[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120706-en":3,"doc-seo-120706-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120706,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Simulation and Machine Learning Investigation on Thermoregulation Performance of Phase Change Walls - Research report","Outdoor thermal conditions strongly affect indoor thermal comfort, motivating building-envelope solutions that regulate wall surface temperatures. The study evaluates phase change materials (PCMs) in phase change walls to enhance heat storage and reduce temperature variations, using COMSOL Multiphysics simulations with thermal comfort time as the key quantitative index. Brick walls filled with composite PCMs significantly extend comfort durations under specified ambient and heating conditions, and a BP neural network predicts indoor comfort time with low deviation.","sustainability   \nArticle  \nSimulation and Machine Learning Investigation on Thermoregulation Performance of Phase Change Walls  \nXin Xiao 1,2, *, Qian Hu 1, Huansong Jiao 1, Yunfeng Wang 2 and Ali Badiei 3, *  \nCitation: Xiao, X.; Hu, Q.; Jiao, H.; Wang, Y.; Badiei, A. Simulation and Machine Learning Investigation on Thermoregulation Performance of Phase Change Walls. Sustainability 2023, 15, 11365. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/su151411365](10.3390/su151411365)  \nAcademic Editor: Antonio Caggiano  \nReceived: 22 June 2023  \nRevised: 15 July 2023  \nAccepted: 17 July 2023  \nPublished: 21 July 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 College of Environmental Science and Engineering, Donghua University, Shanghai 201620, China; [2222366@mail.dhu.edu.cn](2222366@mail.dhu.edu.cn) (Q.H.); [2212195@mail.dhu.edu.cn](2212195@mail.dhu.edu.cn) (H.J.)  \n2 Yunnan Provincial Rural Energy Engineering Key Laboratory, Kunming 650550, China; [wangyf@ynnu.edu.cn](wangyf@ynnu.edu.cn)  \n3 School of Engineering, University of Central Lancashire, Preston PR1 2HE, UK  \n* Correspondence: [xin.xiao@dhu.edu.cn](xin.xiao@dhu.edu.cn) (X.X.); [abadiei@uclan.ac.uk](abadiei@uclan.ac.uk) (A.B.); Tel.: +86-(0)-1896-4749723 (X.X.); +44-(0)-1772-894499 (A.B.)  \nAbstract: 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, Na 2CO3 􀀁10H2O and Na2HPO4 􀀁12H2O with the ratio of 4:6)/5 wt.% BNand 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.  \nKeywords: phase change wall; radiative heating; numerical simulation; machine learning  \n1. Introduction  \nOutdoor climate conditions tend to influence the in","cbCait8PO1PnPnkv","https://ap.wps.com/l/cbCait8PO1PnPnkv","pdf",10342299,1,22,"English","en",105,"# Abstract\n# Introduction\n## Background and motivation\n## Related work on PCM walls","[{\"question\":\"How is thermoregulation performance of phase change walls quantitatively evaluated?\",\"answer\":\"The study uses the time duration during which the internal wall-side temperature stays within the thermal comfort range as the quantitative evaluation index.\"},{\"question\":\"What simulation tool and machine learning model are used in the study?\",\"answer\":\"COMSOL Multiphysics is used for numerical simulations, and a BP neural network is trained to predict indoor thermal comfort time.\"},{\"question\":\"What key inputs and output are used for the machine learning training?\",\"answer\":\"Heating surface temperature and ambient temperature are used as input variables, while the time duration of indoor thermal comfort is used as the output variable.\"}]","Simulation and Machine Learning Investigation on Thermoregulation Performance of Phase Change Walls - 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