Designing artificial neural network of nanoparticle diameter and solid–fluid interfacial layer on single-walled carbon nanotubes/ethylene glycol nanofluid flow on thin slendering needles,International Journal for Numerical Methods in Fluids

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Designing artificial neural network of nanoparticle diameter and solid–fluid interfacial layer on single-walled carbon nanotubes/ethylene glycol nanofluid flow on thin slendering needles
International Journal for Numerical Methods in Fluids ( IF 1.7 ) Pub Date : 2021-08-12 , DOI: 10.1002/fld.5038
Anum Shafiq ₁ , Andaç Batur Çolak ₂ , Tabassum Naz Sindhu ₃

Affiliation

In this study, an artificial neural network (ANN) has been developed to predict the boundary layer flow of a single-walled carbon nanotubes nanofluid toward three different nonlinear thin isothermal needles of paraboloid, cone, and cylinder shapes with convective boundary conditions. Different effects of particle diameter and solid–fluid interface coating have been taken into account in the thermal conductivity model of nanofluid in which ethylene glycol has been used as the base fluid. Single and dual phase approach is used to establish the management model under the phenomenon of zero heat and mass flux. A dataset has been developed for different scenarios of the fluid model by changing the relevant parameters with the Runge–Kutta based shooting technique. Two different ANN models have been developed to predict Nusselt number and skin friction coefficient (SFC) values. The values obtained from ANN models have been compared with the numerical data, which are the target values. In addition, mean square error and R values have also been examined in order to analyze the prediction performance of ANN models more comprehensively. The calculated R values for Nusselt number and SFC were obtained as 0.9999. The results obtained showed that ANN can predict Nusselt number and SFC values with high accuracy.

中文翻译：

在细细针上设计单壁碳纳米管/乙二醇纳米流体流动的纳米颗粒直径和固液界面层的人工神经网络

在这项研究中，已经开发了人工神经网络 (ANN) 来预测单壁碳纳米管纳米流体向具有对流边界条件的抛物面、圆锥和圆柱形状的三种不同非线性薄等温针的边界层流动。在以乙二醇为基液的纳米流体导热模型中，考虑了粒径和固液界面涂层的不同影响。采用单相和双相方法建立零热量和质量通量现象下的管理模型。通过使用基于 Runge-Kutta 的射击技术更改相关参数，为流体模型的不同场景开发了一个数据集。已经开发了两种不同的人工神经网络模型来预测努塞尔数和皮肤摩擦系数 (SFC) 值。从 ANN 模型获得的值已经与数值数据进行了比较，这是目标值。此外，均方误差和为了更全面地分析 ANN 模型的预测性能，还检查了R值。计算出的努塞尔数和 SFC 的R值为 0.9999。获得的结果表明，人工神经网络可以高精度地预测努塞尔数和 SFC 值。

更新日期：2021-08-12

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