdf; http://umpir.ump.edu.my/id/eprint/33890/; https://doi.org/10.1016/j.est.2021.103633; https://doi.org/10.1016/j.est.2021.103633

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 MARS (Multivariate Adaptive Regression Spline), CART (Classification and Regression Tree) and ANN (Artificial Neural Network) is...