论文标题
用于不确定性量化气管网络中瞬时流动流的随机有限体积方法
Stochastic Finite Volume Method for Uncertainty Quantification of Transient Flow in Gas Pipeline Networks
论文作者
论文摘要
我们开发了一个弱侵入性的框架，以模拟具有节点耦合和边界条件的图形连接域上通用双曲线偏微分方程系统的不确定性传播。该方法基于随机有限体积（SFV）方法，并且可以应用于致命运输网络上流体流动流动流动状态的不确定性定量（UQ）。数值方案具有建模时间变化边界参数的跨期不确定性的特定优势，该参数不能以严格的上和下（间隔）边界来表征。我们描述了单个管道的方案，然后制定了可控的Riemann问题（JRP），该问题可以扩展到一般的网络结构。我们使用标准基准测试网络演示了该方法的功能和性能特性。
We develop a weakly intrusive framework to simulate the propagation of uncertainty in solutions of generic hyperbolic partial differential equation systems on graph-connected domains with nodal coupling and boundary conditions. The method is based on the Stochastic Finite Volume (SFV) approach, and can be applied for uncertainty quantification (UQ) of the dynamical state of fluid flow over actuated transport networks. The numerical scheme has specific advantages for modeling intertemporal uncertainty in time-varying boundary parameters, which cannot be characterized by strict upper and lower (interval) bounds. We describe the scheme for a single pipe, and then formulate the controlled junction Riemann problem (JRP) that enables the extension to general network structures. We demonstrate the method's capabilities and performance characteristics using a standard benchmark test network.
