旋转框架中水基 CNT 的 MHD 达西-福克海默流的数值处理，以及同质-异质反应：人工神经网络方法

IF 1.7 4区工程技术 Q3 MECHANICS

Numerical Heat Transfer Part B-Fundamentals Pub Date : 2024-07-04 DOI:10.1080/10407790.2024.2373382

K. Senthilvadivu, S. Eswaramoorthi, K. Loganathan, H. Thameem Basha

引用次数: 0

摘要

许多科学领域都在很大程度上依赖于旋转流，如喷气发动机设计、地球物理流、涡轮机工程、吸尘器设计等。之前关于旋转流的研究主要集中在对旋转流的研究上。

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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Numerical tackling for MHD Darcy-Forchheimer flow of water-based CNTs in a rotating frame with homogeneous-heterogeneous reactions: An artificial neural network approach

Numerous scientific fields rely heavily on rotating flows, such as jet engine design, geophysical flows, turbine engineering, vacuum cleaner design, and many others. Prior research on rotational fl...

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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.