Influence of Negatively Buoyant Jets on a Strongly Curved Open-Channel Flow Using RANS Models with Experimental Data
Bibliographic record
Abstract
Experimental and numerical studies of flow structures in a strongly curved 135-degree laboratory flume were carried out to investigate the influence of negatively buoyant jets using the finite volume method. The performance results of three different turbulence models were investigated by comparing the numerical results with the experimental measurements. The present study demonstrates that fully 3D numerical models are capable of simulating the primary flow pattern in a strongly curved channel with the presence of a negatively buoyant jet. The comparison also shows that the k-omega SST model can satisfactorily predict some of the smaller flow features in bend flow, such as the inner bank circulation cell and the overall form of the vorticity distributions. It was found that the flow distribution and the strength of secondary flow vary due to the interaction between the jet mixing behavior and the secondary flow in the channel bend. The presence of negatively buoyant jets attenuated the development of the outer bank cell as salinity increased. In the inner bank region, flow separation was strengthened by the participation of the negatively buoyant jets.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".