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Differentiable monotonicity-preserving schemes for discontinuous Galerkin methods on arbitrary meshes

机译:任意网格上不连续Galerkin方法的可微单调性保持格式

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摘要

This work is devoted to the design of interior penalty discontinuous Galerkin (dG) schemes that preserve maximum principles at the discrete level for the steady transport and convection–diffusion problems and the respective transient problems with implicit time integration. Monotonic schemes that combine explicit time stepping with dG space discretization are very common, but the design of such schemes for implicit time stepping is rare, and it had only been attained so far for 1D problems. The proposed scheme is based on a piecewise linear dG discretization supplemented with an artificial diffusion that linearly depends on a shock detector that identifies the troublesome areas. In order to define the new shock detector, we have introduced the concept of discrete local extrema. The diffusion operator is a graph-Laplacian, instead of the more common finite element discretization of the Laplacian operator, which is essential to keep monotonicity on general meshes and in multi-dimension. The resulting nonlinear stabilization is non-smooth and nonlinear solvers can fail to converge. As a result, we propose a smoothed (twice differentiable) version of the nonlinear stabilization, which allows us to use Newton with line search nonlinear solvers and dramatically improve nonlinear convergence. A theoretical numerical analysis of the proposed schemes shows that they satisfy the desired monotonicity properties. Further, the resulting operator is Lipschitz continuous and there exists at least one solution of the discrete problem, even in the non-smooth version. We provide a set of numerical results to support our findings.
机译:这项工作致力于内部惩罚不连续伽勒金(dG)方案的设计,该方案在离散级别上保留了最大原理,以解决稳态输运和对流扩散问题以及具有隐式时间积分的各个瞬态问题。将显式时间步长与dG空间离散化相结合的单调方案非常普遍,但是这种用于隐式时间步长的方案的设计很少见,并且迄今为止仅针对一维问题实现。所提出的方案基于分段线性dG离散化,并辅以人工扩散,该扩散线性地依赖于识别麻烦区域的震动检测器。为了定义新的震动检测器,我们引入了离散局部极值的概念。扩散算子是图-拉普拉斯算子,而不是拉普拉斯算子的更常见的有限元离散化,这对于在常规网格和多维上保持单调性至关重要。由此产生的非线性稳定是非平滑的,非线性求解器可能无法收敛。结果,我们提出了一个非线性稳定化的平滑(两次微分)版本,它允许我们将牛顿与线性搜索非线性求解器一起使用,并显着改善了非线性收敛。所提出方案的理论数值分析表明,它们满足了所需的单调性。此外,所得算子是Lipschitz连续的,并且即使在非平滑版本中,也存在至少一种离散问题的解决方案。我们提供了一组数值结果来支持我们的发现。

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