{"title":"利用人工神经网络和 Levenberg-Marquardt 优化技术探索纳米层形态对磁化三混合纳米流体流动的影响","authors":"Faisal, Abdul Rauf, Fiaz Ahmad, Nehad Ali Shah","doi":"10.1080/10407790.2024.2346922","DOIUrl":null,"url":null,"abstract":"This study investigates the impact of morphological nanolayers on the thermal conductivity of magnetized tri-hybrid nanofluid flow using artificial neural networks (ANNs) employing the Levenberg–Ma...","PeriodicalId":49732,"journal":{"name":"Numerical Heat Transfer Part B-Fundamentals","volume":null,"pages":null},"PeriodicalIF":1.7000,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Exploring the influence of nanolayer morphology on magnetized tri-hybrid nanofluid flow using artificial neural networks and Levenberg–Marquardt optimization\",\"authors\":\"Faisal, Abdul Rauf, Fiaz Ahmad, Nehad Ali Shah\",\"doi\":\"10.1080/10407790.2024.2346922\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study investigates the impact of morphological nanolayers on the thermal conductivity of magnetized tri-hybrid nanofluid flow using artificial neural networks (ANNs) employing the Levenberg–Ma...\",\"PeriodicalId\":49732,\"journal\":{\"name\":\"Numerical Heat Transfer Part B-Fundamentals\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2024-05-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Numerical Heat Transfer Part B-Fundamentals\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1080/10407790.2024.2346922\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"MECHANICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Numerical Heat Transfer Part B-Fundamentals","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/10407790.2024.2346922","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MECHANICS","Score":null,"Total":0}
Exploring the influence of nanolayer morphology on magnetized tri-hybrid nanofluid flow using artificial neural networks and Levenberg–Marquardt optimization
This study investigates the impact of morphological nanolayers on the thermal conductivity of magnetized tri-hybrid nanofluid flow using artificial neural networks (ANNs) employing the Levenberg–Ma...
期刊介绍：
Published 12 times per year, Numerical Heat Transfer, Part B: Fundamentals addresses all aspects of the methodology for the numerical solution of problems in heat and mass transfer as well as fluid flow. The journal’s scope also encompasses modeling of complex physical phenomena that serves as a foundation for attaining numerical solutions, and includes numerical or experimental results that support methodology development.
All submitted manuscripts are subject to initial appraisal by the Editor, and, if found suitable for further consideration, to peer review by independent, anonymous expert referees. The Editor reserves the right to reject without peer review any papers deemed unsuitable.
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