利用 Levenberg-Marquardt 人工神经网络分析静态和动态表面上具有随温度变化的热物理特性的三混合麦克斯韦纳米流体流动

IF 1.7 4区工程技术 Q3 MECHANICS

Numerical Heat Transfer Part B-Fundamentals Pub Date : 2024-08-05 DOI:10.1080/10407790.2024.2386582

Faisal, Abdul Rauf

引用次数: 0

摘要

本研究利用 Levenberg-Marquardt 人工神经网络（LM-ANN）模型，将计算神经科学应用于流动、热量和质量传输特性的分析。这种新颖的神经网络模型可用于分析流动、热量和质量传输特性。

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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Analysis of the tri-hybrid Maxwell nanofluid flow with temperature-dependent thermophysical characteristics on static and dynamic surfaces employing Levenberg-Marquardt artificial neural networks

Using the Levenberg-Marquardt Artificial Neural Network (LM-ANN) model, this study applies computational neuroscience to the analysis of flow, heat, and mass transport characteristics. The novel ch...

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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.