Insight into the thermal transport by considering the modified Buongiorno model during the silicon oil-based hybrid nanofluid flow: probed by artificial intelligence

IF 1.9 3区 物理与天体物理 Q2 PHYSICS, MULTIDISCIPLINARY
Asad Ullah, Hongxing Yao, Farid Ullah, Haifa Alqahtani, Emad A. A. Ismail, Fuad A. Awwad, Abeer A. Shaaban
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Abstract

This work aims to analyze the impacts of the magnetic field, activation of energy, thermal radiation, thermophoresis, and Brownian effects on the hybrid nanofluid (HNF) (Ag++silicon oil) flow past a porous spinning disk. The pressure loss due to porosity is constituted by the Darcy–Forchheimer relation. The modified Buongiorno model is considered for simulating the flow field into a mathematical form. The modeled problem is further simplified with the new group of dimensionless variables and further transformed into a first-order system of equations. The reduced system is further analyzed with the Levenberg–Marquardt algorithm using a trained artificial neural network (ANN) with a tolerance, step size of 0.001, and 1,000 epochs. The state variables under the impacts of the pertinent parameters are assessed with graphs and tables. It has been observed that when the magnetic parameter increases, the velocity gradient of mono and hybrid nanofluids (NFs) decreases. As the input of the Darcy–Forchheimer parameter increases, the velocity profiles decrease. The result shows that as the thermophoresis parameter increases, temperature and concentration increase as well. When the activation energy parameter increases, the concentration profile becomes higher. For a deep insight into the analysis of the problem, a statistical approach for data fitting in the form of regression lines and error histograms for NF and HNF is presented. The regression lines show that 100% of the data is used in curve fitting, while the error histograms depict the minimal zero error 7.1e6 for the increasing values of Nt. Furthermore, the mean square error and performance validation for each varying parameter are presented. For validation, the present results are compared with the available literature in the form of a table, where the current results show great agreement with the existing one.
通过人工智能探测硅油基混合纳米流体流动过程中的热传输:考虑修改后的布昂奥尔诺模型
这项工作旨在分析磁场、能量活化、热辐射、热泳和布朗效应对流经多孔旋转盘的混合纳米流体(HNF)(银++硅油)的影响。多孔性导致的压力损失由达西-福克海默关系构成。为了以数学形式模拟流场,考虑了改进的 Buongiorno 模型。利用新的无量纲变量组进一步简化了模型问题,并将其转化为一阶方程系统。利用训练有素的人工神经网络(ANN),以公差、步长 0.001 和 1,000 个历元的 Levenberg-Marquardt 算法对简化后的系统进行进一步分析。通过图形和表格对相关参数影响下的状态变量进行了评估。据观察,当磁性参数增加时,单纳米流体和混合纳米流体(NFs)的速度梯度会减小。随着达西-福克海默参数输入的增加,速度曲线也随之减小。结果表明,随着热泳参数的增加,温度和浓度也随之增加。当活化能参数增加时,浓度曲线变高。为了深入分析问题,本文介绍了以回归线和误差直方图形式对 NF 和 HNF 进行数据拟合的统计方法。回归线显示,在曲线拟合中使用了 100% 的数据,而误差直方图显示,随着 Nt 值的增加,最小零误差为 -7.1e6。此外,还显示了每个变化参数的均方误差和性能验证。在验证方面,以表格的形式将当前结果与现有文献进行了比较,结果显示当前结果与现有文献非常一致。
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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来源期刊
Frontiers in Physics
Frontiers in Physics Mathematics-Mathematical Physics
CiteScore
4.50
自引率
6.50%
发文量
1215
审稿时长
12 weeks
期刊介绍: Frontiers in Physics publishes rigorously peer-reviewed research across the entire field, from experimental, to computational and theoretical physics. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics, engineers and the public worldwide.
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