Mean characteristics of fluid structures in shallow-wake flows
Bibliographic record
Abstract
An enhanced picture of the formation of fluid structures including the effect of the free surface and the bed is captured via a numerical solution of flow past a bed mounted bluffbody. A numerical comparison for the fluid structures observed in a time-averaged field of the shallow-wake flow behind a bluffbody utilizing the rigid-lid assumption and volume of fluid method is carried out in this study to bring forth the shortcomings in previous studies. The transient three-dimensional governing Navier–Stokes equations are computationally solved using a finite volume technique with Detached Eddy Simulation as a turbu- lence model. A flat wave model is used with the volume of fluid method to simulate the free surface of the water-air interface. The flow structures in the shallow-wake are qualitatively identified using the λ2 -criterion. In addition to the well-known features present in traditional shallow flows, other distinc- tive features are captured at the near-bed and near-surface locations. The formation of these structures and their dynamics are addressed in this paper. It is found that use of the volume of fluid method, which provides a more accurate representation of the free surface, has a significant impact on the structures’ de- velopment at the near-surface location in the far-wake and on the streamtraces in front of the bluffbody. Unique streamwise oriented structures are captured in the near-bed and free surface, which transport the fluid particles from the centre of the wake to the outer region. To the contrary, the fluid structures with the rigid-lid assumption are not completely developed near the free surface.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".