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Il Won Seo - One of the best experts on this subject based on the ideXlab platform.
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longitudinal Dispersion Coefficient for mixing in open channel flows with submerged vegetation
Ecological Engineering, 2020Co-Authors: Jaehyun Shin, Jin Yu Seo, Il Won SeoAbstract:Abstract An experimental investigation was conducted to analyze the flow and pollutant mixing characteristics in the vegetated channel, in which the velocity data was collected by micro acoustic Doppler velocimeter, and pollutant Dispersion data was obtained through tracer tests in an open channel with submerged vegetation. The effects of submergence ratio (Sr = 2.0–3.5), and stem density (M = 0.4–5.9) on the vertical distribution of the stream-wise velocity and the turbulent shear stress were analyzed. The results of the flow experiments showed that the intensity of velocity deviation became larger with increasing stem density and decreasing submergence ratio. The turbulent shear stress at the exchange zone also increased with submergence ratio, as well as with increasing stem density. The calculation results of the longitudinal Dispersion Coefficient based on the vertical velocity profile data showed that as the submergence ratio and stem density increased, the Dispersion Coefficients increased linearly. This monotonic increase of the velocity-based Dispersion Coefficient differed from former research, in which concentration-based Dispersion Coefficient values approached a constant value in a higher submergence ratio of over 2. Comparison of the velocity-based longitudinal Dispersion Coefficients with concentration-based Coefficients in this research demonstrated that the velocity-driven Coefficient had a linear relation with the concentration-driven Coefficients, with smaller values than the concentration-based Dispersion. This difference can be explained by the fact that concentration-driven Dispersion Coefficient included the storage effects due to the submerged vegetation, while the velocity-driven Coefficient only accounted for shear flow effects.
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estimation of the transverse Dispersion Coefficient for two dimensional models of mixing in natural streams
Journal of Hydro-environment Research, 2017Co-Authors: Kyong Oh Baek, Il Won SeoAbstract:Abstract Existing equations to predict the transverse Dispersion Coefficient for the two-dimensional model of mixing and transport in natural streams use the channel aspect ratio and roughness factor as well as the sinuosity of the channel as the controlling parameters, and the performance of the equation varies largely according to the range of each parameter that was studied. To this end, this study suggests the criteria to select the proper method to calculate the transverse Dispersion Coefficient according to the availability and range of each parameter. The strengths and limitations of existing methodologies were compared against observed data acquired from tracer experiments conducted in natural streams. Further, a flow chart was proposed with the criteria to select a suitable method under specific hydraulic and geometric conditions of the stream. The results of the classification flow chart showed that, at the first step, in the cases where secondary currents data were available for natural streams, the theoretical equation by Baek and Seo (2011) could be used to estimate the transverse Dispersion Coefficient. At the second step, in the case of large value of P , i.e., P > 0.04, the equation by Baek and Seo (2013) was suitable to estimate the transverse Dispersion Coefficient, while in the case of a small value of P , equations by Yotukura and Sayer (1976) and Baek and Seo (2013), could be used with little differences. At the third step, for the narrow streams with W / h W / h > 50, the results of Jeon et al. (2007) showed much better agreement with the observed values than the others.
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on the methods for determining the transverse Dispersion Coefficient in river mixing
Advances in Water Resources, 2016Co-Authors: Kyong Oh Baek, Il Won SeoAbstract:In this study, the strengths and weaknesses of existing methods for determining the Dispersion Coefficient in the two-dimensional river mixing model were assessed based on hydraulic and tracer data sets acquired from experiments conducted on either laboratory channels or natural rivers. From the results of this study, it can be concluded that, when the longitudinal Dispersion Coefficient as well as the transverse Dispersion Coefficients must be determined in the transient concentration situation, the two-dimensional routing procedures, 2D RP and 2D STRP, can be employed to calculate Dispersion Coefficients among the observation methods. For the steady concentration situation, the STRP can be applied to calculate the transverse Dispersion Coefficient. When the tracer data are not available, either theoretical or empirical equations by the estimation method can be used to calculate the Dispersion Coefficient using the geometric and hydraulic data sets. Application of the theoretical and empirical equations to the laboratory channel showed that equations by Baek and Seo [[3], 2011] predicted reasonable values while equations by Fischer [23] and Boxwall and Guymer (2003) overestimated by factors of ten to one hundred. Among existing empirical equations, those by Jeon et al. [28] and Baek and Seo [6] gave the agreeable values of the transverse Dispersion Coefficient for most cases of natural rivers. Further, the theoretical equation by Baek and Seo [5] has the potential to be broadly applied to both laboratory and natural channels.
