双曲型网络仿真的不连续Galerkin法可伸缩Riemann解算器

Aidan Hamilton, Jing-Mei Qiu, Hong Zhang
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引用次数: 0

摘要

我们开发了一套高效的网络流体模拟计算算法和仿真工具。数学模型是网络边缘上的一组双曲守恒定律,以及网络结点上的耦合条件。例如,浅水系统,以及河流交叉口的通量平衡和连续性条件,可以模拟河网上的水流。将计算精度高、鲁棒性好的间断伽辽金方法与显式强稳定保持龙格-库塔方法相结合,实现了网络边缘仿真。同时,线性和非线性可伸缩黎曼解算器正在开发和实现在网络顶点。这些网络模拟产生的工具被添加到现有的PETSc和DMNetwork软件库中,一般用于科学界。在一台拥有8192个处理器的超大规模计算机上,对具有超过10亿个网络变量的密西西比河浅水系统进行了仿真,并获得了最佳并行效率。进一步的潜在应用包括高速公路网络上的交通流模拟和动脉网络上的血流模拟等。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Scalable Riemann Solvers with the Discontinuous Galerkin Method for Hyperbolic Network Simulation
We develop a set of highly efficient and effective computational algorithms and simulation tools for fluid simulations on a network. The mathematical models are a set of hyperbolic conservation laws on edges of a network, as well as coupling conditions on junctions of a network. For example, the shallow water system, together with flux balance and continuity conditions at river intersections, model water flows on a river network. The computationally accurate and robust discontinuous Galerkin methods, coupled with explicit strong stability preserving Runge-Kutta methods, are implemented for simulations on network edges. Meanwhile, linear and nonlinear scalable Riemann solvers are being developed and implemented at network vertices. These network simulations result in tools that are added to the existing PETSc and DMNetwork software libraries for the scientific community in general. Simulation results of a shallow water system on a Mississippi river network with over one billion network variables are performed on an extreme-scale computer using up to 8,192 processor with an optimal parallel efficiency. Further potential applications include traffic flow simulations on a highway network and blood flow simulations on a arterial network, among many others.
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