Effect of background turbulence on an axisymmetric turbulent jet
Bibliographic record
Abstract
Abstract The effect of different levels of background turbulence on the dynamics and mixing of an axisymmetric turbulent jet at different Reynolds numbers has been investigated. Approximately homogeneous and isotropic background turbulence was generated by a random jet array and had a negligible mean flow ( ${\langle {U}_{\alpha } \rangle }/ {u}_{\alpha \mathit{rms}} \ll 1$ ). Velocity measurements of a jet issuing into two different levels of background turbulence were conducted for three different jet Reynolds numbers. The results showed that the mean axial velocities decay faster with increasing level of background turbulence (compared with a jet in quiescent surroundings), while the mean radial velocities increase, especially close to the edges of the jet. Furthermore, the axial root-mean-square velocities of the jet increased in the presence of background turbulence, as did the jet’s width. However, the mass flow rate of the jet decreased, from which it can be inferred that the entrainment into the jet is reduced in a turbulent background. The effect of background turbulence on the entrainment mechanisms is discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".