Interactions between cavity flow and main stream skimming flows: an experimental study
Bibliographic record
Abstract
In the last two decades, research on the hydraulics of skimming flows down stepped chutes was driven by needs for better design guidelines. Skimming flows are characterized by significant momentum transfer from the main stream to the recirculation zones. Investigations are difficult because of the complex nature of the flow, the strong flow aeration, and the interactions between entrained air and turbulence. This study provides a comprehensive database on main stream and cavity flow interactions in skimming flows down a stepped chute. Measurements were conducted on a large facility (α = 15.9°, h = 0.05 and 0.1 m, W = 1 m) with precise instrumentation based upon a Froude similitude. Air–water velocity and turbulence measurements demonstrated a well-defined mixing layer developing downstream of each step edge in which the velocity profiles had the same shape as classical monophase flow results. A comparative analysis of air–water flow properties for Froude similar flow conditions showed a good agreement between the two step heights in terms of dimensionless distributions of air content, velocity, and turbulence intensity, but dimensionless bubble count rates, turbulence levels, and bubble chord sizes were improperly scaled.Key words: stepped chute, skimming flow, flow recirculation, momentum exchange, physical modelling.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".