{"title":"Multi-objective optimization of supply air inlet structure for impinging jet ventilation system based on radial basis function neural network","authors":"Chen Wang, Ke Hu, Yin Liu","doi":"10.1016/j.csite.2024.105629","DOIUrl":null,"url":null,"abstract":"A multi-objective optimization of the supply air inlet structure for Impinging Jet Ventilation (IJV) was conducted based on the Radial Basis Function Neural Network (RBFNN) and using a genetic optimization algorithm. The Predicted Mean Vote at the occupant's ankle level (PMV<ce:inf loc=\"post\">0.1</ce:inf>) and the Energy Utilization Coefficient (<ce:italic>E</ce:italic><ce:inf loc=\"post\">t</ce:inf>) exhibited significant variability across different inlet structures, thus they were selected as optimization objectives. The predicted results showed substantial consistency with numerical simulations. Within the selected parameter range, the optimal PMV<ce:inf loc=\"post\">0.1</ce:inf> value was −0.17, and the optimal <ce:italic>E</ce:italic><ce:inf loc=\"post\">t</ce:inf> value was 3.57. Furthermore, by adjusting the weights of different optimization objectives, suitable structural parameters can be determined. It was also concluded that, for the given indoor ventilation conditions, the length of the supply air inlet structure should be shorter than its width to better enhance the PMV<ce:inf loc=\"post\">0.1</ce:inf> value in the areas surrounding occupants.","PeriodicalId":9658,"journal":{"name":"Case Studies in Thermal Engineering","volume":"13 1","pages":""},"PeriodicalIF":6.4000,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Case Studies in Thermal Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1016/j.csite.2024.105629","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"THERMODYNAMICS","Score":null,"Total":0}
Multi-objective optimization of supply air inlet structure for impinging jet ventilation system based on radial basis function neural network
A multi-objective optimization of the supply air inlet structure for Impinging Jet Ventilation (IJV) was conducted based on the Radial Basis Function Neural Network (RBFNN) and using a genetic optimization algorithm. The Predicted Mean Vote at the occupant's ankle level (PMV0.1) and the Energy Utilization Coefficient (Et) exhibited significant variability across different inlet structures, thus they were selected as optimization objectives. The predicted results showed substantial consistency with numerical simulations. Within the selected parameter range, the optimal PMV0.1 value was −0.17, and the optimal Et value was 3.57. Furthermore, by adjusting the weights of different optimization objectives, suitable structural parameters can be determined. It was also concluded that, for the given indoor ventilation conditions, the length of the supply air inlet structure should be shorter than its width to better enhance the PMV0.1 value in the areas surrounding occupants.
期刊介绍:
Case Studies in Thermal Engineering provides a forum for the rapid publication of short, structured Case Studies in Thermal Engineering and related Short Communications. It provides an essential compendium of case studies for researchers and practitioners in the field of thermal engineering and others who are interested in aspects of thermal engineering cases that could affect other engineering processes. The journal not only publishes new and novel case studies, but also provides a forum for the publication of high quality descriptions of classic thermal engineering problems. The scope of the journal includes case studies of thermal engineering problems in components, devices and systems using existing experimental and numerical techniques in the areas of mechanical, aerospace, chemical, medical, thermal management for electronics, heat exchangers, regeneration, solar thermal energy, thermal storage, building energy conservation, and power generation. Case studies of thermal problems in other areas will also be considered.