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

IF 1.7 4区工程技术 Q3 MECHANICS

Numerical Heat Transfer Part B-Fundamentals Pub Date : 2024-05-07 DOI:10.1080/10407790.2024.2346922

Faisal, Abdul Rauf, Fiaz Ahmad, Nehad Ali Shah

引用次数: 0

摘要

本研究利用人工神经网络（ANN），采用莱文伯格-马（Levenberg-Ma...

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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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...

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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.