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Quantifying the Uncertainty Associated with Estimating Sediment Concentrations in Open Channel Flows Using the Stochastic Particle Tracking Method

机译:使用随机粒子跟踪方法量化与估算明渠水流中泥沙浓度相关的不确定性

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Suspended sediment concentrations are typically estimated using either the advection-diffusion equation or sediment rating curves in a deterministic manner. This study attempts to develop a stochastic approach for quantifying the probabilistic characteristics of sediment concentrations that can account for the uncertainty associated with flow randomness. Turbulence is a primary cause of particle diffusion in a flow. Impacts from such flow randomness on particle diffusion can be observed from two aspects: (1) the degree of spreading of a particle cloud, and (2) variability in concentration curves observed in different releases of particles under the same turbulence intensity. While the former diffusion has been extensively studied, the latter has not been fully investigated, in spite of its significance in terms of identifying the uncertainty associated with estimating concentrations. Herein, the effect of probabilistic characteristics attributed to turbulence on sediment concentrations is evaluated through multiple realizations of a Lagrangian-based stochastic differential equation for particle trajectory. Both the resuspension and deposition of particles are considered in the transport processes. Sediment concentrations can then be estimated from the spatial distribution of particles. As a result of the ensemble standard deviation, it is found that estimating higher-concentration regions is subject to a higher uncertainty. The coefficient of variation representing the extent of variability relative to their mean in lower-concentration regions is found to be more variable than that in higher-concentration regions. It is observed that when the ensemble standard deviation of the concentration is normalized by the square root of the particle number, the magnitude of the variability of concentration curves tends to approach asymptotically to one single curve. (C) 2015 American Society of Civil Engineers.
机译:通常使用对流扩散方程或沉积物额定曲线以确定的方式估算悬浮物的浓度。这项研究试图开发一种随机方法来量化沉积物浓度的概率特征,该特征可以解释与流量随机性相关的不确定性。湍流是流中颗粒扩散的主要原因。可以从两个方面观察到这种流动随机性对颗粒扩散的影响:(1)颗粒云的扩散程度,以及(2)在相同湍流强度下,不同颗粒释放时观察到的浓度曲线变化。尽管对前者的扩散进行了广泛的研究,但对后者的扩散尽管在确定与估算浓度有关的不确定性方面具有重要意义,但尚未得到充分研究。在此,通过基于粒子轨迹的基于拉格朗日的随机微分方程的多次实现,评估了湍流引起的概率特性对沉积物浓度的影响。在运输过程中都考虑了颗粒的重悬和沉积。然后可以根据颗粒的空间分布估算沉积物浓度。作为整体标准偏差的结果,发现估计较高浓度的区域具有较高的不确定性。发现在较低浓度区域中代表相对于其均值的变化程度的变异系数比在较高浓度区域中变异系数更大。观察到,当通过粒子数的平方根对浓度的总体标准偏差进行归一化时,浓度曲线的变化量的大小趋向于渐近地接近一条曲线。 (C)2015年美国土木工程师学会。

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