Simulation and Machine Learning Investigation on Thermoregulation Performance of Phase Change Walls

Xiao, Xin, Hu, Qian, Jiao, Huansong, Wang, Yunfeng and Badiei, Ali orcid iconORCID: 0000-0002-2103-2955 (2023) Simulation and Machine Learning Investigation on Thermoregulation Performance of Phase Change Walls. Sustainability, 15 (14).

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The outdoor thermal environment can be regarded as a significant factor influencing indoor thermal conditions. The application of phase change materials (PCMs) to the building envelope has the potential to improve the heat storage performance of building walls and, therefore, effectively regulate the temperature variations of the inner surfaces of walls. COMSOL Multiphysics software was adopted firstly to perform the simulations on the thermoregulation performance of phase change wall; the time duration of the temperature at the internal side maintained within the thermal comfort range was used as a quantitative evaluation index of the thermoregulation effects. It was revealed from the simulation results that the time durations of thermal comfort were extended to 5021 s and 4102 s, respectively, when the brick walls were filled with two types of composite PCMs, namely eutectic hydrate (EHS, Na2CO3·10H2O and Na2HPO4·12H2O with the ratio of 4∶6)/5 wt.% BN and EHS/5 wt.% BN/7.5 wt.% expanded graphite (EG), under the conditions of 18 °C ambient temperature and 60 °C heating temperature at the charging stage. Both of them were longer than 3011 s, which corresponds to a pure brick wall. EHS/5 wt.% BN/7.5 wt.% EG exhibited better leakage prevention performance and, therefore, was a candidate for actual application, in comparison with EHS/5 wt.% BN. Then, a machine learning training process focused on the temperature control effects of phase change wall was carried out using a BP neural network, where the heating surface and ambient temperature were used as input variables and the time duration of indoor thermal comfort was the output variable. Finally, the learning deviation between the raw data and the results obtained from machine learning was within 5%, indicating that machine learning can accurately predict the temperature control effects of the phase change wall. The results of the simulations and machine learning can provide information and guidance for the advantages and potentials of PCMs of hydrate salts when being applied to the building envelope. In addition, the accurate prediction of machine learning demonstrated its application prospects to the research of phase change walls.

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