Das Prosenjit, Mamun Mohammad Arif Hasan
Department of Mechanical Engineering, Bangladesh University of Engineering and Technology, Dhaka 1000, Bangladesh.
Heliyon. 2024 Sep 22;10(19):e38303. doi: 10.1016/j.heliyon.2024.e38303. eCollection 2024 Oct 15.
This study presents a numerical analysis of magnetohydrodynamic (MHD) mixed convection in a semicircular enclosure containing a rotating inner cylinder and filled with nanofluids and hybrid nanofluids. The investigation explores the effects of AlO-TiO-SWCNT-water hybrid nanofluids with varying nanoparticle compositions, as well as AlO-water, TiO-water, and SWCNT-water nanofluids. The analysis includes the development of an artificial neural network (ANN) model to predict outcomes, achieving 97.34 % accuracy in training and 97.41 % in testing for the average Nusselt number. The study examines the impact of Reynolds number (Re), Richardson number (Ri), Hartmann number (), cylinder rotation speed (Ω), cylinder size, and nanoparticle volume fraction (φ) on heat transfer and fluid flow. Key findings include a 6.98 % increase in heat transfer for SWCNT-water nanofluid from Ri = 1 to Ri = 10, a reduction in heat transfer with higher Hartmann numbers, and a significant 21.12 % enhancement when cylinder speed increases to Ω = 10 compared to a stationary cylinder. Larger cylinder sizes also improve convective heat transfer, with a 66.14 % increase for SWCNT-water nanofluid. Additionally, higher concentrations of SWCNT and AlO in hybrid nanofluids enhance heat transfer performance.
本研究对包含旋转内圆柱且充满纳米流体和混合纳米流体的半圆形封闭腔内的磁流体动力学（MHD）混合对流进行了数值分析。该研究探讨了具有不同纳米颗粒成分的AlO-TiO-SWCNT-水混合纳米流体以及AlO-水、TiO-水和SWCNT-水纳米流体的影响。分析包括开发一个人工神经网络（ANN）模型来预测结果，该模型在训练中平均努塞尔数的预测准确率达到97.34%，在测试中达到97.41%。该研究考察了雷诺数（Re）、理查森数（Ri）、哈特曼数（）、圆柱转速（Ω）、圆柱尺寸和纳米颗粒体积分数（φ）对传热和流体流动的影响。主要发现包括：对于SWCNT-水纳米流体，当Ri从1增加到10时，传热增加6.98%；随着哈特曼数增大，传热降低；与静止圆柱相比，当圆柱转速增加到Ω = 10时，传热显著增强21.12%。更大的圆柱尺寸也能改善对流换热，对于SWCNT-水纳米流体，对流换热增加66.14%。此外，混合纳米流体中较高浓度的SWCNT和AlO可增强传热性能。
