2022
DOI: 10.1016/j.applthermaleng.2022.119005
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Electrohydrodynamic enhancement of phase change material melting in cylindrical annuli under microgravity

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Cited by 15 publications
(1 citation statement)
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“…The model demonstrated significantly fewer mean absolute errors (MAEs) than the regression model. In addition, the machine learning models demonstrated a notable level of precision when applied to infrequent geometric shapes and specific operational circumstances, such as a triangle pin shape or the utilization of R134A as a working fluid [16][17][18][19][20]. The findings of this study demonstrate the higher predictive accuracy of machine learning models compared to traditional correlation methods in assessing the thermal performance of tiny-pin fin HSs across various geometry and operating situations.…”
Section: Introductionmentioning
confidence: 77%
“…The model demonstrated significantly fewer mean absolute errors (MAEs) than the regression model. In addition, the machine learning models demonstrated a notable level of precision when applied to infrequent geometric shapes and specific operational circumstances, such as a triangle pin shape or the utilization of R134A as a working fluid [16][17][18][19][20]. The findings of this study demonstrate the higher predictive accuracy of machine learning models compared to traditional correlation methods in assessing the thermal performance of tiny-pin fin HSs across various geometry and operating situations.…”
Section: Introductionmentioning
confidence: 77%