Numerical Simulation of Turbulent Flow in River Bends and Confluences Using the k-ω SST Turbulence Model and Comparison with Standard and Realizable k-ε Models
Bibliographic record
Abstract
River bends and confluences are critical features in fluvial environments where complex flow patterns, including secondary currents, turbulence, and surface changes, strongly influence sediment transport, river morphology, and water quality. The accurate prediction of these flow characteristics is essential for hydraulic engineering applications. In this study, we present a numerical simulation of turbulent flow in river bends and confluences, with special consideration given to the dynamic interaction between free-surface variations and closed-surface constraints. The simulations were performed using OpenFOAM, an open-source computational fluid dynamics (CFDs) platform, with the k-ω SST (Shear Stress Transport) turbulence model, which is well-suited for capturing boundary layer behavior and complex turbulence structures. The finite volume method (FVM) is used to simulate and examine the behavior of the secondary current in channel bends and confluences. Two sets of experimental data, one with a sharply curved channel and the other with a confluent channel, were used to compare the numerical results and to evaluate the validity of the model. This study focuses on investigating to what extent the k-ω SST turbulence model can capture the effects of secondary flow and surface changes on flow hydrodynamics, analyzing velocity profiles and turbulence effects. The results are validated against experimental data, demonstrating the model’s ability to reasonably replicate flow features under both free- and closed-surface conditions. This study provides insights into the performance of the k-ω SST model in simulating the impact of geometrical constraints on flow regimes, offering a computationally robust and reasonable tool for river engineering and water resources management, particularly in the context of hydraulic structure design and erosion control in curved and confluence regions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".