Distribution of spanwise enstrophy in the near wake of three symmetric elongated bluff bodies at high Reynolds number
Bibliographic record
Abstract
Three elongated bluff bodies with a chord-to-thickness ratio of seven have been studied experimentally at a Reynolds number based on body thickness of 3 × 104. The defining feature of elongated bluff bodies is the interaction between trailing edge Kármán vortex shedding and leading edge separation-reattachment. We have used particle image velocimetry with different body geometries to investigate this interaction for three distinct cases: (i) small leading edge separation-reattachment length; (ii) large leading edge separation-reattachment length; and (iii) one case in between these bounds. The leading edge separation-reattachment is a significant source of spanwise enstrophy. Thus, changes in the wake enstrophy distribution are of particular interest. We have examined the time-averaged distribution and production of both the turbulent kinetic energy and the spanwise enstrophy in the near wake region utilizing proper orthogonal decomposition on the vorticity field to distinguish between turbulence and the periodic contribution of the trailing edge vortex shedding. A significant increase in the lateral distribution of spanwise enstrophy is observed – exceeding the typical bounds of the near wake – which is due to the leading edge separation-reattachment and the resulting scale of the flow at the trailing edge. As a result, strengthening the leading edge flow, which tends to weaken the trailing edge vortex shedding, may lead to enhanced mixing in the wake.
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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 | 0.001 |
| 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".