The effect of background turbulence on jet entrainment: an experimental study of a plane jet in a shallow coflow
Bibliographic record
Abstract
Many effluents are released into water bodies or into the atmosphere, and dilution is relied on to minimize the toxic effects of the pollutants on the environment. Dilution occurs due to entrainment and subsequent mixing of the “clean” (or cleaner) ambient fluid and the effluent stream. In the near field, dilution occurs due to momentum or buoyancy driven entrainment into the effluent stream, which is proportional to a characteristic velocity of the jet or plume. Whereas in the far field, dilution occurs due to turbulent diffusion at a rate dependent on the level of turbulence in the receiving fluid. In order to predict the expected dilution of an effluent stream, it is necessary to answer the question, at what point does the dilution mechanism change and how does it affect dilution rates. An experimental study examining the velocity and concentration decay of plane jets released into shallow coflows with increasing levels of external turbulence, indicated that levels of external turbulence just large enough to disrupt the jet structure reduced dilution rates significantly. This indicates that models, in which the jet dilution and turbulent diffusion are superimposed, will not always give a conservative estimate of effluent dilution.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".