Influence of bed proximity on the three-dimensional characteristics of the wake of a sharp-edged bluff body
Bibliographic record
Abstract
The gap flow effect in a wake is investigated to develop an improved picture of the formation of fluid structures via a numerical simulation of flow past a bluff body with two different clearances from the bed. These two cases are compared with the no-gap case which is considered as a reference case. The transient three-dimensional Navier-Stokes equations are numerically solved using a finite volume approach with the detached eddy simulation as the turbulence model. The effect of the free surface is included in the model by using the volume of fluid method. The fluid structures that are generated in the wake are identified using the λ2-criterion. It is found that the gap flow influences the formation of various types of fluid structures in the wake region. The horseshoe vortex appears to be attenuated as the gap size increases. The turbulent structures at the core of the wake appear to be disorganized and have the ability to extend further in the transverse direction in the absence of the horseshoe vortex. A new structure is identified in the wake flow when the gap size exceeds a threshold value. This structure acts to enhance the positive wall-normal velocity and accelerate the restoration of the free surface to its original position at shorter distances downstream of the bluff body. The lateral entrainment to the wake region is also enhanced as the gap is introduced in the wake flow.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".