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Heat Transfer Research
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ISSN Print: 1064-2285
ISSN Online: 2162-6561

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Heat Transfer Research

DOI: 10.1615/HeatTransRes.2018020080
pages 617-631

MODELING PHASE-CHANGE MATERIALS HEAT CAPACITY USING ARTIFICIAL NEURAL NETWORKS

Benoit Delcroix
École Polytechnique de Montréal, Department of Mechanical Engineering, 2500 Chemin de Polytechnique, Montréal, QC, H3T 1J4, Canada
M. Kummert
École Polytechnique de Montréal, Department of Mechanical Engineering, 2500 Chemin de Polytechnique, Montréal, QC, H3T 1J4, Canada
A. Daoud
Laboratoire des Technologies de l'Énergie, Institut de Recherche d'Hydro-Québec, 600 Avenue de la Montagne, Shawinigan, QC, G9N 7N5, Canada

ABSTRACT

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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