Effect of the free-stream turbulence on the bi-modal wake dynamics of square-back bluff body
Bibliographic record
Abstract
The effect of a free-stream turbulence intensity level on the wake dynamics of a square-back Ahmed body is modeled using the improved delayed detached eddy simulation at Re=9.6×104. The center of pressure, pressure gradient on the base surface, and the barycenter of the momentum deficit on the wake plane are analyzed to characterize the wake bi-modality dynamics. Given that different flow dynamics have different dominant frequencies, the spectral proper orthogonal decomposition is utilized to separate the wake bi-stability, pumping motion of the whole recirculation region, the Von Kármán vortex shedding and the shear layer instability. The results show that entrainment of the oncoming flow into the wake is enhanced, the vorticity thickness is thickened and the length of the wake recirculation region is decreased with the increasing free-stream turbulence, resulting in a lower base suction pressure and a higher level of shear stress. The frequency of the pumping motion is increased with the increase in the oncoming turbulence intensity, while the frequency of Von Kármán vortex shedding is irrespective of the level of the background turbulence. Though the correlation between the switching rate and the oncoming turbulence intensity cannot be put forward due to the relatively short numerical simulation time compared with the wind tunnel experiment, it is still known that the turbulence intensity has a positive effect on the wake bi-stability switching.
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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) | 0.001 | 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".