Mechanism of formation of wake patterns behind two staggered in-phase pitching foils
Bibliographic record
Abstract
The mechanism of wake formation behind two staggered in-phase pitching foils is numerically investigated over a range of Strouhal numbers (0.15<St<0.4), Reynolds numbers (1000<Re<10000), and horizontal (1c<h<3c) and vertical (0.5c<v<2c) foil separation distances, wherecis the foil chord. First, two flow patterns are identified, constituting merged and separated wakes. These are characterized in wake maps to explain the role of geometric and kinematic parameters in the wake formation. Relevant geometric thresholds for the formation of different wake topologies are identified, and their correspondence to kinematics is explained. This provides a wake evolution mechanism that explains the formation of these patterns. This mechanism is triggered by the interaction of upper and lower wakes, which results in the rearrangement of vortex pairs downstream. The interaction and resultant flow patterns change with the offset position of the upper wake, which is given by the horizontal and vertical placement of the upper foil. A wide range of separation distances in staggered foils enables, for the first time, the study of interactions between an external vortex and a vortex street. This allows the mechanisms of wake pattern formation triggered by this interaction to be explored. A novel wake model is proposed to explain this interaction, which consistently holds for different Reynolds numbers.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".