RT Journal Article ID 6a30e4ed315ec206 A1 Delcroix, Benoit A1 Kummert, M. A1 Daoud, A. T1 MODELING PHASE-CHANGE MATERIALS HEAT CAPACITY USING ARTIFICIAL NEURAL NETWORKS JF Heat Transfer Research JO HTR YR 2018 FD 2018-04-24 VO 49 IS 7 SP 617 OP 631 K1 phase-change material (PCM) K1 heat capacity K1 artificial neural network (ANN) K1 experimental validation AB 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. PB Begell House LK https://www.dl.begellhouse.com/journals/46784ef93dddff27,43e2399435f7aac5,6a30e4ed315ec206.html