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Empirical equation for transverse Dispersion Coefficient based on theoretical background in river bends
Environmental Fluid Mechanics, 2013Co-Authors: Kyong Oh Baek, Il Won SeoAbstract:There are different approaches to estimating the transverse Dispersion Coefficient in river mixing. Theoretical approaches have derived the Dispersion Coefficient from the concept of shear flow, which has dominant effects on the transverse mixing. Empirical approaches have developed an equation using the hydraulic and geometric data of rivers through dimensional analysis and regression techniques. These two equations interact closely with each other. For example, the complicated theoretical equation can be simplified by empirical approaches, and the functional relationships of the empirical equation can be derived from theoretical bases. In this study, a new empirical equation for the transverse Dispersion Coefficient has been developed based on the theoretical background in river bends. As a regression method, the least-square iterative method was used because the equation was a nonlinear model. The estimated Dispersion Coefficients derived by the new equation were compared with observed transverse Dispersion Coefficients acquired from natural rivers and Coefficients calculated by the other existing empirical equations. From a comparison of the existing transverse Dispersion equations and the proposed equation, it appears that the behavior of the existing formula in a relative sense is very much dependent on the flow condition and the river geometry. Moreover, the proposed equation does not vary widely according to variation of flow conditions. Also, it was revealed that the equation proposed in this study becomes an asymptotic curve as the curvature effect increases.
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development of an empirical equation for the transverse Dispersion Coefficient in natural streams
Environmental Fluid Mechanics, 2007Co-Authors: Tae Myoung Jeon, Kyong Oh Baek, Il Won SeoAbstract:In this study, a new empirical equation for the transverse Dispersion Coefficient has been developed based on the hydraulic and geometric parameters in natural streams using a regression technique. First, a total of 32 data sets in 16 streams were collected. Among those sets, 16 sets were used for deriving the new equation, and the other 16 sets were used for verifying the equation. Then, through dimensional analysis, it was found that the normalized transverse Dispersion Coefficient is associated with several parameters such as sinuosity, aspect ratio, and a friction term. The robust least square method was applied to estimate regression Coefficients. The newly proposed equation was proven to be superior in explaining the Dispersion characteristics of natural streams more precisely compared to the existing equations.
M Roustan - One of the best experts on this subject based on the ideXlab platform.
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a unified correlation for predicting liquid axial Dispersion Coefficient in bubble columns
Chemical Engineering Science, 2001Co-Authors: S Moustiri, Gilles Hebrard, S S Thakre, M RoustanAbstract:Abstract Experimental measurements of gas holdup and liquid axial Dispersion Coefficients have been carried out in two bubble columns (column I; D c =15 cm and column II; D c =20 cm ), operating with co-current upflow of gas and liquid. In addition, measurement of bubble size was investigated. Peclet number and liquid axial Dispersion Coefficient were calculated from the means and variances of the residence time distribution (RTD) curve. The experimental data obtained show that the hydrodynamic of bubble column depends on superficial gas and liquid velocities, column diameter and the gas flow regime. Present results and the available literature data were used to develop a model involving Bo,Re,Fr and Ga for predicting liquid axial Dispersion Coefficient.
Wenxin Huai - One of the best experts on this subject based on the ideXlab platform.
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a simplified method for estimating the longitudinal Dispersion Coefficient in ecological channels with vegetation
Ecological Indicators, 2017Co-Authors: Wenxin Huai, Haoran Shi, Suwen SongAbstract:The longitudinal Dispersion Coefficient is an important parameter for describing the transport processes in rivers. Riparian vegetation significantly influences the velocity profile and transport processes. This paper examines the longitudinal Dispersion Coefficient under the condition that rigid emergent vegetation grows symmetrically along the river bank. We build a three-zone model by extending the N-zone models of Chickwendu and Boxall & Guymer. We also analyze the velocity profiles that are significantly affected by vegetation to estimate the parameters in our model. Our tests using the experimental data from a series of experiments validate the acceptable accuracy of our three-zone model.
