14 research outputs found

    Estimation of time-dependent heat flux using temperature distribution at a point in a two layer system

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    AbstractIn this paper, the conjugate gradient method, coupled with the adjoint problem, is used in order to solve the inverse heat conduction problem and estimation of the time-dependent heat flux, using temperature distribution at a point in a two layer system. Also, the effect of noisy data on the final solution is studied. The numerical solution of the governing equations is obtained by employing a finite-difference technique. For solving this problem, the general coordinate method is used. The irregular region in the physical domain (r,z) is transformed into a rectangle in the computational domain (ξ,η). The present formulation is general and can be applied to the solution of boundary inverse heat conduction problems over any region that can be mapped into a rectangle. The obtained results for few selected examples show the good accuracy of the presented method. Also, the solutions have good stability even if the input data includes noise. The problem is solved in an axisymmetric case. Applications of this model are in the thermal protect systems (t.p.s.) and heat shield systems

    Investigating Pressure Gradient Dynamics in Two-phase Fluid Flow through Porous Media: An Experimental and Numerical Analysis

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    This study investigates pressure gradient dynamics within a porous medium in the context of two-phase fluid flow, specifically water and sand particle interactions. Using experimental data, we refine pressure correction coefficients within a numerical solution framework, employing the Semi-Implicit Method for the Pressure-linked Equations algorithm. Our findings highlight the relative nature of pressure gradient phenomena, with particle size and volume fraction emerging as crucial determinants. Graphical representations reveal a clear trend: an increase in volume fraction, up to 40%, across varying Reynolds Numbers, leads to a transition towards non-Newtonian behavior in the two-phase fluid system. Unlike the linear pressure gradient seen in single-phase fluid flow, the interplay between liquid and solid phases, along with drag forces, imparts a distinctly nonlinear trajectory to the pressure gradient in two-phase fluid flow scenarios. As the two-phase flow enters a porous medium, numerous factors come into play, resulting in a pressure drop. These factors include changes in cross-sectional geometry, alterations in boundary layer dynamics, and ensuing momentum fluctuations. Interestingly, an increase in porosity percentage inversely correlates with pressure gradient, resulting in reduced pressure gradient with higher porosity levels.&nbsp

    Numerical Investigation of Channel Geometry on the Performance of a Pem Fuel Cell

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    A complete three-dimensional and single phase model for proton exchange membrane (PEM) fuel cells was used to investigate the effect of using different channels geometry on the performances, current density and gas concentration. The proposed model was a full cell model, which includes all the parts of the PEM fuel cell, flow channels, gas diffusion electrodes, catalyst layers and the membrane. Coupled transport and electrochemical kinetics equations were solved in a single domain; therefore no interfacial boundary condition was required at the internal boundaries between cell components. This computational fluid dynamics code was employed as the direct problem solver, which was used to simulate the three-dimensional mass, momentum, energy and species transport phenomena as well as the electron- and proton-transfer process taking place in a PEMFC. The results showed that the predicted polarization curves by using this model were in good agreement with the experimental results and a high performance was observed by using circle geometry for the channels of anode and cathode sides. Also, the results showed that the performance of the fuel cell improved when a rectangular channel was used

    Time-Dependent Heat Flux Estimation in Multi-Layer Systems by Inverse Method

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