Novel artificial neural network approach for hybrid nanofluid flow over nonlinear permeable stretching sheets with Thomson and Troian boundary conditions

IF 2.6 3区 工程技术 Q2 ENGINEERING, MECHANICAL International Journal of Heat and Fluid Flow Pub Date : 2025-03-01 Epub Date: 2024-12-27 DOI:10.1016/j.ijheatfluidflow.2024.109721
Shazia Habib , Zeeshan Khan , Esraa N. Thabet , A.M. Abd-Alla , S.H. Elhag
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Abstract

The study investigates the flow of a hybrid nanofluid over a non-linear, permeable stretched sheet under Thomson and Troian boundary conditions, while also considering the Darcy-Forchheimer relationship. We employ the Cattaneo-Christov heat flux model and novel artificial neural networks for the first time. This paper describes a new way to use artificial neural networks to add carbon nanotubes to hybrid nanofluids with Thomson and Troian boundary conditions. This creates induced MHD. The MSE ranges from 10-08 to 10-09. The AE range for all the cases lies around 10-03 to 10-07. The value of mu is around 10-08, while gradient ranges from 10-07 to 10-08. This shows the high accuracy and precision of the proposed scheme. This research highlights the variation of different parameters with velocity, temperature and concentration. As the solid volume fraction rises, fluid velocity diminishes and temperature rises. Nanofluids exhibit enhancement with elevated inertial coefficient and Eckert number values. Increased inertial coefficient and Eckert number values correspond to rising temperatures. Concentration diminishes with rising solid volume percentage; yet, elevated activation energy results in enhanced concentration dispersion. It proves superior thermal conductivity and heat transmission capabilities, with future studies investigating the additional factors. Potential areas for further investigation include the study of other nanoparticles and different hybrid nanofluids and the investigation of real engineering challenges associated to heat and mass transfer in porous media. A graphic comparison between simple and hybrid nanofluids is presented. It is shown that the solid volume fraction improves the temperature distribution while decreasing the velocity profile. Furthermore, hybrid nanofluids perform better in heat transfer and have higher thermal conductivity than simple nanofluids.
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具有Thomson和Troian边界条件的非线性可渗透拉伸片上混合纳米流体流动的新人工神经网络方法
在Thomson和Troian边界条件下,研究了混合纳米流体在非线性、可渗透的拉伸薄片上的流动,同时也考虑了Darcy-Forchheimer关系。我们首次采用了Cattaneo-Christov热流模型和新的人工神经网络。本文提出了一种利用人工神经网络在混合纳米流体中加入碳纳米管的新方法,该方法具有Thomson和Troian边界条件。这就产生了诱导MHD。MSE取值范围是10-08 ~ 10-09。所有病例的AE范围在10-03至10-07之间。mu值在10-08左右,梯度在10-07 ~ 10-08之间。这表明该方案具有较高的准确度和精密度。本研究重点研究了不同参数随速度、温度和浓度的变化。随着固体体积分数的增加,流体速度减小,温度升高。纳米流体随惯性系数和埃克特数值的升高而增强。惯性系数和埃克特数值的增加与温度的升高相对应。浓度随固体体积百分比的增大而减小;然而,活化能的升高导致浓度分散的增强。它证明了优越的导热性和传热能力,未来的研究将调查其他因素。进一步研究的潜在领域包括研究其他纳米颗粒和不同的混合纳米流体,以及研究与多孔介质中传热传质相关的实际工程挑战。给出了简单纳米流体和混合纳米流体的图形比较。结果表明,固体体积分数改善了温度分布,降低了速度分布。此外,混合纳米流体的传热性能和导热性能优于单纯纳米流体。
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来源期刊
International Journal of Heat and Fluid Flow
International Journal of Heat and Fluid Flow 工程技术-工程:机械
CiteScore
5.00
自引率
7.70%
发文量
131
审稿时长
33 days
期刊介绍: The International Journal of Heat and Fluid Flow welcomes high-quality original contributions on experimental, computational, and physical aspects of convective heat transfer and fluid dynamics relevant to engineering or the environment, including multiphase and microscale flows. Papers reporting the application of these disciplines to design and development, with emphasis on new technological fields, are also welcomed. Some of these new fields include microscale electronic and mechanical systems; medical and biological systems; and thermal and flow control in both the internal and external environment.
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