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• W4315750466 endingPage "104662" @default.
• W4315750466 startingPage "104662" @default.
• W4315750466 abstract "In this modeling project, employing various machine learning methods, the thermophysical properties of phase change material (PCM) containing three nanoparticles were predicted. PCM with Multi-Walled Carbon Nanotube (MWCNT), Graphene Nanoplatelets (GNP), and nano-graphite (NG) were considered as nano-enhanced PCM, and their experimental data were extracted from the literature. The data consisted of thermal conductivity and latent heat of 100 thermal cycles measured for 1, 2, and 3 wt.% of nanoparticles in the myristic acid (MA) as PCM. This research uses supervised algorithms such as k-nearest neighbors (KNN), Automatic relevance determination (ARD), and least absolute shrinkage and selection operator (LASSO) for regression, creating formulas and making models. This research using the ARD regression to find the relationship between solid-state and liquid-state for three materials (GNPs/MA, MWCNTs/MA, and NG/MA) with the R-Squared value of 0.999 for all materials. The MSE of the ARD algorithms for the materials respectively is 1 × 10−9, 9 × 10−10, and 6 × 10−10. Using the MA, which is the primary material, creates the polynomial regression for GNPs/MA, MWCNTs/MA, and NG/MA, and the R-Squared values are 0.981, 0.984, and 0.981 and the MSE, respectively is 0.000159351, 0.000016945, and 0.000022425. The KNN algorithm is used to make the model for this subject, and the R-Squared values are 0.985, 0.988, and 0.988. The LASSO regression is used to make the linear regression for the relationship between melting-state and freezing-state, and the R-Squared value is 0.999 for all materials. The MSE of the LASSO method for these materials of this part respectively is 0.000176203, 0.000000545, and 0.000005035." @default.
• W4315750466 created "2023-01-13" @default.
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• W4315750466 date "2023-07-01" @default.
• W4315750466 modified "2023-10-15" @default.
• W4315750466 title "Thermophysical properties prediction of carbon-based nano-enhanced phase change material's using various machine learning methods" @default.
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• W4315750466 doi "https://doi.org/10.1016/j.jtice.2022.104662" @default.
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