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A hydraulic roughness model for submerged flexible vegetation with uncertainty estimation

机译:不确定性淹没柔性植被的水力粗糙度模型

摘要

Submerged vegetation is a key component in natural and restored rivers. It preserves the ecological balance yet has an impact on the flow carrying capacity of a river. The hydraulic resistance produced by submerged flexible vegetation depends on many factors, including the vegetation stem size, height, number density and flow depth. In the present work a numerical model is used to generate synthetic velocity profile data for hydraulic roughness determination. In the model turbulence is simulated by the Spalart-Allmaras closure with a modified length scale which is dependent on the vegetation density and vegetation height to water depth ratio. Flexibility of vegetation is accounted for by using a large deflection analysis. The model has been verified against available experiments. Based on the synthetic data an inducing equation is derived, which relates the Manning roughness coefficient to the vegetation parameters, flow depth and a zero-plane displacement parameter. Furthermore, the uncertainty of the inducing equations in the estimation of the Manning roughness is assessed and the propagation of the uncertainty due to the variability of the vegetation and flow parameters existed in nature is investigated by using the method of Unscented Transformation (UT). The UT is found efficient and gives a more accurate estimation of the mean Manning roughness coefficient and provides information on the covariance of the roughness coefficient.
机译:淹没的植被是天然和恢复河流中的关键组成部分。它保持了生态平衡,但对河流的流量却产生了影响。淹没的柔性植被产生的水力阻力取决于许多因素,包括植被茎的大小,高度,数量密度和流动深度。在本工作中,使用数值模型来生成用于确定水力粗糙度的合成速度剖面数据。在模型中,湍流由Spalart-Allmaras闭合模拟,其长度比例经修改,取决于植被密度和植被高度与水深之比。植被的柔韧性通过大挠度分析来说明。该模型已针对现有实验进行了验证。基于合成数据,导出一个诱导方程,该方程将曼宁粗糙度系数与植被参数,流深和零平面位移参数相关。此外,通过无味变换(UT)方法,评估了曼宁粗糙度估算中的归纳方程的不确定性,并研究了由于植被和自然界中存在的流动参数的变化而引起的不确定性的传播。发现该UT是有效的,并且给出了平均曼宁粗糙度系数的更准确估计,并且提供了关于粗糙度系数的协方差的信息。

著录项

  • 作者

    Busari AO; Li CW;

  • 作者单位
  • 年度 2015
  • 总页数
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类

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