The investigation of Computational Fluid Dynamics (CFD) focuses on the mechanical properties of a Rabinowitsch fluid model that was constructed with the help of a stenosed elliptic conduit using the well-known Finite Volume Method (FVM). If you are interested in studying physiology or biomedicine, you will find that the Rabinowitsch fluid model in peristalsis is very helpful. This is because it is used to move blood around the heart and lungs, as well as in sanitary fluid transport systems, the transfer of corrosive fluids and innovative pharmaceutical delivery systems. By using the Cartesian coordinates, it is feasible to get the elliptic domain for this duct model. Additionally, the equation of an ellipse is used in the boundary conditions for this duct in order to maintain the elliptic cross-section for this duct in its current state. The Partial Differential Equations (PDEs) for the velocity profile may be determined as soon as the issue is recast in a form that does not need any dimensions to be specified. With the assistance of boundary conditions that are provided, these PDEs are solved over elliptical cross-sections, and after that, numerical solutions are obtained for them. A dilatant fluid represents state [Formula: see text], a Newtonian fluid represents state [Formula: see text] and a pseudoplastic fluid represents state [Formula: see text] in this model, making it significant. The desired model is computed numerically. FVM is used to study the results at a low Reynolds number (around [Formula: see text]. The goal is to theoritically analyze the transmission of heat and fluid resistance to the flow. The CFD findings with experimental data are compared to confirm the model predictions. A CFD model based on the FVM and experimental data from a flow cell is used to study how fluid moves through the ducts. This study focuses on the pressure, velocity and temperature fields of a fluid moving through a stenosed duct.

<abstract><p>The mean curvature-based image deblurring model is widely used to enhance the quality of the deblurred images. However, the discretization of the associated Euler-Lagrange equations produce a nonlinear ill-conditioned system which affect the convergence of the numerical algorithms like Krylov subspace methods. To overcome this difficulty, in this paper, we present two new symmetric positive definite (SPD) preconditioners. An efficient algorithm is presented for the mean curvature-based image deblurring problem which combines a fixed point iteration (FPI) with new preconditioned matrices to handle the nonlinearity and ill-conditioned nature of the large system. The eigenvalues analysis is also presented in the paper. Fast convergence has shown in the numerical results by using the proposed new preconditioners.</p></abstract>

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