Spatiotemporal Stochastic Open-Channel Flow. I: Model and Its Parameter Data
Spatiotemporal Stochastic Open-Channel Flow. I: Model and Its Parameter Data
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DOI：
10.1061/(asce)0733-9429(1996)122:11(641
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发表时间：
1996-11
影响因子：
2.4
通讯作者：
T. Gates;Muhammad A. Al-Zahrani
中科院分区：
文献类型：
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作者：
T. Gates;Muhammad A. Al-Zahrani
由于空间和时间的变化性、测量误差和代表系统特性、边界和初始条件以及汇/源的参数的有限采样，明渠系统中的流量工程预测充满了模糊性。近年来，在解决这种不确定性以及不确定性给规划、设计和管理决策带来的风险、可靠性和信心等概念方面取得了稳步进展。一些简化的方法已被探索作为一个随机过程的明渠水流建模。然而，由于各种原因，没有解决的流动的完全时空随机性质。本文将圣维南模型描述为一组以时空随机场为参数的随机偏微分方程。替代的解决方案进行了讨论和比较的基础上考虑的控制方程的结构和参数的统计特性。广泛的现场数据分析提供的证据表明，模型参数具有较高的相对变异性，是统计非齐次的，通常有非正态残差与强滞后相关结构。这些特点必须占在实施任何建议的方法，解决方案的完整圣维南方程在随机设置。一个配套文件使用蒙特卡罗模拟来捕捉这些特点，在探索解决方案的代表性和广义流系统。
Engineering predictions of flows in open-channel systems are fraught with ambiguity due to spatial and temporal variability, measurement error and limited sampling of the parameters that represent system properties, boundary and initial conditions, and sinks/sources. Over recent years, steady progress has been made in addressing this uncertainty and the notions of risk, reliability, and confidence that uncertainty poses to planning, design, and management decisions. A number of simplified approaches have been explored for modeling open-channel flow as a stochastic process. However, for a variety of reasons, none have addressed the complete spatiotemporal random nature of the flow. This paper presents the Saint-Venant model as a set of stochastic partial differential equations whose parameters are spatiotemporal random fields. Alternative solution approaches are discussed and compared based upon a consideration of the structure of the governing equations and the statistical characteristics of the parameters. Analysis of extensive field data provides evidence that model parameters have high relative variability, are statistically nonhomogeneous, and commonly have nonnormal residuals with strong lag-dependent correlation structure. Such characteristics must be accounted for in implementing any proposed method for solution of the full Saint-Venant equations in a stochastic setting. A companion paper uses Monte Carlo simulation to capture these traits in exploring solutions for representative and generalized stream systems.