Supervised machine learning techniques for optimization of heat transfer rate of Cu-H2O nanofluid flow over a radial porous fin

IF 1.7 4区 材料科学 Q3 MATERIALS SCIENCE, MULTIDISCIPLINARY Multidiscipline Modeling in Materials and Structures Pub Date : 2023-05-29 DOI:10.1108/mmms-08-2022-0153
J. Raza, M. Raza, Tahir Mustaq, M. Qureshi
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引用次数: 2

Abstract

PurposeThe purpose of this paper is to study the thermal behavior of radial porous fin surrounded by water-base copper nanoparticles under the influence of radiation.Design/methodology/approachIn order to optimize the response variable, the authors perform sensitivity analysis with the aid of response surface methodology (RSM). Moreover, this study enlightens the applications of artificial neural networks (ANN) for predicting the temperature gradient. The governing modeled equations are firstly non-dimensionalized and then solved with the aid of Runge–Kutta fourth order together with the shooting method in order to guess the initial conditions.FindingsNumerical results are analyzed and presented in the form of tables and graphs. This study reveals that the temperature of the fin is decreasing as the wet porous parameter increases (m2) and the temperature for 10% concentration of nanoparticles are higher than 5 and 1%. Physical parameters involved in the study are analyzed and processed through RSM. It is come to know that sensitivity of temperature gradient to radiative parameter (Nr) and convective parameter (Nc) is positive and negative to dimensionless ambient temperature (θa). Furthermore, after ANN training it can be argued that the established model can efficiently be used to predict the temperature gradient over a radial porous fin for the copper-water nanofluid flow.Originality/valueTo the best of our knowledge, only a few attempts have been made to analyze the thermal behavior of radial porous fin surrounded by copper-based nanofluid under the influence of radiation and convection.
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优化Cu-H2O纳米流体在径向多孔翅片上流动传热速率的监督机器学习技术
目的研究水基铜纳米粒子包围的径向多孔翅片在辐射影响下的热行为。设计/方法/方法为了优化响应变量,作者借助响应面方法(RSM)进行灵敏度分析。此外,本研究对人工神经网络在温度梯度预测中的应用具有启示意义。首先对控制模型方程进行无量纲化,然后借助Runge–Kutta四阶和射击法进行求解,以猜测初始条件。数值结果以表格和图表的形式进行分析和呈现。该研究表明,翅片的温度随着湿多孔参数(m2)的增加而降低,并且10%浓度的纳米颗粒的温度高于5%和1%。通过RSM对研究中涉及的物理参数进行分析和处理。温度梯度对辐射参数(Nr)和对流参数(Nc)的敏感性对无量纲环境温度(θa)分别为正和负。此外,在ANN训练之后,可以认为所建立的模型可以有效地用于预测铜-水纳米流体流动的径向多孔翅片上的温度梯度。独创性/价值据我们所知,只有少数几次尝试分析铜基纳米流体包围的径向多孔翅片在辐射和对流影响下的热行为。
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来源期刊
CiteScore
3.70
自引率
5.00%
发文量
60
期刊介绍: Multidiscipline Modeling in Materials and Structures is published by Emerald Group Publishing Limited from 2010
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