Flows over surface-mounted bluff bodies with different spanwise widths submerged in a deep turbulent boundary layer
Bibliographic record
Abstract
The spatio-temporal dynamics of separation bubbles induced by surface-mounted bluff bodies with different spanwise widths and submerged in a thick turbulent boundary layer is experimentally investigated. The streamwise extent of the bluff bodies is fixed at 2.36 body heights and the spanwise aspect ratio ( $AR$ ), defined as the ratio between the width and height, is increased from 1 to 20. The thickness of the upstream turbulent boundary layer is 4.8 body heights, and the dimensionless shear and turbulence intensity evaluated at the body height are 0.23 % and 15.8 %, respectively, while the Reynolds number based on the body height and upstream free-stream velocity is 12 300. For these upstream conditions and limited streamwise extent of the bluff bodies, two distinct and strongly interacting separation bubbles are formed over and behind the bluff bodies. A time-resolved particle image velocimetry is used to simultaneously measure the velocity field within these separation bubbles. Based on the dynamics of the mean separation bubbles over and behind the bluff bodies, the flow fields are categorized into three-dimensional, transitional and two-dimensional regimes. The results indicate that the low-frequency flapping motions of the separation bubble on top of the bluff body with $\mathit{AR}=1$ are primarily influenced by the vortex shedding motion, while those with larger aspect ratios are modulated by the large-scale streamwise elongated structures embedded in the oncoming turbulent boundary layer. For $\mathit{AR}=1$ and 20, the flapping motions in the wake region are strongly influenced by those on top of the bluff bodies but with a time delay that is dependent on the $AR$ . Moreover, an expansion of the separation bubble on the top surface tends to lead to an expansion and contraction of separation bubbles in the wake of $\mathit{AR}=20$ and 1, respectively. As for the transitional case of $\mathit{AR}=8$ , the separation bubbles over and behind the body are in phase over a wide range of time difference. The dynamics of the shear layer in the wake region of the transitional case is remarkably more complex than the limiting two-dimensional and three-dimensional configurations.
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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".