非局部守恒律二阶格式的收敛性

IF 0.6 4区 数学 Q4 STATISTICS & PROBABILITY
Sudarshan Kumar Kenettinkara, Nikhil Manoj, Veerappa Gowda G. D.
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引用次数: 0

摘要

在本文中,我们给出了包含非局部守恒律的交通流模型二阶数值格式的收敛性分析。我们将musl型空间重构与强稳定保持龙格-库塔时间步进相结合,设计了一个完全离散的二阶格式。通过建立极大值原理、有界变差估计和时间上的L1 Lipschitz连续性,证明了所得方案收敛于弱解。进一步,利用空间阶跃相关的斜率限制器,证明了其收敛于熵解。我们还提出了一种MUSCL-Hancock型二阶方案,与Runge-Kutta方案不同,它只需要一个中间阶段,并且更容易实现。通过数值实验验证了二阶格式与一阶格式的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Convergence of a second-order scheme for non-local conservation laws
In this article, we present the convergence analysis of a second-order numerical scheme for traffic flow models that incorporate non-local conservation laws. We combine a MUSCL-type spatial reconstruction with strong stability preserving Runge-Kutta time-stepping to devise a fully discrete second-order scheme. The resulting scheme is shown to converge to a weak solution by establishing the maximum principle, bounded variation estimates and L1 Lipschitz continuity in time. Further, using a space-step dependent slope limiter, we prove its convergence to the entropy solution. We also propose a MUSCL-Hancock type second-order scheme which requires only one intermediate stage unlike the Runge-Kutta schemes and is easier to implement. The performance of the proposed second-order schemes in comparison to a first-order scheme is demonstrated through several numerical experiments.
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来源期刊
Esaim-Probability and Statistics
Esaim-Probability and Statistics STATISTICS & PROBABILITY-
CiteScore
1.00
自引率
0.00%
发文量
14
审稿时长
>12 weeks
期刊介绍: The journal publishes original research and survey papers in the area of Probability and Statistics. It covers theoretical and practical aspects, in any field of these domains. Of particular interest are methodological developments with application in other scientific areas, for example Biology and Genetics, Information Theory, Finance, Bioinformatics, Random structures and Random graphs, Econometrics, Physics. Long papers are very welcome. Indeed, we intend to develop the journal in the direction of applications and to open it to various fields where random mathematical modelling is important. In particular we will call (survey) papers in these areas, in order to make the random community aware of important problems of both theoretical and practical interest. We all know that many recent fascinating developments in Probability and Statistics are coming from "the outside" and we think that ESAIM: P&S should be a good entry point for such exchanges. Of course this does not mean that the journal will be only devoted to practical aspects.
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