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首页> 外文期刊>International journal of nonlinear sciences and numerical simulation >Numerical Solutions for Radiative Heat Transfer in Ferrofluid Flow due to a Rotating Disk: Tiwari and Das Model
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Numerical Solutions for Radiative Heat Transfer in Ferrofluid Flow due to a Rotating Disk: Tiwari and Das Model

机译:旋转盘引起的铁磁流体流动辐射传热的数值解:Tiwari和DAS模型

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In this paper, we explore the von-Kármán infinite disk problem for the situation where ferrofluid resides in the space above the rotating disk. Furthermore, flow field is influenced by axial magnetic field. In this study, we treat water as the base fluid which consists of homogeneous suspensions of Fe3O4${m{F}}{{m{e}}_{m{3}}}{{m{O}}_{m{4}}}$ ferromagnetic particles. The main motivation here is to resolve heat transfer problem in the existence of non-linear radiative heat transfer. With the aid of von-Kármán relations, the equations of fluid motion and heat transfer are changed into a set of self-similar differential equations. These equations are dealt by an implicit finite-difference method with high precision. The results reveal that wall heat transfer rate can be improved by increasing solid volume fraction of ferromagnetic particles. Drag coefficient at the disk and heat transfer rate are increased as the strength of Lorentz force is enhanced. Viscous dissipation effect has an important part in improving heart transfer process which is vital in some applications. The results demonstrate that cooling capability of magnetite–water nanofluid is much superior to the conventional coolants. An excellent correlation of present results with the previous published articles is found in the all the cases.
机译:在本文中,我们探讨了von-kármán无限磁盘问题,以便是铁磁流体驻留在旋转盘上方的空间中的情况。此外,流场受轴磁场的影响。在这项研究中,我们将水作为基础液体,由均匀悬浮液组成fe3O4 $ { rm {e}} {{ rm {e}}} { rm {3}}} { rm {o}} _ { rm {4}} $ 铁磁粒子。这里的主要动机是解决存在非线性辐射传热存在的传热问题。借助于von-kármán关系,流体运动和传热的方程被改变为一组自相似的微分方程。这些方程由具有高精度的隐含有限差分方法进行处理。结果表明,通过增加铁磁性颗粒的固体体积分数,可以提高壁传热速率。随着Lorentz力的强度增强,磁盘和传热速率的拖动系数增加。粘性耗散效应具有改善心脏转移过程的重要组成部分,这在某些应用中至关重要。结果表明,磁铁矿纳米流体的冷却能力远远优于常规冷却剂。在所有情况下发现了与先前公布的文章的当前结果的优异相关性。

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