Bibliographic record
Abstract
The wakes of wishbone, doublet, and ramp-type vortex generators (VGs) were investigated. The VGs were placed in the thin laminar boundary layer of a flat plate at a Reynolds number of 930 based on the freestream velocity and VG height. The turbulence statistics of the wake were measured with high spatial resolution using planar particle image velocimetry (PIV) and stereoscopic PIV. Three-dimensional time-resolved tomographic PIV was also carried out to visualize the evolution of vortices. The fastest recovery of the wake deficit was observed for the wishbone VG. The peak of turbulence production in the wake of the wishbone and doublet VGs had a similar magnitude and was 1.5 times stronger than that of the ramp VG. The hairpin vortices generated by the ramp VG formed the largest percentage of the wake turbulent kinetic energy, and their size is about half of the hairpins produced by the wishbone and doublet VGs. The wishbone and ramp VGs had the best overall performance. The wishbone VG generated the strongest mixing in the wake region, whereas the ramp VG had the smallest drag coefficient. The doublet VG had the weakest overall performance due to low mixing and the largest drag.
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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".