ESTIMATING THE FRICTION VELOCITY IN A TURBULENT PLANE WALL JET OVER A TRANSITIONALLY ROUGH SURFACE
Bibliographic record
Abstract
In this paper, a power law velocity profile is used to estimate the friction velocity in a plane turbulent wall jet on a rough surface based on experimental data collected by laser Doppler anemometry (LDA). The Reynolds number based on the slot height and exit velocity of the jet was approximately Re = 7500. A 36-grit sheet was used as the rough surface, creating a transitionally rough flow regime (44 < ks+ < 70). Power law velocity profiles were fitted using both inner and outer scales. The power-law coefficients were obtained by applying the expressions proposed by Seo and Castillo (2004) and Brzek et al. (2009) for rough boundary layer flows. According to the present results, the power-law velocity profiles are in good agreement with the conventional logarithmic profile in the overlap region. The power law coefficients can also be used to evaluate a theoretical expression for the skin friction coefficient. The skin friction coefficient for the rough surface turns out to be substantially greater than that for a wall jet on a smooth surface.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".