Reynolds Number Effects on the Near-Exit Region of Turbulent Jets
Bibliographic record
Abstract
In this paper, attention has been focused on the near-exit region of a round turbulent free jet to study the large-scale coherent structures and to document the signatures of the vortices over a range of Reynolds numbers. Particle image velocimeter measurements were conducted at three jet exit Reynolds numbers of 10,000, 30,000, and 55,000. The large-scale structures in the near field (X/D<12) were investigated by performing a proper orthogonal decomposition analysis of the velocity fields. A vortex identification algorithm was complemented by swirling strength maps to identify the vortex centers, rotational sense, size, and circulation of the vortices. The influence of the Reynolds number on the distribution of the number, size, and circulation of the identified vortices was studied. Proper orthogonal decomposition of the velocity fields revealed that Reynolds number has a strong influence on the mean circulation of vortices. The present results show that the axial location where vortices first appear and the number of vortices close to the nozzle exit (X/D<5) are dependent on the Reynolds number.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".