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Successive linearisation approach to analyse thermally radiative stagnation point micropolar nanofluid flow with regression model

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Abstract

The present paper is devoted to the investigation of magnetohydrodynamics (MHD) mixed convection stagnation point flow of a micropolar nanofluid with thermal radiation, microrotation, viscous and Joule dissipations, Brownian and thermophoretic diffusions, etc. The present analysis is done because it contains large potential to deal with many industrial processes such as electrical power generation, nuclear energy plant, melt spinning technique for cooling liquids, astrophysical flows, space vehicles, geothermal extractions, solar system, etc. The numerical solutions of the governing equations are obtained by successive linearisation method (SLM). The influence of various developing parameters, such as thermal radiation parameter, mixed convection parameter, thermophoretic parameter, etc., on the flow field is examined through graphs by accumulating sufficient data using SLM. A comparative study is performed between our results and previously obtained results in the limiting sense. Apart from this, the quadratic multiple regression analysis is performed for skin friction coefficient. It indicates that when the free stream is moving with less velocity than stretching velocity then a small variation in microrotation leads to large perturbation in skin friction in comparison to mixed convection parameter but in the opposite case, the buoyancy force becomes more dominant.

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Authors and Affiliations

  1. Department of Applied Mathematics, Indian Institute of Technology (Indian School of Mines), Dhanbad,  826 004, India

    B Kumar & G S Seth

  2. Department of Mathematics, National Institute of Technology, Jamshedpur,  831 014, India

    R Nandkeolyar

Authors
  1. B Kumar
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  2. G S Seth
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  3. R Nandkeolyar
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Correspondence to B Kumar.

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Kumar, B., Seth, G.S. & Nandkeolyar, R. Successive linearisation approach to analyse thermally radiative stagnation point micropolar nanofluid flow with regression model. Pramana - J Phys 93, 74 (2019). https://doi.org/10.1007/s12043-019-1834-z

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  • DOI: https://doi.org/10.1007/s12043-019-1834-z

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