Evaluation of Reynolds-averaged Navier-Stokes turbulence models in open channel flow over salmon redds
| Authors |
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|---|---|
| Publication date | 08-2024 |
| Journal | Journal of Hydrodynamics |
| Volume | Issue number | 36 | 4 |
| Pages (from-to) | 741-756 |
| Number of pages | 16 |
| Organisations |
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| Abstract |
This study evaluates computational fluid dynamics (CFD) turbulence
closures for Reynolds-averaged Navier-Stokes (RANS) equations against
experimental data to model complex open channel flows, like those
occurring over dune-shaped salmon spawning nests called “redds”. Open
channel flow complexity, characterized by near-bed turbulence, adverse
pressure, and free surfaces, requires suitable turbulence closure
capable of capturing the flow structure between streambed and water
surface. We evaluated three RANS models: Standard k - ω, shear-stress transport (SST) k - ω and realizable k - ε, along with four wall treatments for the realizable k - ε:
Standard, and scalable wall functions, enhanced wall treatment, and an
unconventional closure combining standard wall function with near-wall
mesh resolving the viscous sublayer. Despite all models generally
capturing the bulk flow characteristics, considerable discrepancies were
evident in their ability to predict specific flow features, such as
flow detachments. The realizable k - ε model, with standard wall
function and mesh resolving viscous sublayer, outperformed other
closures in predicting near-wall flow separations, velocity fields, and
free surface elevation. This realizable k - ε model with a
log-layer resolved mesh predicted the free surface elevation equally
well but lacked precision for near-wall flows. The SST k - ω
model outperformed in predicting turbulent kinetic energy and provided
better predictions of the near-boundary velocity distributions than
realizable k - ε closure with any of the conventional wall treatments but overestimated the separation vortex magnitude. The standard k - ω
model also overestimated near-wall separation. This study highlights
the variability in accuracy among turbulence models, underlining the
need for careful model selection based on specific prediction regions.
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| Document type | Article |
| Language | English |
| Published at | https://doi.org/10.1007/s42241-024-0051-5 |
| Other links | https://www.scopus.com/pages/publications/85204453644 |
| Downloads |
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