{"title":"Thermal concentrating efficiency enhanced for multilayer circular thermal concentrators with gradient structures","authors":"","doi":"10.1016/j.ijheatmasstransfer.2024.126166","DOIUrl":null,"url":null,"abstract":"<div><p>The thermal concentrating efficiency of a thermal concentrator is determined by the ratio of its interior to exterior temperature gradients, serving as a crucial indicator influenced by the interaction of geometrical and thermal conductivity parameters. Finding simpler and more effective ways to improve thermal concentrating efficiency has been a key concern in this field. In our study, we present a method to enhance the concentrating efficiency of an isotropic multilayer circular thermal concentrator by introducing gradient-distributed thermal conductivities or layer thicknesses within the multilayer circular structure. Our goal is to identify the optimal structural setup parameters for achieving enhanced thermal concentrating efficiency using an optimization approach that combines stepwise refinement search with machine-learning predictions. Initial investigations explore the impacts of different gradient schemes on thermal concentration performance. The gradient distribution function with high thermal concentrating efficiency is established through the stepwise refinement search strategy and the machine-learning model. Subsequently, a detailed search process is carried out in small increments, followed by finite element simulations to validate the thermal concentrating efficiency and ascertain the optimal design parameters of the thermal concentrator. Our findings reveal that the optimally designed gradient thermal concentrator showcases an 8.56 % increase in thermal concentrating efficiency compared to a single-layer structure without gradients. Moreover, applying the gradient function to the outer and inner rings elucidates the inherent influence of the inner and outer layered ring structures on thermal concentrating efficiency. The optimization methodology, combining stepwise refinement search and machine-learning predictions, succeeds in improving the efficiency with easy, fast and efficient operation. This approach can be extended to advance the development of various other thermal metastructured devices.</p></div>","PeriodicalId":336,"journal":{"name":"International Journal of Heat and Mass Transfer","volume":null,"pages":null},"PeriodicalIF":5.0000,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Heat and Mass Transfer","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0017931024009967","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
引用次数: 0
Abstract
The thermal concentrating efficiency of a thermal concentrator is determined by the ratio of its interior to exterior temperature gradients, serving as a crucial indicator influenced by the interaction of geometrical and thermal conductivity parameters. Finding simpler and more effective ways to improve thermal concentrating efficiency has been a key concern in this field. In our study, we present a method to enhance the concentrating efficiency of an isotropic multilayer circular thermal concentrator by introducing gradient-distributed thermal conductivities or layer thicknesses within the multilayer circular structure. Our goal is to identify the optimal structural setup parameters for achieving enhanced thermal concentrating efficiency using an optimization approach that combines stepwise refinement search with machine-learning predictions. Initial investigations explore the impacts of different gradient schemes on thermal concentration performance. The gradient distribution function with high thermal concentrating efficiency is established through the stepwise refinement search strategy and the machine-learning model. Subsequently, a detailed search process is carried out in small increments, followed by finite element simulations to validate the thermal concentrating efficiency and ascertain the optimal design parameters of the thermal concentrator. Our findings reveal that the optimally designed gradient thermal concentrator showcases an 8.56 % increase in thermal concentrating efficiency compared to a single-layer structure without gradients. Moreover, applying the gradient function to the outer and inner rings elucidates the inherent influence of the inner and outer layered ring structures on thermal concentrating efficiency. The optimization methodology, combining stepwise refinement search and machine-learning predictions, succeeds in improving the efficiency with easy, fast and efficient operation. This approach can be extended to advance the development of various other thermal metastructured devices.
期刊介绍:
International Journal of Heat and Mass Transfer is the vehicle for the exchange of basic ideas in heat and mass transfer between research workers and engineers throughout the world. It focuses on both analytical and experimental research, with an emphasis on contributions which increase the basic understanding of transfer processes and their application to engineering problems.
Topics include:
-New methods of measuring and/or correlating transport-property data
-Energy engineering
-Environmental applications of heat and/or mass transfer