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Adaptive-Mesh-Refinement for hyperbolic systems of conservation laws based on a posteriori stabilized high order polynomial reconstructions

机译:基于后验稳定的高阶多项式重构的双曲守恒律系统的自适应网格精化

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摘要

In this paper we propose a third order accurate finite volume scheme based on polynomial reconstruction along with a posteriori limiting within an Adaptive-Mesh-Refinement (AMR) simulation code for hydro-dynamics equations in 2D. The a posteriori limiting is based on the detection of problematic cells on a so-called candidate solution computed at each stage of a third order Runge-Kutta scheme. Such detection may include different properties, derived from physics, such as positivity, from numerics, such as a non-oscillatory behavior, or from computer requirements such as the absence of NaN's. Troubled cell values are discarded and re-computed starting again from the previous time-step using this time a more dissipating scheme but only locally to these cells. By decrementing the degree of the polynomial reconstructions from 2 to 0 we switch from a third-order to a first-order accurate scheme. Ultimately some troubled cells may be updated with a first order accurate scheme for instance close to steep gradients. The entropy indicator sensor is used to refine/coarsen the mesh. This sensor is also employed in an a posteriori manner because if some refinement is needed at the end of a time step, then the current time-step is recomputed but only locally with such refined mesh. We show on a large set of numerical tests that this a posteriori limiting procedure coupled with the entropy-based AMR technology not only can maintain optimal accuracy on smooth flows but also stability on discontinuous profiles such as shock waves, contacts, interfaces, etc. Moreover numerical evidences show that this approach is comparable in terms of accuracy and cost to a more classical CWENO approach within the same AMR context.
机译:在本文中,我们提出了一种基于多项式重构的三阶精确有限体积方案,并在二维网格中的流体动力学方程的自适应网格精化(AMR)仿真代码中采用了后验极限。后验限制是基于对在三阶Runge-Kutta方案的每个阶段计算出的所谓候选解上有问题的单元的检测。这样的检测可以包括不同的属性,这些属性是从物理(例如正),数字(例如非振荡行为)或计算机要求(例如没有NaN)得出的。陷入困境的单元格值将被丢弃,并从前一个时间步开始重新计算,这次使用的是耗散更大的方案,但仅限于这些单元格局部使用。通过将多项式重构的次数从2减到0,我们从三阶精确方案转换为一阶精确方案。最终,可以用一阶精确方案(例如接近陡峭梯度)来更新一些故障单元。熵指示器传感器用于细化/粗化网格。该传感器也以后验方式使用,因为如果在某个时间步长的末尾需要进行一些改进,则将重新计算当前时间步长,但仅使用这种改进的网格进行局部计算。我们在大量的数值测试中表明,这种后验限制程序与基于熵的AMR技术相结合,不仅可以在平滑流动上保持最佳精度,而且还可以在不连续轮廓(例如冲击波,接触面,界面等)上保持稳定性。数值证据表明,在相同的AMR环境中,此方法的准确性和成本与经典的CWENO方法相当。

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