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estimating the longitudinal Dispersion Coefficient in straight natural rivers
Journal of Hydraulic Engineering, 2016Co-Authors: Yufei Wang, Wenxin HuaiAbstract:AbstractTheoretical methods have been developed to estimate the longitudinal Dispersion Coefficients (k) in natural rivers. The triple integral expression for longitudinal Dispersion caused by the transverse velocity gradient and the solution for the depth-averaged streamwise velocity distribution in the transverse direction in a rectangular flume are used in this study. The longitudinal Dispersion Coefficient was calculated after changing the nonintegral formula for the velocity distribution into a trigonometric function series by Fourier transformation and then by substituting this series into the triple integral expression. A dimensionless formula for longitudinal Dispersion in the flume was then obtained by regression analysis and was consistent with the experimental results obtained in previous studies and this study. By analyzing the measured Dispersion Coefficients of the natural rivers and the corresponding values obtained from the formula for a flume with the same hydraulic parameters, a formula ...
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estimation of longitudinal Dispersion Coefficient in rivers
Journal of Hydro-environment Research, 2014Co-Authors: Yuhong Zeng, Wenxin HuaiAbstract:The longitudinal Dispersion Coefficient is a crucial parameter for 1D water quality analyzing in natural rivers, and different types of empirical equations have been presented in the literature. To evaluate the precision of those commonly used equations, 116 sets of measured data for rivers in U.S. and UK have been collected for comparison. Firstly, the precisions of selected ten empirical equations under different aspect ratio (water surface width B/water depth H) have been compared, and calculation shows that most of the equations have underestimated the longitudinal Dispersion when 20 < B/H < 100, in which most of the natural rivers located. The regression analysis on the collected data sets proved that the product of water depth H and the cross-sectional averaged velocity U has a higher linear correlation with the longitudinal Dispersion Coefficient than the product of H and shear velocity u∗, and then a new expression of longitudinal Dispersion Coefficient, which is a combination of the product of HU and other two nondimensional hydraulic and geometric parameters, was deduced and the exponents were determined by the regression analysis. The comparison between the measured data and the predicted results shows that the presented equation has the highest precision for the studied natural rivers. To further evaluate the precision of the empirical formulae to artificial open channels, comparison was made between laboratory measuring data and empirical equation prediction, and the results have shown that the newly presented model is effective at predicting longitudinal Dispersion in trapezoidal artificial channels too.
Kyong Oh Baek - One of the best experts on this subject based on the ideXlab platform.
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estimation of the transverse Dispersion Coefficient for two dimensional models of mixing in natural streams
Journal of Hydro-environment Research, 2017Co-Authors: Kyong Oh Baek, Il Won SeoAbstract:Abstract Existing equations to predict the transverse Dispersion Coefficient for the two-dimensional model of mixing and transport in natural streams use the channel aspect ratio and roughness factor as well as the sinuosity of the channel as the controlling parameters, and the performance of the equation varies largely according to the range of each parameter that was studied. To this end, this study suggests the criteria to select the proper method to calculate the transverse Dispersion Coefficient according to the availability and range of each parameter. The strengths and limitations of existing methodologies were compared against observed data acquired from tracer experiments conducted in natural streams. Further, a flow chart was proposed with the criteria to select a suitable method under specific hydraulic and geometric conditions of the stream. The results of the classification flow chart showed that, at the first step, in the cases where secondary currents data were available for natural streams, the theoretical equation by Baek and Seo (2011) could be used to estimate the transverse Dispersion Coefficient. At the second step, in the case of large value of P , i.e., P > 0.04, the equation by Baek and Seo (2013) was suitable to estimate the transverse Dispersion Coefficient, while in the case of a small value of P , equations by Yotukura and Sayer (1976) and Baek and Seo (2013), could be used with little differences. At the third step, for the narrow streams with W / h W / h > 50, the results of Jeon et al. (2007) showed much better agreement with the observed values than the others.
