A machine learning approach for entropy due to natural convection flow of a nanofluid under the uniform inclined magnetic field

IF 1.7 4区工程技术 Q3 MECHANICS

Numerical Heat Transfer Part B-Fundamentals Pub Date : 2024-04-15 DOI:10.1080/10407790.2024.2338912

B. Pekmen Geridonmez, H. F. Oztop

引用次数: 0

Abstract

In this study, entropy generation of natural convection flow of Cu-water nanofluid under the influence of a uniform inclined magnetic field (MF) is firstly modeled by a machine learning approach us...

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均匀倾斜磁场下纳米流体自然对流熵的机器学习方法

本研究首先利用机器学习方法对均匀倾斜磁场（MF）影响下铜-水纳米流体自然对流的熵生成进行了建模。

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.