Empirical equation for transverse dispersion coefficient based on theoretical background in river bends

Baek, Kyong Oh; Seo, Il Won

doi:10.1007/s10652-013-9276-5

Cited by 15 publications

(23 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, the estimated dispersion coefficients through a tracer experiment are valid for only the examined stream segment (Abderrezzak et al 2015). Consequently, numerous studies were conducted to develop empirical formulas for the determination of both the longitudinal and transverse dispersion coefficients (Fischer 1967 Seo and Cheong 1998;Baek and Seo 2013), or sometimes the coefficients are achieved by researcher's own experience (Velísková et al 2014). Detailed and brief reviews and comparison of these empirical formulas can be found in Seo and Cheong (1998) and Baek and Seo (2016).…”

Section: Introductionmentioning

confidence: 99%

Estimation of river Tigris dispersivities using a steady-state numerical model

Ismail

Muntasir

2018

Appl Water Sci

View full text Add to dashboard Cite

The present work suggests a simple approach for simulating the dispersion of pollutants in rivers and streams with the aid of computational fluid dynamics technique. The two-dimensional advection-dispersion equation was solved using FlexPDE code to predict the biochemical oxygen demand (BOD) and the total dissolved solids (TDS) concentrations at the confluence of Diyala with Tigris Rivers. In this model, two sets of data were used for calibration and validation processes. Various values of longitudinal and transverse dispersion coefficients were adopted in the model to determine the suitable value using the trial-and-error method. The results revealed that the best agreement between simulated and measured values was observed when the longitudinal and transverse dispersion coefficients are close to 10 and 5 m 2 /s, respectively. The results showed that the proposed methodology is more suitable for TDS, while for BOD, it needs an accurate representation of the chemical or biological transformations.

show abstract

Section: Introductionmentioning

confidence: 99%

Estimation of river Tigris dispersivities using a steady-state numerical model

Ismail

Muntasir

2018

Appl Water Sci

View full text Add to dashboard Cite

show abstract

“…theoretical and empirical. The theoretical approach deals with the concept of shear flow while the empirical one uses channels hydraulic and geometric properties in order to establish a relationship for E z (Baek and Seo 2013). Many investigators attempted to estimate the TMC by either the theoretical approach (e.g.…”

mentioning

confidence: 99%

“…Many investigators attempted to estimate the TMC by either the theoretical approach (e.g. Baek and Seo 2013) or the empirical approach (e.g. Chau…”

mentioning

confidence: 99%

“…Azamathulla and Ahmad 2012; and meandering ones (e.g. Baek and Seo 2013;Yotsukura and Sayre 1976) while use of field data sets may not be straightforward due to the scarcity of field data (Beltaos 1979). Obtaining a general formula for estimating the TMC provides an opportunity to characterize the mixing mechanism while lowering the needs for time-consuming tracer studies that are costly (Etemad-Shahidi and Taghipour 2012).…”

mentioning

confidence: 99%

“…As a result, many researchers have focused on providing formulae for estimation of this coefficient (e.g. Azamathulla and Ahmad 2012;Baek and Seo 2013;Elder 1959;Fischer et al 1979;Jeon et al 2007;Yotsukura and Sayre 1976). Although, the previous studies have provided invaluable knowledge about the role of different parameters, some issues still remain unsolved.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Estimation of Transverse Mixing Coefficient in Straight and Meandering Streams

Aghababaei

Etemad‐Shahidi

Jabbari

et al. 2017

Water Resour Manage

View full text Add to dashboard Cite

Transverse mixing coefficient (TMC) is one of the key factors in the modelling of lateral dispersion of pollutants. Several researchers have attempted to estimate this coefficient using various models. However, robust equations that can accurately estimate lateral mixing in both straight and meandering streams are still required. In this study, novel formulae were developed using the hydraulic and geometric parameters of rivers. The multiple linear regression (MLR), genetic programming based symbolic regression (GPSR) and dimensionless parameters were used for this purpose. Two extensive data sets including data from straight channels/streams and meandering ones were employed to develop the formulae. The main advantage of the developed formula for meandering streams is proper consideration of the effects of aspect ratio, friction, and sinuosity. The formulae performances were then compared quantitatively with those of existing ones using accuracy metrics such as RMSE (Root Mean Square Error). The results illustrated that the proposed formulae outperform others in terms of accuracy and can be used for estimating TMC in straight and meandering streams. In addition, the comparison of MLR and GPSR models showed that the latter is marginally more accurate than MLR specially in meandering streams. However, the MLR models presented a more justifiable relationship between the TMC and governing dimensionless parameters. The main advantages of the presented formulae are that they are more accurate than previous models, can be used in both meandering and straight streams; and can be easily implemented in numerical models to estimate the pollutant concentration and mixing length

show abstract