The Tracer Experimental Method for Determining Longitudinal Dispersion Coefficient of Nature River

2013 ◽  
Vol 748 ◽  
pp. 1155-1159 ◽  
Author(s):  
Yong Fan ◽  
Ke Bin Shi
Keyword(s):  
Experimental Method ◽  
Dispersion Coefficient ◽  
Flow Characteristics ◽  
Tracer Experiment ◽  
Longitudinal Dispersion ◽  
Longitudinal Dispersion Coefficient ◽  
Local Conditions ◽  
Practical Applications ◽  
Advantages And Disadvantages ◽  
Natural Rivers

Tracer experiment to obtain flow longitudinal dispersion coefficient is the most direct and reliable method. In this paper, the existing natural rivers longitudinal dispersion coefficient tracer experimental method for calculating is analyzed comprehensively. Focuses on the method of moments, the principles and applications of regression analysis, calculus optimization methods, and analyzes their advantages and disadvantages. In recent years the calculus optimization method is outstanding, determine river longitudinal dispersion coefficient method was developed and applied to an instance of some of the new tracer experiment. Local conditions should combine with topographic flow characteristics to select the appropriate calculation methods in practical applications. Finally, some problems of the natural rivers longitudinal dispersion coefficient study that need further investigation were put forward.

Prediction of longitudinal dispersion coefficients in natural rivers using genetic algorithm

2009 ◽  
Vol 40 (6) ◽  
pp. 544-552 ◽  
Author(s):  
Rajeev Ranjan Sahay ◽  
Som Dutta
Keyword(s):  
Field Data ◽  
Dispersion Coefficient ◽  
Shear Velocity ◽  
Longitudinal Dispersion ◽  
Performance Indices ◽  
Longitudinal Dispersion Coefficient ◽  
Mean Velocity ◽  
Cross Sectional ◽  
New Formula ◽  
Natural Rivers

A new expression for the prediction of longitudinal dispersion coefficient in natural rivers, using genetic algorithms, is proposed. The expression uses hydraulic and geometric characteristics of rivers, which are readily available. For performance evaluation, using published field data, results of coefficient prediction by the new expression and by the other reported expressions are compared. According to various performance indices, it is concluded that the new formula predicts the longitudinal dispersion coefficient more accurately. Sensitive analysis performed on input parameters indicates the ratio of the cross-sectional mean velocity to the bottom shear velocity to be the most influencing parameter for accurate prediction of the longitudinal dispersion coefficient.

scholarly journals Curve fitting the hydrodynamic and dispersion characteristics of pollutant released in an inland water system

2020 ◽  
Vol 5 (2) ◽  
pp. 038-046
Author(s):  
Baridakara Nwidadah ◽  
Olalekan Michael Adeloye
Keyword(s):  
Research Study ◽  
Dispersion Coefficient ◽  
Water System ◽  
Tracer Experiment ◽  
Longitudinal Dispersion ◽  
Longitudinal Dispersion Coefficient ◽  
Absolute Value ◽  
Constant Distance ◽  
Discrepancy Ratio ◽  
Distance Variable

The research study was performed by estimating the longitudinal dispersion coefficient for Dor Nwezor section of Bodo-Bonny River and conducting a tracer experiment using the constant distance variable time method. Eleven empirical models for the prediction of longitudinal dispersion coefficients were considered and analyzed using the hydraulic and geometric parameters of the river. The empirical and experimental results were analysed and compared statistically with Deng et al model yielded the most reliable method of predicting the longitudinal coefficient of dispersion of Dor Nwezor section of Bodo-Bonny River with the least root mean square value of 0.1221, mean absolute value of 0.0617 close to zero and discrepancy ratio of -0.2303 that falls within the accepted accuracy range of -0.3 to 0.3.

Prediction of longitudinal dispersion coefficient in natural rivers using a cluster-based Bayesian network

2017 ◽  
Vol 76 (2) ◽  
Author(s):  
Mohamad Javad Alizadeh ◽  
Hosein Shahheydari ◽  
Mohammad Reza Kavianpour ◽  
Hamid Shamloo ◽  
Reza Barati
Keyword(s):  
Bayesian Network ◽  
Dispersion Coefficient ◽  
Longitudinal Dispersion ◽  
Longitudinal Dispersion Coefficient ◽  
Natural Rivers

scholarly journals Experimental and numerical study of bed roughness effect on longitudinal dispersion

2021 ◽  
Author(s):  
Mohsen Nasrabadi ◽  
Mohammad Hossein Omid ◽  
Ali Mahdavi Mazdeh
Keyword(s):  
Dispersion Coefficient ◽  
Numerical Study ◽  
Breakthrough Curves ◽  
Roughness Height ◽  
Longitudinal Dispersion ◽  
Longitudinal Dispersion Coefficient ◽  
Relative Roughness ◽  
Longitudinal Dispersivity ◽  
Bed Roughness ◽  
Natural Rivers

Abstract The effects of bed roughness on the longitudinal dispersion coefficient (DL) were experimentally and numerically investigated in the present study. The tracer experiments were first carried out in a circular flume with a diameter of 1.6 m over both smooth and rough beds (coarse sand) with four sizes (ks = d65) of 1.04, 2.09, 3.01, and 4.24 mm. In addition, the one-dimensional advection-dispersion equation was numerically solved. The longitudinal dispersion coefficient was calculated by comparing the numerical and experimental breakthrough curves. The results showed that by increasing the bed roughness height (from zero to 4.24 mm), the longitudinal dispersion coefficient increased by 34%. In addition, the longitudinal dispersivity (λ = DL/V) increased with increasing relative roughness (ks/h), so that the range of longitudinal dispersivities in smooth bed experiments were 0.037–0.049 m and for rough bed (ks = 4.24 mm) were 0.07–0.084 m. In other words, with increasing the bed roughness height from zero (smooth bed) to 4.24 mm, the longitudinal dispersivities increased from 0.037 to 0.077 m, indicating an increase of about 108%. Furthermore, a relationship was developed using non-dimensional longitudinal dispersion (DL/(Vh)) as a function of relative roughness (ks/h). It can be concluded that taking into consideration bed roughness as the driving force of shear dispersion would improve predictive equations of the longitudinal dispersion in the rivers. Since the bottom of all natural rivers has roughness elements with different sizes, the results of this study will definitely be useful in estimating the longitudinal dispersion coefficient in natural rivers and quantifying the effect of roughness in the longitudinal dispersion coefficient equations.

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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