Bistability of turbulent flow in open-channel expansion: Characterization and suppression
Bibliographic record
Abstract
Three-dimensional turbulent bistable flow (TBF) in an open-channel expansion is predicted using large eddy simulation. The free surface of TBF is tracked using the volume of fluid method, coupled with the level-set method. This paper aims to reveal the ensemble-average flow characteristics and explore effective ways to control bistability. For a given condition of flow approaching an expansion, either of two stable flow states can possibly occur, depending on the flow history. The predicted pressure field agrees well with experimental data. The velocity field is decomposed into deformation regions and eddy-rotation regions using the Okubo–Weiss parameter. Turbulent eddies initiated by shear instability dominate those associated with sidewall-friction force; this condition is responsible for the occurrence of bistability. Fitting a simple hump at a flat-bottom expansion is an effective way to suppress bistability. The presence of the hump shrinks eddy cores and breaks the interaction between eddies triggered by instabilities and eddies induced by friction forces; the result is an increase in flow uniformity and control of turbulence, flow separation, and vortex behavior.
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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 | 0.000 |
| 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) | 0.000 | 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".