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on the methods for determining the transverse Dispersion Coefficient in river mixing
Advances in Water Resources, 2016Co-Authors: Kyong Oh Baek, Il Won SeoAbstract:In this study, the strengths and weaknesses of existing methods for determining the Dispersion Coefficient in the two-dimensional river mixing model were assessed based on hydraulic and tracer data sets acquired from experiments conducted on either laboratory channels or natural rivers. From the results of this study, it can be concluded that, when the longitudinal Dispersion Coefficient as well as the transverse Dispersion Coefficients must be determined in the transient concentration situation, the two-dimensional routing procedures, 2D RP and 2D STRP, can be employed to calculate Dispersion Coefficients among the observation methods. For the steady concentration situation, the STRP can be applied to calculate the transverse Dispersion Coefficient. When the tracer data are not available, either theoretical or empirical equations by the estimation method can be used to calculate the Dispersion Coefficient using the geometric and hydraulic data sets. Application of the theoretical and empirical equations to the laboratory channel showed that equations by Baek and Seo [[3], 2011] predicted reasonable values while equations by Fischer [23] and Boxwall and Guymer (2003) overestimated by factors of ten to one hundred. Among existing empirical equations, those by Jeon et al. [28] and Baek and Seo [6] gave the agreeable values of the transverse Dispersion Coefficient for most cases of natural rivers. Further, the theoretical equation by Baek and Seo [5] has the potential to be broadly applied to both laboratory and natural channels.
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Empirical equation for transverse Dispersion Coefficient based on theoretical background in river bends
Environmental Fluid Mechanics, 2013Co-Authors: Kyong Oh Baek, Il Won SeoAbstract:There are different approaches to estimating the transverse Dispersion Coefficient in river mixing. Theoretical approaches have derived the Dispersion Coefficient from the concept of shear flow, which has dominant effects on the transverse mixing. Empirical approaches have developed an equation using the hydraulic and geometric data of rivers through dimensional analysis and regression techniques. These two equations interact closely with each other. For example, the complicated theoretical equation can be simplified by empirical approaches, and the functional relationships of the empirical equation can be derived from theoretical bases. In this study, a new empirical equation for the transverse Dispersion Coefficient has been developed based on the theoretical background in river bends. As a regression method, the least-square iterative method was used because the equation was a nonlinear model. The estimated Dispersion Coefficients derived by the new equation were compared with observed transverse Dispersion Coefficients acquired from natural rivers and Coefficients calculated by the other existing empirical equations. From a comparison of the existing transverse Dispersion equations and the proposed equation, it appears that the behavior of the existing formula in a relative sense is very much dependent on the flow condition and the river geometry. Moreover, the proposed equation does not vary widely according to variation of flow conditions. Also, it was revealed that the equation proposed in this study becomes an asymptotic curve as the curvature effect increases.
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development of an empirical equation for the transverse Dispersion Coefficient in natural streams
Environmental Fluid Mechanics, 2007Co-Authors: Tae Myoung Jeon, Kyong Oh Baek, Il Won SeoAbstract:In this study, a new empirical equation for the transverse Dispersion Coefficient has been developed based on the hydraulic and geometric parameters in natural streams using a regression technique. First, a total of 32 data sets in 16 streams were collected. Among those sets, 16 sets were used for deriving the new equation, and the other 16 sets were used for verifying the equation. Then, through dimensional analysis, it was found that the normalized transverse Dispersion Coefficient is associated with several parameters such as sinuosity, aspect ratio, and a friction term. The robust least square method was applied to estimate regression Coefficients. The newly proposed equation was proven to be superior in explaining the Dispersion characteristics of natural streams more precisely compared to the existing equations.
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estimation of the longitudinal Dispersion Coefficient using the velocity profile in natural streams
Journal of Hydraulic Engineering, 2004Co-Authors: Il Won Seo, Kyong Oh BaekAbstract:In this study, a theoretical method for predicting the longitudinal Dispersion Coefficient is developed based on the transverse velocity distribution in natural streams. Equations of the transverse velocity profile for irregular cross sections of the natural streams are analyzed. Among the velocity profile equations tested in this study, the beta distribution equation, which is a probability density function, is considered to be the most appropriate model for explaining the complex behavior of the transverse velocity structure of irregular natural streams. The new equation for the longitudinal Dispersion Coefficient that is based on the beta function for the transverse velocity profile is developed. A comparison of the proposed equation with existing equations and the observed longitudinal Dispersion Coefficient reveals that the proposed equation shows better agreement with the observed data compared to other existing equations.
Vijay P Singh - One of the best experts on this subject based on the ideXlab platform.
