Pioneering machine learning techniques to estimate thermal conductivity of carbon-based phase change materials: A comprehensive modeling framework
This study presents a comprehensive data-driven framework to accurately estimate the thermal conductivity of nano-enhanced phase change materials (NEPCMs) using machine learning. A dataset of 482 samples, incorporating various nanoparticle types, concentrations, PCM types, and operating temperatures...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
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| Series: | Case Studies in Thermal Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214157X25009086 |
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