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A Numerical Simulation of the Magneto-Micropolar Nanofluid Flow Configured by the Stimulus Energies and Chemical Interaction

Publication Type : Journal Article

Publisher : Taylor & Francis

Source : Journal of Computational and Theoretical Transport

Url : https://www.tandfonline.com/doi/abs/10.1080/23324309.2024.2379798

Campus : Chennai

School : School of Engineering

Year : 2024

Abstract : Inspired by the numerous applications of non-Newtonian nanomaterials in science and industries, the two-dimensional convective hydromagnetic movement in a micropolar nanofluid within an expanding permeable surface with the existence of binary chemical reaction and convective boundary constraints is examined in this study. Thermal radiation, energy generation, and activation energy interactions are employed to handle the nanofluid flow. The underlying equations are transformed via the similarity transitions into an array of non-linear ODE. The BVP4C MATLAB package is applied to solve the system of equations numerically. The necessary outcomes of the micropolar fluid velocity, micro-rotation, temperature, concentration, friction factor, mass transfer, and heat transfer rates are shown graphically and thoroughly analyzed quantitatively. The micropolar nanofluid’s mobility is reduced by the heating and solutal Grashof numbers. It is noted that the magnetic coefficient decreases velocity, it has the opposite impact on the degree of temperature. The plotted outcomes also show that the temperature increases as the increase in stimulation radiation variable. Meanwhile, the thermal field exhibits strengthen as the intensity of Biot number and Eckert number responses enhances. The growth of the activation energy leads to a noticeable enrichment in the concentration profile.

Cite this Research Publication : Ajithkumar, M., N. Ravi Kumar, Jintu Mani Nath, M. Vinodkumar Reddy, and Tusar Kanti Das. "A numerical simulation of the magneto-micropolar nanofluid flow configured by the stimulus energies and chemical interaction." Journal of Computational and Theoretical Transport 53, no. 7 (2024): 469-489.

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