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Pareto Optimal Multigene Genetic Programming for Prediction of Longitudinal Dispersion Coefficient
Water Resources Management, 2019Co-Authors: Hossien Riahi-madvar, Majid Dehghani, Akram Seifi, Vijay P SinghAbstract:The longitudinal Dispersion Coefficient (Kx) is fundamental to modeling of pollutant and sediment transport in natural rivers, but a general expression for Kx, with applicability in low or high flow conditions, remains a challenge. The objective of this paper is to develop a Pareto-Optimal-Multigene Genetic Programming (POMGGP) equation for Kx by analyzing 503 data sets of channel geometry and flow conditions in natural streams worldwide. In order to acquire reliable data subsets for training and testing, Subset Selection of Maximum Dissimilarity Method (SSMD), rather than the classical trial and error method, was used by a random manipulation of these data sets. A new hybrid framework was developed that integrates SSMD with Multigene Genetic Programming (MGP) and Pareto-front optimization to produce a set of selected dimensionless equations of Kx and find the best equation with wide applicability. The POMGGP-based final equation was evaluated and compared with 8 published equations, using statistical indices, graphical visualization of 95% confidence ellipse, Taylor diagram, discrepancy ratio (DR) distribution, and scatter plots. Besides being simple and applicable to a broad range of conditions, the proposed equation predicted K_x more accurately than did the other equations and can therefore be used for the prediction of longitudinal Dispersion Coefficient in natural river flows.
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predicting longitudinal Dispersion Coefficient in natural streams by artificial neural network
Journal of Hydraulic Engineering, 2005Co-Authors: Vijay P Singh, Gokmen TayfurAbstract:An artificial neural network (ANN) model was developed to predict the longitudinal Dispersion Coefficient in natural streams and rivers. The hydraulic variables [flow discharge (Q) , flow depth (H) , flow velocity (U) , shear velocity (u*) , and relative shear velocity (U∕u*) ] and geometric characteristics [channel width (B) , channel sinuosity (σ) , and channel shape parameter (β) ] constituted inputs to the ANN model, whereas the Dispersion Coefficient ( Kx ) was the target model output. The model was trained and tested using 71 data sets of hydraulic and geometric parameters and Dispersion Coefficients measured on 29 streams and rivers in the United States. The training of the ANN model was accomplished with an explained variance of 90% of the Dispersion Coefficient. The Dispersion Coefficient values predicted by the ANN model satisfactorily compared with the measured values corresponding to different hydraulic and geometric characteristics. The predicted values were also compared with those predicted...
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longitudinal Dispersion Coefficient in single channel streams
Journal of Hydraulic Engineering, 2002Co-Authors: Zhiqiang Deng, Lars Bengtsson, Vijay P Singh, Donald Dean AdrianAbstract:Using a new channel shape equation for straight channels and a more versatile channel shape or local flow depth equation for natural streams a method is developed for prediction of the longitudinal Dispersion Coefficient in single-channel natural streams, including straight and meandering ones. The method involves derivation of a new triple integral expression for the longitudinal Dispersion Coefficient and development of an analytical method for prediction of this Coefficient in natural streams. The proposed method is verified using 70 sets of field data collected from 30 streams in the United States ranging from straight manmade canals to sinuous natural rivers. The new method predicts the longitudinal Dispersion Coefficient, where more than 90% calculated values range from 0.5 to 2 times the observed values. The advantage of the new method is that it is capable of accurately predicting the longitudinal Dispersion Coefficient in single-channel natural streams without using detailed dye concentration test data. A comparison between the new method and the existing methods shows that the new method significantly improves the prediction of the longitudinal Dispersion Coefficient.
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longitudinal Dispersion Coefficient in straight rivers
Journal of Hydraulic Engineering, 2001Co-Authors: Zhiqiang Deng, Vijay P Singh, Lars BengtssonAbstract:An analytical method is developed to determine the longitudinal Dispersion Coefficient in Fischer's triple integral expression for natural rivers. The method is based on the hydraulic geometry relationship for stable rivers and on the assumption that the uniform-flow formula is valid for local depth-averaged variables. For straight alluvial rivers, a new transverse profile equation for channel shape and local flow depth is derived and then the lateral distribution of the deviation of the local velocity from the cross-sectionally averaged value is determined. The suggested expression for the transverse mixing Coefficient equation and the direct integration of Fischer's triple integral are employed to determine a new theoretical equation for the longitudinal Dispersion Coefficient. By comparing with 73 sets of field data and the equations proposed by other investigators, it is shown that the derived equation containing the improved transverse mixing Coefficient predicts the longitudinal Dispersion coefficie...