Prediction of heat exchanger efficiency using laminar heat transfer in swirling flow of radiated graphene oxide with nano fluid additives using machine learning technique

IF 1.7 4区工程技术 Q3 MECHANICS

Numerical Heat Transfer Part B-Fundamentals Pub Date : 2024-07-17 DOI:10.1080/10407790.2024.2377792

Shalini M. Patil, Abilash Radhakrishnan, Sanjay R. Pawar, Dani Jermisha Railis, Praveen Barmavatu

引用次数: 0

Abstract

Nanotechnology has recently led to new possibilities for enhancing heat transfer in heat exchangers. The remarkable thermal characteristics of graphene oxide (GO)-based nanofluids with nanoscale ad...

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利用机器学习技术预测辐射氧化石墨烯与纳米流体添加剂漩涡流中层流传热的热交换器效率

最近，纳米技术为增强热交换器的传热带来了新的可能性。以氧化石墨烯（GO）为基础的纳米流体具有显著的热特性，其纳米尺度的吸附剂可在热交换器中产生热量。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Numerical Heat Transfer Part B-Fundamentals 工程技术-力学

CiteScore

2.40

自引率

0.00%

发文量

审稿时长

2.8 months

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.