每年出版 18 期
ISSN 打印: 1064-2285
ISSN 在线: 2162-6561
Indexed in
MODELING PHASE-CHANGE MATERIALS HEAT CAPACITY USING ARTIFICIAL NEURAL NETWORKS
摘要
This article investigates the application of Artificial Neural Networks (ANNs) to model Phase-Change Materials (PCMs) heat capacity using data from Differential Scanning Calorimetry (DSC) tests and experimentations. Coefficients of determination of 0.99 and 0.66 are respectively obtained using two (DSC test) and four (experimentations) independent variables to simulate the dependent variable, i.e., PCM heat capacity. The independent variables include the PCM temperature and heat transfer characteristics such as the heating/cooling rate, heating/cooling duration, and the previous state (temperature and heat capacity). These results show the ability of ANNs for PCM modeling if meaningful independent variables are used.
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Urresti A., Campos-Celador A., Sala J.M., Dynamic neural networks to analyze the behavior of phase change materials embedded in building envelopes, Applied Thermal Engineering, 158, 2019. Crossref
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He Zhaoyu, Guo Weimin, Zhang Peng, Performance prediction, optimal design and operational control of thermal energy storage using artificial intelligence methods, Renewable and Sustainable Energy Reviews, 156, 2022. Crossref