2016
DOI: 10.1186/s40064-016-2053-4
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On a bivariate spectral relaxation method for unsteady magneto-hydrodynamic flow in porous media

Abstract: The paper presents a significant improvement to the implementation of the spectral relaxation method (SRM) for solving nonlinear partial differential equations that arise in the modelling of fluid flow problems. Previously the SRM utilized the spectral method to discretize derivatives in space and finite differences to discretize in time. In this work we seek to improve the performance of the SRM by applying the spectral method to discretize derivatives in both space and time variables. The new approach combin… Show more

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Cited by 17 publications
(6 citation statements)
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References 19 publications
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“…Most of the standard methods of solving the boundary layer problems are the numerical approach based on the shooting algorithm with the Runge-Kutta scheme, finite difference method, spectral homotopy analysis method, and Newton-Raphson based methods such as the quasilinearization method and the successive linearization method. Recently, spectral based numerical techniques such as the Spectral Quasilinearization Method and Spectral Relaxation Method have been developed (see Motsa et al [23], Motsa [24], and Magagula et al [25]). As indicated by Motsa et al [26] and Zhou [27] Chebyshev spectral collocation methods are easy to implement and adaptable to various problems and provide more accurate approximations with a relatively small number of unknowns.…”
Section: Introductionmentioning
confidence: 99%
“…Most of the standard methods of solving the boundary layer problems are the numerical approach based on the shooting algorithm with the Runge-Kutta scheme, finite difference method, spectral homotopy analysis method, and Newton-Raphson based methods such as the quasilinearization method and the successive linearization method. Recently, spectral based numerical techniques such as the Spectral Quasilinearization Method and Spectral Relaxation Method have been developed (see Motsa et al [23], Motsa [24], and Magagula et al [25]). As indicated by Motsa et al [26] and Zhou [27] Chebyshev spectral collocation methods are easy to implement and adaptable to various problems and provide more accurate approximations with a relatively small number of unknowns.…”
Section: Introductionmentioning
confidence: 99%
“…We should remark that the BSRM method uses a similar approach to the BSLLM method except that the BSRM method does not use the quasilinearization approach but rather the Gauss‐Seidel approach to decouple the equations and hence resulting in different variable coefficients from the BSLLM. The BSRM for systems of three equations or more have been applied to various problems by many researchers including References 19,29. Rearranging terms in Equation (), we obtain trues=0pαs,rfalse(1false)false(η,ζfalse)f1,r+1false(sfalse)+βrfalse(1false)false(η,ζfalse)f1,r+1false(0false)ζ+γrfalse(1false)false(η,ζfalse)f1,r+1false(1false)ζ=R1false(η,ζfalse),0emtrues=0pαs,rfalse(2false)false(η,ζfalse)f2,r+1false(sfalse)+βrfalse(2false)false(η,ζfalse)f2,r+1false(0false)ζ+γrfalse(2false)false(η,ζfalse)f2,r+1false(1false)ζ=R2false(η,ζfalse),0em0emtrues=0p…”
Section: Pseudospectral Numerical Methodsmentioning
confidence: 99%
“…Thus combining finite differences and spectral methods compromises the computational accuracy of the spectral method. This method was extended to solve system of nonlinear partial differential equations 18,19 with spectral methods applied independently on both space and time derivatives. This method was named the bivariate spectral relaxation method (BSRM).…”
Section: Introductionmentioning
confidence: 99%
“…These useful features of the MD-BSQLM enable the approach to yield a very accurate solution. The method has a much better region of convergence for the approximate solution when compared to other Chebyshev spectral collocationbased methods such as bivariate spectral homotopy analysis method [47], bivariate spectral quasilinearization [48], and bivariate spectral relaxation method [49], among others. This study sought, among other things, to check the accuracy and robustness of the MD-BSQLM scheme in finding solutions to this class of problems with significant complexities.…”
Section: Introductionmentioning
confidence: 99%