Abstract
This framework is devoted to analyze the tangent hyperbolic fluid in the presence of nanoparticles. In order to disperse the nanoparticle from the surface of sheet, condition of zero normal flux of nanoparticles is introduced at the surface. Inclined magnetic field is applied with an aligned angle \(\gamma\) at the surface of the sheet. Moreover, consideration of nanoparticles which are passively controlled at the surface is physically more realistic condition. The system of partial differential equations generated for tangent hyperbolic nanofluid are modeled and then converted into the system of nonlinear ordinary differential equations by employing suitable similarity transformations. Obtained systems of ordinary differential equations along with the condition of zero normal flux are successfully solved numerically by Runge–Kutta fourth-order method with shooting technique. The effects of various emerging parameters on velocity, temperature and concentration profiles are discussed in detail and presented graphically. Variation of skin friction coefficient and local Nusselt number are also oriented to analyze the variation of nanofluid at the surface. Considerable effects are found on velocity, temperature and concentration with the variable values of Weissenberg number \(We\) and inclination of angle \(\gamma\). It is finally concluded that increase in the Weissenberg number and power law index reduce the velocity profile; however, thermophoresis parameter shows the dominant effects on temperature and concentration profile. Significant effects on velocity, temperature and concentration profiles are also determined for both suction and injection cases.
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The first author is very thankful to University Grants Commission, India, for providing the opportunity to do this research work under UGC - Faculty Development Program (FDP), India.
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Prabhakar, B., Bandari, S. & Ul Haq, R. Impact of inclined Lorentz forces on tangent hyperbolic nanofluid flow with zero normal flux of nanoparticles at the stretching sheet. Neural Comput & Applic 29, 805–814 (2018). https://doi.org/10.1007/s00521-016-2601-4
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DOI: https://doi.org/10.1007/s00521-016-2601-4