3 research outputs found

    Numerical Analysis on the Two-Dimensional Unsteady Magnetohydrodynamic Compressible Flow through a Porous Medium

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    In the present study, the unsteady magnetrohydrodynamic (MHD) flow of compressible fluid with variable thermal properties has been numerically investigated. The electrically conducting fluid flows through a porous media channel. The uniform magnetic field is applied perpendicular to the direction of the flow. The wall is assumed to be non-conducting and maintained at two different temperatures. The thermal conductivity and viscosity of the fluid change with temperature. Sixth - Order Accurate Compact Finite Difference scheme together with the Third-order Runge-Kutta method is used to solve a set of non-linear equations. The results of the calculation are expressed in the form of the velocity and temperature at different values of the magnetic field and porosity. The proposed mathematical model and numerical methods have been validated by comparing with the results of previously published studies that the compared results reveal the same trends. The difference is due to the compressibility and property variation effects. The results showed that the magnetic field and variable properties considerably influences the flows that is compressible thereby affecting the heat transfer as well as the wall shear stress

    Analysis of natural convection and the generation of entropy within an enclosure filled with nanofluid-packed structured pebble beds subjected to an external magnetic field and thermal radiation

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    DATA AVAILABILITY : No data was used for the research described in the article.Please read abstract in the article.https://www.elsevier.com/locate/esthj2024Mechanical and Aeronautical EngineeringSDG-09: Industry, innovation and infrastructur

    Heat and mass transport of nano-encapsulated phase change materials in a complex cavity: An artificial neural network coupled with incompressible smoothed particle hydrodynamics simulations

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    This work simulates thermo-diffusion and diffusion-thermo on heat, mass transfer, and fluid flow of nano-encapsulated phase change materials (NEPCM) within a complex cavity. It is a novel study in handling the heat/mass transfer inside a highly complicated shape saturated by a partial layer porous medium. In addition, an artificial neural network (ANN) model is used in conjunction with the incompressible smoothed particle hydrodynamics (ISPH) simulation to forecast the mean Nusselt and Sherwood numbers (Nuβˆ’ \stackrel{-}{Nu} and Shβˆ’ \stackrel{-}{Sh} ). Heat and mass transfer, as well as thermo-diffusion effects, are useful in a variety of applications, including chemical engineering, material processing, and multifunctional heat exchangers. The ISPH method is used to solve the system of governing equations for the heat and mass transfer inside a complex cavity. The scales of pertinent parameters are fusion temperature ΞΈf=0.05βˆ’0.95 {\theta }_{f} = 0.05-0.95 , Rayleigh number Ra=103βˆ’106 Ra = {10}^{3}-{10}^{6} , buoyancy ratio parameter N=βˆ’2βˆ’1 N = -2-1 , Darcy number Da=10βˆ’2βˆ’10βˆ’5 Da = {10}^{-2}-{10}^{-5} , Lewis number Le=1βˆ’20 Le = 1-20 , Dufour number Du=0βˆ’0.25 Du = 0-0.25 , and Soret number Sr=0βˆ’0.8 Sr = 0-0.8 . Alterations of Rayleigh number are effective in enhancing the intensity of heat and mass transfer and velocity field of NEPCM within a complex cavity. The high complexity of a closed domain reduced the influences of Soret-Dufour numbers on heat and mass transfer especially at the steady state. The fusion temperature works well in adjusting the intensity and location of a heat capacity ratio inside a complex cavity. The presence of a porous layer in a cavity's center decreases the velocity field within a complex cavity at a reduction in Darcy number. The goal values of Nuβˆ’ \stackrel{-}{Nu} and Shβˆ’ \stackrel{-}{Sh} for each data point are compared to those estimated by the ANN model. It is discovered that the ANN model's Nuβˆ’ \stackrel{-}{Nu} and Shβˆ’ \stackrel{-}{Sh} values correspond completely with the target values. The exact harmony of the ANN model prediction values with the target values demonstrates that the developed ANN model can forecast the Nuβˆ’ \stackrel{-}{Nu} and Shβˆ’ \stackrel{-}{Sh} values precisely
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