Uncertainty in computations of the spread of warm water in a river – lessons from Environmental Impact Assessment case study

Kalinowska, Monika B.; Rowiński, Paweł M.

doi:10.5194/hess-16-4177-2012

Cited by 16 publications

(6 citation statements)

References 30 publications

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“…From this, the diffusion tensor may be written as

D_{ij} = ([], \begin{matrix} k_{1} & k_{ψ} & 0 \\ k_{ψ} & k_{2} & 0 \\ 0 & 0 & D_{zz} \end{matrix})

to take into account nonorthogonality, where

k_{1} = D_{xx} {cos}^{2} ((), ψ) + D_{yy} {sin}^{2} ((), ψ)

k_{2} = D_{xx} {sin}^{2} ((), ψ) + D_{yy} {cos}^{2} ((), ψ)

k_{ψ} = ((), D_{xx} - D_{yy}) cos ((), ψ) sin ((), ψ)

and D xx , D yy , and D zz are the x , y , and z components of diffusion with respect to the direction of flow as presented earlier. Kalinowska and Rowinski () and Arega and Sanders () use a similar mechanism to handle anisotropic diffusion within river and coastal flow, respectively. D zz needs no rotation, as it is perpendicular to the xy ‐plane.…”

Section: Modeling Frameworkmentioning

confidence: 99%

A CFD‐Based Mixing Model for Vegetated Flows

Sonnenwald

Guymer

Stovin

2019

Water Resources Research

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This paper provides a computational fluid dynamics (CFD)‐based modeling framework for predicting flow field, turbulence, and mixing characteristics within vegetated environments such as ponds and wetlands. The framework has been implemented within a commercial CFD code—ANSYS Fluent 19—via a set of user‐defined functions. Following the approach outlined by King et al. (2012, https://doi.org/10.1017/jfm.2012.113), the standard k‐ε turbulence closure model has been modified to capture the energy transfer at the vegetation/clear flow shear interface and within the vegetation. The implementation assumes that vegetation is vertical, but nonorthogonal flow in the horizontal plane is accounted for. Values for the drag coefficient and the mixing coefficients are estimated based on the vegetation stem diameter and density. Following Tanino and Nepf (2008, https://doi.org/10.1017/S0022112008000505), a switch has been incorporated to account for the fact that the relevant length scale changes from stem diameter to stem spacing as stem density increases. A set of model parameters is proposed, based on a reevaluation of previously published laboratory data and theoretical analysis. Five different experimental data sets are used to demonstrate that the model is able to predict mixing within fully vegetated systems and due to both vertical and horizontal shear layers. The framework was developed to provide a practical prediction tool for engineering purposes, in particular for the estimation of residence time distributions in real partially vegetated stormwater management ponds. Its implementation here within a commercial CFD package potentially facilitates application to complex pond geometries, including patches of different types of vegetation with different bulk stem diameter and density characteristics.

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“…From this, the diffusion tensor may be written as

D_{ij} = ([], \begin{matrix} k_{1} & k_{ψ} & 0 \\ k_{ψ} & k_{2} & 0 \\ 0 & 0 & D_{zz} \end{matrix})

to take into account nonorthogonality, where

k_{1} = D_{xx} {cos}^{2} ((), ψ) + D_{yy} {sin}^{2} ((), ψ)

k_{2} = D_{xx} {sin}^{2} ((), ψ) + D_{yy} {cos}^{2} ((), ψ)

k_{ψ} = ((), D_{xx} - D_{yy}) cos ((), ψ) sin ((), ψ)

Section: Modeling Frameworkmentioning

confidence: 99%

A CFD‐Based Mixing Model for Vegetated Flows

Sonnenwald

Guymer

Stovin

2019

Water Resources Research

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“…We analyse the influence of the chosen initial conditions on the prediction of river temperature increase, namely different values of the river flow and different values of the discharged heated water temperature. Note, however, that other input coefficients and parameters in the heat transfer equation may also be important and may influence the obtained results to a large extent (see [1,2,3]).…”

Section: Introductionmentioning

confidence: 99%

“…The twodimensional (2D) RivMix model has been used to simulate the temperature distribution. This model is based upon the depth-averaged heat transport equation in which a non-diagonal dispersion tensor with four dispersion coefficients is taken into account (see details in [1,2,3,6,7,8]. Crucial input values, namely the depth-averaged velocities and the water depths distributions for the given river flow, are obtained using the 2D depth-averaged turbulent open channel flow model CCHE2D [9,10,11].…”

Section: Introductionmentioning

confidence: 99%

Impact of initial conditions on the prediction of the spread of thermal pollution in rivers

2018

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The influence of initial conditions on the prediction of the increase of river temperature below the point of release of heated water for a designed power plant has been analysed in this study. The results for different assumed values of river flow and different temperatures of the discharged heated water have been presented. The results have been analysed taking into account existing legal frames. The two-dimensional inhouse RivMix model has been used to simulate the temperature distribution whereas the two-dimensional depth-averaged turbulent open channel flow model CCHE2D has been used to simulate the velocity fields and the water depths for the selected flows of the river.

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“…Our primary focus is the determination of the longitudinal dispersion coefficients (D L ) and their dependence on the vegetation coverage. These coefficients are, in fact, the most important and the most difficult to determine factors characterising the mixing processes (Czernuszenko, 1990, Kalinowska andRowiński, 2012).…”

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confidence: 99%

Influence of vegetation maintenance on flow and mixing: case study comparing full cut with high-coverage conditions

Kalinowska

Västilä

Nones

et al. 2022

Preprint

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Abstract. In temperate climates, agricultural ditches are generally bounded by seasonal vegetation, which affects the hydrodynamics and mixing processes within the channel and acts as a buffer strip to reduce a load of pollutants coming from the surrounding cultivated fields. However, even if the control of such vegetation represents a key strategy to support sediment and nutrient management, the studies that investigated the effect of different vegetation maintenance scenarios or vegetation coverage on the flow and mixing dynamics at the reach scale are very limited. To overcome these limitations and provide additional insights on the involved processes, tracer tests were conducted in a 500 meters long agricultural ditch close to Warsaw in Poland, focusing on two different vegetation scenarios: highly vegetated and fully cut. Additionally, under the highly vegetated scenario, sub-reaches differing in surficial vegetation coverage are analysed separately to understand better the influence of the vegetation conditions on the flow and mixing parameters. Special attention has been paid to the longitudinal dispersion coefficient in complex natural conditions and its dependency on vegetation coverage (V). The vegetation maintenance decreased the travel and residence times of the solute by 3–5 times, moderately increasing the peak concentrations. We found that the dispersion coefficient decreased approximately linearly with the increase of vegetation coverage at V>68 %. Further research is needed at lower vegetation coverage values and different spatial plant distributions. The obtained longitudinal dispersion coefficient values complement the previously published data, which are barely available for small natural streams. The new process understanding supports the design of future investigations with more environmentally sound vegetation maintenance scenarios.

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Uncertainty in computations of the spread of warm water in a river – lessons from Environmental Impact Assessment case study

Cited by 16 publications

References 30 publications

A CFD‐Based Mixing Model for Vegetated Flows

A CFD‐Based Mixing Model for Vegetated Flows

Impact of initial conditions on the prediction of the spread of thermal pollution in rivers

Influence of vegetation maintenance on flow and mixing: case study comparing full cut with high-coverage conditions

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