The use of deconvolution techniques to identify the fundamental mixing characteristics of urban drainage structures.
The use of deconvolution techniques to identify the fundamental mixing characteristics of urban drainage structures.
复制标题
使用反卷积技术来识别城市排水结构的基本混合特征。
DOI：
10.2166/wst.2010.134
复制
发表时间：
2010
影响因子：
2.7
通讯作者：
J. G. Hattersley
中科院分区：
文献类型：
--
作者：
V. Stovin;I. Guymer;M. Chappell;J. G. Hattersley
混合和分散过程会影响城市排水系统中运输的污染物的时间和浓度。因此，表征特定液压结构的混合效应的方法对于排水网络模块来说是感兴趣的。以前的研究着重于附加的人孔，利用了一阶对流局方程（ADE）和聚集的死区（ADZ）模型来表征分散体。但是，尽管已经确定了旅行时间的系统变化，但已经确定了排放和附加密度的函数，但一阶ADE和ADZ模型并未为观察到的人孔数据提供特别良好的拟合，这意味着派生的参数值并不独立于该值上游颞浓度轮廓。一种替代性，更强大的方法是使用该系统的累积停留时间分布（CRTD），并且已显示出附加的人孔的溶质传输特性的特征仅为二小无尺度CRTD，一个用于预示例，另一个用于thes-threshishold threshorshold附加深度。尽管可以使用计算流体动力学（CFD）模型轻松地生成与瞬时上游注射的CRTD，但实际上困难阻碍了非固有和嘈杂的实验室数据集的CRTD特性的鉴定。本文展示了如何应用从系统理论得出的反卷积方法来识别与城市排水结构相关的CRTD。
Mixing and dispersion processes affect the timing and concentration of contaminants transported within urban drainage systems. Hence, methods of characterising the mixing effects of specific hydraulic structures are of interest to drainage network modellers. Previous research, focusing on surcharged manholes, utilised the first-order Advection-Dispersion Equation (ADE) and Aggregated Dead Zone (ADZ) models to characterise dispersion. However, although systematic variations in travel time as a function of discharge and surcharge depth have been identified, the first order ADE and ADZ models do not provide particularly good fits to observed manhole data, which means that the derived parameter values are not independent of the upstream temporal concentration profile. An alternative, more robust, approach utilises the system's Cumulative Residence Time Distribution (CRTD), and the solute transport characteristics of a surcharged manhole have been shown to be characterised by just two dimensionless CRTDs, one for pre- and the other for post-threshold surcharge depths. Although CRTDs corresponding to instantaneous upstream injections can easily be generated using Computational Fluid Dynamics (CFD) models, the identification of CRTD characteristics from non-instantaneous and noisy laboratory data sets has been hampered by practical difficulties. This paper shows how a deconvolution approach derived from systems theory may be applied to identify the CRTDs associated with urban drainage structures.