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High-Order Hyperbolic Residual-Distribution Schemes on Arbitrary Triangular Grids

机译:任意三角形网格上的高阶双曲余数分布方案

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In this paper, we construct high-order hyperbolic residual-distribution schemes for general advection-diffusion problems on arbitrary triangular grids. We demonstrate that the second-order accuracy of the hyperbolic schemes can be greatly improved by requiring the scheme to preserve exact quadratic solutions. We also show that the improved second-order scheme can be easily extended to the third-order by further requiring the exactness for cubic solutions. We construct these schemes based on the Low-Diffusion-A and the Streamwise-Upwind-Petrov-Galerkin methodology formulated in the framework of the residual-distribution method. For both second- and third-order-schemes, we construct a fully implicit solver by the exact residual Jacobian of the second-order scheme, and demonstrate rapid convergence of 10-15 iterations to reduce the residuals by 10 orders of magnitude. We also demonstrate that these schemes can be constructed based on a separate treatment of the advective and diffusive terms, which paves the way for the construction of hyperbolic residual-distribution schemes for the compressible Navier-Stokes equations. Numerical results show that these schemes produce exceptionally accurate and smooth solution gradients on highly skewed and anisotropic triangular grids, including curved boundary problems, using linear elements. We also present Fourier analysis performed on the constructed linear system and show that an under-relaxation parameter is needed for stabilization of Gauss-Seidel relaxation.
机译:在本文中,我们针对任意三角形网格上的一般对流扩散问题构造了高阶双曲残差分布方案。我们证明,通过要求该方案保留精确的二次解,可以大大提高双曲线方案的二阶精度。我们还表明,通过进一步要求三次解的精确度,改进的二阶方案可以轻松扩展到三阶。我们基于在残差分布方法框架内制定的低扩散A和流向上风Petrov-Galerkin方法构建这些方案。对于二阶和三阶方案,我们通过二阶方案的精确残差雅可比矩阵构造一个完全隐式求解器,并演示了10-15次迭代的快速收敛,以将残差减少10个数量级。我们还证明,可以基于对流项和扩散项的单独处理来构造这些方案,这为构造可压缩Navier-Stokes方程的双曲残差分布方案铺平了道路。数值结果表明,这些方案使用线性元素在高度偏斜和各向异性的三角网格(包括弯曲边界问题)上产生异常准确且平滑的求解梯度。我们还介绍了在构造的线性系统上执行的傅立叶分析,结果表明,需要一个松弛松弛参数来稳定高斯-塞德尔松弛。

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