Determination of the longitudinal dispersion coefficient in flows subjected to high-frequency oscillations

1983 ◽  
Vol 26 (6) ◽  
pp. 1380 ◽  
Author(s):  
M. J. Jaeger
Keyword(s):  
High Frequency ◽  
Dispersion Coefficient ◽  
Longitudinal Dispersion ◽  
Longitudinal Dispersion Coefficient ◽  
High Frequency Oscillations

Prediction of longitudinal dispersion coefficient using laboratory and field data: relationship comparisons

2012 ◽  
Vol 44 (2) ◽  
pp. 362-376 ◽  
Author(s):  
Z. Ahmad
Keyword(s):  
Dispersion Coefficient ◽  
Field Studies ◽  
Pollutant Concentration ◽  
Longitudinal Dispersion ◽  
Stream Velocity ◽  
Longitudinal Dispersion Coefficient ◽  
Flow Parameters ◽  
Wide Range ◽  
Channel Parameters

Knowledge of dispersion of pollutants in streams is necessary for the determination of both the acceptable limits of effluent input and the concentration along the river course. In the far-field, the primary variation of concentration is in one direction and termed longitudinal dispersion; it is independent of the geometrical configuration and type of source. The longitudinal dispersion coefficient represents the dispersive characteristics of a stream and is required to compute the pollutant concentration at downstream locations of the streams. The longitudinal dispersion coefficient can be estimated either from the pollutant concentration profile, stream velocity profile or channel and flow parameters. Many laboratory and field studies have been carried out by several investigators to develop relationships for the longitudinal dispersion coefficient in terms of the known hydraulic characteristics of the stream. This paper evaluates the accuracy of the existing empirical relationships for the prediction of longitudinal dispersion coefficient, using a large volume of data that cover a wide range of flow and channel parameters.

scholarly journals Investigation of a parameter estimation method for contaminant transport in aquifers

2001 ◽  
Vol 3 (4) ◽  
pp. 203-213 ◽  
Author(s):  
Channa Rajanayaka ◽  
Don Kulasiri
Keyword(s):  
Experimental Data ◽  
Hydraulic Conductivity ◽  
Contaminant Transport ◽  
Dispersion Coefficient ◽  
Estimation Method ◽  
Longitudinal Dispersion ◽  
Longitudinal Dispersion Coefficient ◽  
System Noise ◽  
Estimated Parameters ◽  
Comparison Of The Results

Real world groundwater aquifers are heterogeneous and system variables are not uniformly distributed across the aquifer. Therefore, in the modelling of the contaminant transport, we need to consider the uncertainty associated with the system. Unny presented a method to describe the system by stochastic differential equations and then to estimate the parameters by using the maximum likelihood approach. In this paper, this method was explored by using artificial and experimental data. First a set of data was used to explore the effect of system noise on estimated parameters. The experimental data was used to compare the estimated parameters with the calibrated results. Estimates obtained from artificial data show reasonable accuracy when the system noise is present. The accuracy of the estimates has an inverse relationship to the noise. Hydraulic conductivity estimates in a one-parameter situation give more accurate results than in a two-parameter situation. The effect of the noise on estimates of the longitudinal dispersion coefficient is less compared to the effect on hydraulic conductivity estimates. Comparison of the results of the experimental dataset shows that estimates of the longitudinal dispersion coefficient are similar to the aquifer calibrated results. However, hydraulic conductivity does not provide a similar level of accuracy. The main advantage of the estimation method presented here is its direct dependence on field observations in the presence of reasonably large noise levels.

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