Longitudinal dispersion in waterways with vegetated floodplain

Hamidifar, Hossein; Omid, M H; Keshavarzi, Alireza

doi:10.1016/j.ecoleng.2015.09.048

Cited by 29 publications

(17 citation statements)

References 52 publications

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“…Nonadsorptive solute dye tracer carmine was discharged at 10.54 mL/s in all tests. Solute concentrations were analyzed at six sections P1-P6 (x = [0, 5,10,15,20,25] cm) based on the image processing technology (Fig. 1b).…”

Section: Methodsmentioning

confidence: 99%

“…However, opposite trends appeared in the predicted results directly using Eqs. (4)(5)(6), showing the negative relationship between D y /(u•d) and R ed . Equations (4-6) were developed to estimate the lateral diffusion coefficient affected by a rigid vegetation [10], which was proved to be not appropriate for flows with a flexible vegetation.…”

Section: Influence Of Flexible Vegetation Bending On Lateral Diffusion Coefficient D Ymentioning

confidence: 99%

“…Aquatic vegetation, especially flexible vegetation such as reed and bitter grass, is an important part of the river ecosystem. Vegetation can reduce flow velocity, change the turbulence structure, and affect the process of solute transport [2,5,8,9]. Interactions between flow-vegetation-solute are extremely complicated which are affected by vegetation features and hydrodynamic conditions [11].…”

Section: Introductionmentioning

confidence: 99%

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Laboratory study of the effects of flexible vegetation on solute diffusion in unidirectional flow

et al. 2021

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Background Flexible vegetation is an important part of the riverine ecosystem, which can reduce flow velocity, change turbulence structure, and affect the processes of solute transport. Compared with the flow with rigid vegetation, which has been reported in many previous studies, bending of flexible vegetation increases the complexity of the flow–vegetation–solute interactions. In this study, laboratory experiments are carried out to investigate the influence of flexible vegetation on solute transport, and methods for estimating the lateral and longitudinal diffusion coefficients in the rigid vegetated flow are examined for their applications to the flow with flexible vegetation. Results The experimental observations find that vegetation can significantly reduce flow velocity, and the Manning coefficient increases with increasing vegetation density and decreases with inflow discharge. Under all the cases, the vertical peak of the solute concentration moves towards the bottom bed along the flow, and the values of vertical peak concentration longitudinally decreases from the injection point. The lateral diffusion coefficients Dy increase with vegetation density, while the longitudinal diffusion coefficients DL are opposite. Both Dy and DL increase with the inflow discharge. To estimate the Dy and DL in the flow with flexible vegetation, an effective submerged vegetation height considering vegetation bending is incorporated in the methods proposed for flow with rigid vegetation (Lou et al. Environ Sci Eur 32:15, 2020). The modified approach can well predict the diffusion coefficients in the experiments with the relative errors in the range of 5%–12%. Conclusions The methods proposed in this study can be used to estimate the lateral and longitudinal diffusion coefficients in flows through both rigid and flexible vegetations using the effective submerged vegetation height.

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Section: Methodsmentioning

confidence: 99%

Section: Influence Of Flexible Vegetation Bending On Lateral Diffusion Coefficient D Ymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Laboratory study of the effects of flexible vegetation on solute diffusion in unidirectional flow

et al. 2021

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“…Longitudinal dispersion coefficient calculation Although many researchers extensively studied longitudinal dispersion coefficients in simple-shape channels, the flow conditions in the more complex geometries are entirely different than those in simple channels (Hamidifar et al, 2015). Longitudinal dispersion is a complicated transport process, which is highly responsive to any hydrological, hydraulic, or vegetation change (Taldei et al, 2010).…”

Section: Experimental Designmentioning

confidence: 99%

Modelling Seasonal Changes of Longitudinal Dispersion in the Okna River

Manina¹,

Halaj²,

Jurík³

et al. 2020

Acta Sci. Pol., Formatio Circumiectus

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Aim of study The problem of investigation of pollution transport along watercourses is not new, but since it is so complex there is still space for fine-tuning in order to apply it to individual watercourses. This study presents an opportunity to determine a value of longitudinal dispersion and is focused on comparing seasonal variation of the longitudinal dispersion predetermined by natural conditions of the river aquatic zone. Material and methods The dispersion of a pollutant tracer was simulated by HEC-RAS model and by one-dimensional advection-diffusion equation. Longitudinal dispersion coefficient was determined based on of tracer experiments (2 kg NaCl in 10 l H 2 O) carried out in March, August, and October 2019. Both methods were applied to the Okna River in the Eastern Slovak Lowland. Result and conclusions Result showed that HEC-RAS modelling correlated to changes of dispersion and vegetation in the river. During the year, the longitudinal dispersion coefficient for Okna river estimated by the equation remained in a range from 0.093 to 1.08 m 2 • s-1 , and when estimated using the HEC-RAS, from 0.220 to 1.850 m 2 • s-1. Results could represent different values of longitudinal dispersion simulated by the model or the equation. The obtained coefficients have a broad range of applications. The main use of these is as input data in the simulation of the spread of accidental pollution in rivers. Also, they can be applied to streams with similar characteristics. Results demonstrated an important role of seasonal variation of longitudinal dispersion coefficient that must be taken into account in the simulation of pollution spreading in rivers.

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“…It is found that functions such as exponential, rational and polynomial curve functions are used in literature to fit the magnetizing characteristic data points [16]- [24]. Soft computing techniques such as feed forward multilayer ANN networks are also developed to model saturation in electrical machines [25]- [27].…”

Section: Introductionmentioning

confidence: 99%

Modeling of the magnetizing phenomena of doubly fed induction generator using neuro-fuzzy algorithm considering non-linearity

Jose¹,

Chattopadhyay²

2019

IJECE

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Doubly fed Induction Generators (DFIGs) are quite common in wind energy conversion systems because of their variable speed nature and the lower rating of converters. Magnetic flux saturation in the DFIG significantly affect its behavior during transient conditions such as voltage sag, sudden change in input power and short circuit. The effect of including saturation in the DFIG modeling is significant in determining the transient performance of the generator after a disturbance. To include magnetic saturation in DFIG model, an accurate representation of the magnetization characteristics is inevitable. This paper presents a qualitative modeling for magnetization characteristics of doubly fed induction generator using neuro-fuzzy systems. Neuro-fuzzy systems with one hidden layer of Gaussian nodes are capable of approximating continuous functions with arbitrary precision. The results obtained are compared with magnetization characteristics obtained using discrete fourier transform, polynomial and exponential curve fitting. The error analysis is also done to show the effectiveness of the neuro fuzzy modeling of magnetizing characteristics. By neuro-fuzzy algorithm, fast learning convergence is observed and great performance in accuracy is achieved.

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Longitudinal dispersion in waterways with vegetated floodplain

Cited by 29 publications

References 52 publications

Laboratory study of the effects of flexible vegetation on solute diffusion in unidirectional flow

Laboratory study of the effects of flexible vegetation on solute diffusion in unidirectional flow

Modelling Seasonal Changes of Longitudinal Dispersion in the Okna River

Modeling of the magnetizing phenomena of doubly fed induction generator using neuro-fuzzy algorithm considering non-linearity

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