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Enhanced heat transfer in novel star-shaped enclosure with hybrid nanofluids: A neural network-assisted study
Case Studies in Thermal Engineering ( IF 6.4 ) Pub Date : 2024-09-01 , DOI: 10.1016/j.csite.2024.105065
Qurrat Ul Ain , Imtiaz Ali Shah , Saleh Mousa Alzahrani

The current research is a numerical investigation of the thermo-fluidic transport trend within a new star-shaped enclosure filled with (Multiwall Carbon Nanotubes) suspended hybrid nanoparticles in a base fluid (water), induced by a horizontal magnetizing field. A rectangular rod inside the cavity positioned at center is kept at a high temperature, as the low temperature is chosen for outer corrugated boundary. Formulation yelled in mathematical principles, along with the boundary conditions and some physical assumptions, are formulated as dimensionless PDEs. The finite element method (FEM) assisted by “COMSOL” software, employed for numerical simulations, and the PARDISO solver is used for solving nonlinear system of equations. Also, an artificial neural network (ANN) model is assembled to analyze the effect of these parameters on thermal and flow transport properties of Hybrid nanofluid. The training of this ANN model is done by a dataset generated from numerical simulations, suggesting predictions regarding heat transport under varying parameters. This approach does not only reduce the effort, but also reduces computing time when exploring the thermal behaviors across various datasets. The visualization of velocity and temperature fields through streamlines and isotherms, along with the evaluation of the Nusselt number, illustrate the enhanced heat transfer facilitated by the incorporation of nanoparticles. The results show that increased magnetic field strength reduces fluid velocity due to the generated Lorentz force, which counteracts convective flow. Percentage analysis indicates that hybrid nanofluids () significantly improve thermal distribution compared to conventional fluids. This study demonstrates the effectiveness of ANN models in forecasting the influence of diverse factors on the flow and heat transport characteristics of hybrid nanofluids. These insights are essential to the design and optimization of heat transfer systems.

中文翻译:


混合纳米流体新型星形外壳中的增强传热:神经网络辅助研究



目前的研究是对一个新的星形外壳内的热流体传输趋势进行数值研究,该外壳填充有(多壁碳纳米管)悬浮在基础流体(水)中的混合纳米颗粒,由水平磁化场感应。位于中心的腔体内的矩形杆保持在高温,因为外部波纹边界选择低温。数学原理中的公式,连同边界条件和一些物理假设,被公式化为无量纲偏微分方程。采用“COMSOL”软件辅助的有限元法(FEM)进行数值模拟,采用PARDISO求解器求解非线性方程组。此外,还组装了人工神经网络(ANN)模型来分析这些参数对混合纳米流体的热和流动传输特性的影响。该 ANN 模型的训练是通过数值模拟生成的数据集完成的,该数据集提出了有关不同参数下热传输的预测。这种方法不仅减少了工作量,而且还减少了探索各种数据集的热行为时的计算时间。通过流线和等温线对速度场和温度场的可视化,以及对努塞尔数的评估,说明了纳米粒子的掺入促进了传热的增强。结果表明,磁场强度的增加会由于产生洛伦兹力而降低流体速度,从而抵消对流。百分比分析表明,与传统流体相比,混合纳米流体 () 显着改善了热分布。 本研究证明了 ANN 模型在预测多种因素对混合纳米流体的流动和热传输特性的影响方面的有效性。这些见解对于传热系统的设计和优化至关重要。
更新日期:2024-09-01
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