Review and Comparison of Numerical Simulations of Secondary Flow in River Confluences
Bibliographic record
Abstract
River confluences are a common feature in natural water resources. The flow characteristics in confluences are complicated, especially at junction areas between tributaries and the main river. One of the typical characteristics of confluences is secondary flow, which plays an important role in mixing, velocity, sediment transport, and pollutant dispersion. In addition to the experimental and field studies that have been conducted in this area, the development of computational fluid dynamics has allowed researchers in this field to use different numerical models to simulate turbulence properties in rivers, especially secondary flows. Nowadays, the hydrodynamics of flows in confluences are widely simulated by using three-dimensional models in order to fully capture the flow structures, as the flow characteristics are considered to be turbulent and three-dimensional at river junctions. Several numerical models have been recommended for this purpose, and various turbulence models have been used to simulate the flows at confluences. To assess the accuracy of turbulence models, flows have been predicted by applying different turbulence models in the numerical model and the results have been compared with other data, such as field, laboratory, and experimental data. The purpose behind these investigations was to find the suitable model for each case of turbulent flow and for different types of confluences. In this study, the performances of turbulence models for confluences are reviewed for different numerical simulation strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".