Evaluation of turbulence models for the prediction of flow properties in vegetated channels

The performance of turbulence models was investigated to predict the flow and turbulence features of the vegetated channel using computational fluid dynamics (CFD). The Ansys Fluent, CFD software was implemented for the numerical studies. The flow was three-dimensional, incompressible, steady, and turbulent. Ten turbulence models, provided by Ansys Fluent, were implemented for the comparative study. The numerical model was validated against an experimental study conducted in the literature. The numerical studies show that the Renormalization group k–ε model is the most successful model for predicting the flow characteristics of the vegetated channel with a Root Mean Square Error (RMSE) value of 0.2752. At the same time, the Reynolds Stress Model gives the least successful predictive performance, indicated by an RMSE value of 0.4302. Moreover, the Spalart–Allmaras (S–A) model offers the shortest computation time with a value of 6652.393 s, whereas the Shear Stress Transport k–ω model proves to be the most time-consuming with a value of 11 952.219 s. The velocity of water flow in a channel is not uniform as it is slower at the surface of leaves and faster in the free zones. The maximum velocity is observed in the middle section of the channel, below the leaf, and between the roots with the value of u = 0.1158 m/s. Furthermore, the characteristics of turbulence in a channel are influenced by several factors such as channel geometry, flow velocity, and vegetation distribution. As a result, the presence of vegetation in a channel affects the flow and turbulence characteristics of the water significantly.

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Publication Details

Journal
Physics of Fluids
Published
2024-09-01
DOI
https://doi.org/10.1063/5.0225685
Primary Topic
Hydrology and Sediment Transport Processes
Type
article
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article

Evaluation of turbulence models for the prediction of flow properties in vegetated channels

Rahim Şibil
Physics of Fluids
Hydrology and Sediment Transport Processes
article

Evaluation of turbulence models for the prediction of flow properties in vegetated channels

Rahim Şibil
article en

Abstract

The performance of turbulence models was investigated to predict the flow and turbulence features of the vegetated channel using computational fluid dynamics (CFD). The Ansys Fluent, CFD software was implemented for the numerical studies. The flow was three-dimensional, incompressible, steady, and turbulent. Ten turbulence models, provided by Ansys Fluent, were implemented for the comparative study. The numerical model was validated against an experimental study conducted in the literature. The numerical studies show that the Renormalization group k–ε model is the most successful model for predicting the flow characteristics of the vegetated channel with a Root Mean Square Error (RMSE) value of 0.2752. At the same time, the Reynolds Stress Model gives the least successful predictive performance, indicated by an RMSE value of 0.4302. Moreover, the Spalart–Allmaras (S–A) model offers the shortest computation time with a value of 6652.393 s, whereas the Shear Stress Transport k–ω model proves to be the most time-consuming with a value of 11 952.219 s. The velocity of water flow in a channel is not uniform as it is slower at the surface of leaves and faster in the free zones. The maximum velocity is observed in the middle section of the channel, below the leaf, and between the roots with the value of u = 0.1158 m/s. Furthermore, the characteristics of turbulence in a channel are influenced by several factors such as channel geometry, flow velocity, and vegetation distribution. As a result, the presence of vegetation in a channel affects the flow and turbulence characteristics of the water significantly.

Physics of FluidsVol. 36(9)
Gümüşhane University (TR)
Openalex Percentile: Top 84%
Hydrology and Sediment Transport Processes
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Evaluation of turbulence models for the prediction of flow properties in vegetated channels — Rahim Şibil · Physics of Fluids (2024) | TGRS Research Map | TGRS