The Maximum Skin Friction and Flow Field of a Planar Impinging Gas Jet
Bibliographic record
Abstract
The maximum skin friction and flow field are experimentally measured on a planar impinging gas jet using oil film interferometry (OFI) and particle image velocimetry (PIV), respectively. A jet nozzle width of W = 15 mm, impingement ratios H/W = 4, 6, 8, 10, and a range of jet Reynolds numbers Rejet = 11,000–40,000 are tested to provide a parametric map of the maximum skin friction. The maximum skin friction predictions of Phares et al. (2000, “The Wall Shear Stress Produced by the Normal Impingement of a Jet on a Flat Surface,” J. Fluid Mech., 418, pp. 351–375) for plane jets agree within 5% of the current OFI results for H/W = 6, but deviates upward of 28% for other impingement ratios. The maximum skin friction is found to be less sensitive to changes in the impingement ratio when the jet standoff distance is roughly within the potential core length of the jet. PIV measurements show turbulence transition locations moving toward the nozzle exit with increasing Reynolds number, saturation in the downstream evolution of the maximum axial turbulence intensity before reaching a maximum peak upon impingement, followed by sudden damping at the plate surface. As the flow is redirected, there is an orthogonal redistribution of the fluctuating velocity components, and local peaks in both the axial and transverse turbulence intensity distributions at the plate locations of the maximum skin friction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".