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使用 Levenberg-Marquardt 人工神经网络进行智能计算，用于研究可拉伸旋转盘之间的碳纳米管-水。

Intelligent computing with Levenberg-Marquardt artificial neural network for Carbon nanotubes-water between stretchable rotating disks.

机构信息

Department of Mathematics, COMSATS University Islamabad, Attock Campus, Kamra Road, Attock, 43600, Pakistan.

Faculty of Engineering, Department of Industrial Machines and Equipments, "Lucian Blaga" University of Sibiu, 10 Victoriei Boulevard, Sibiu, Romania.

出版信息

Sci Rep. 2023 Mar 8;13(1):3901. doi: 10.1038/s41598-023-30936-x.

DOI:10.1038/s41598-023-30936-x
PMID:36890282
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9995332/
Abstract

Hybrid Nano fluid has emerged to be an important field of study due to its better thermal performance compared to other Nano fluids. The problem of carbon nanotubes rotating between two stretchable discs while suspended in water is investigated in this research. Due to numerous uses of this problem, such as metal mining, drawing plastic films, and cooling continuous filaments, this problem is essential to industry. Considerations here include suction/injection, heat radiation, and the Darcy-Forchheimer scheme with convective boundary conditions. The partial differential equations are reduced to ordinary differential equations by using appropriate transformation. To examine the approximate solution validation, training and testing procedures are interpreted and the performance is verified through error histogram and mean square error results. To describe the behavior of flow quantities, several tabular and graphical representations of a variety of physical characteristics of importance are presented and discussed in detail. The basic aim of this research is to examine the behaviour of carbon nanotubes (nanoparticles) between stretchable disks while considering the heat generation/absorption parameter by using the Levenberg-Marquardt technique of artificial neural network. Heat transfer rate is accelerated by a decrease in velocity and temperature and an increase in the nanoparticle volume fraction parameter which is a significant finding of the current study.

摘要

混合纳米流体因其比其他纳米流体更好的热性能而成为一个重要的研究领域。本研究调查了悬浮在水中的碳纳米管在两个可伸缩圆盘之间旋转的问题。由于这个问题有许多用途，如金属开采、拉伸塑料薄膜和冷却连续长丝，所以这个问题对工业很重要。这里考虑的因素包括抽吸/注入、热辐射以及具有对流边界条件的达西-福希海默方案。通过适当的变换，将偏微分方程简化为常微分方程。为了检验近似解的验证，解释了训练和测试过程，并通过误差直方图和均方根误差结果验证了性能。为了描述流动量的行为，呈现并详细讨论了多种重要物理特性的多种表格和图形表示。本研究的基本目的是通过使用人工神经网络的 Levenberg-Marquardt 技术考虑热生成/吸收参数来研究碳纳米管（纳米粒子）在可伸缩盘之间的行为。通过降低速度和温度以及增加纳米粒子体积分数参数来加速传热速率，这是当前研究的一个重要发现。

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