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A mathematical modelis presented for entropy generation in transient hydromagnetic flow of an electroconductive\ud magnetic Casson (non-Newtonian) nanofluid over a porous stretching sheet in a permeable medium. The\ud Cattaneo-Christov heat flux model is employed to simulate non-Fourier (thermal relaxation) effects. A Rosseland\ud flux model is implemented to model radiative heat transfer. The Darcy model is employed for the porous media\ud bulk drag effect. Momentum slip is also included to simulate non-adherence of the nanofluid at the wall. The\ud transformed, dimensionless governing equations and boundary conditions (featuring velocity slip and convective\ud temperature) characterizing the flow are solved with the Adomian Decomposition Method (ADM). Bejan’s\ud entropy minimization generation method is employed. Cu-water and CuO-water nanofluids are considered.\ud Extensive visualization of velocity, temperature and entropy generation number profiles is presented for variation\ud in magnetic field parameter, unsteadiness parameter, Casson parameter, nanofluid volume fraction, permeability\ud parameter, suction/injection parameter, radiative parameter, Biot number, relaxation time parameter, velocity slip\ud parameter, Brinkman number (dissipation parameter), temperature ratio and Prandtl number. The evolution of\ud skin friction and local Nusselt number (wall heat transfer rate) are also studied. The ADM computations are\ud validated with simpler models from the literature. The solutions show that with elevation in volume fraction of\ud nanoparticle and Brinkman number, the entropy generation magnitudes are increased. An increase in Darcy\ud number also increases the skin friction and local Nusselt number. Increasing magnetic field, volume fraction,\ud unsteadiness, thermal radiation, velocity slip, Casson parameters, Darcy and Biot numbers are all observed to\ud boost temperatures. However, temperatures are reduced with increasing non-Fourier (thermal relaxation)\ud parameter. Greater flow acceleration is achieved for CuO-water nanofluid compared with Cu-water nanofluid\ud although the contrary response is computed in temperature distributions. The simulations are relevant to the high\ud temperature manufacturing fluid dynamics of magnetic nanoliquids, smart coating systems etc. |
