Effects of Reynolds number on some properties of a turbulent jet from a long square pipe
Bibliographic record
Abstract
The flow in the near-to-intermediate field of a jet emanating from a long square pipe is investigated using hot-wire anemometry. The data include distributions of the mean and high order turbulence moments over 8000 < Re < 50 000 along the jet centreline. It is demonstrated that the far-field rates of the mean velocity decay and spread, as well as the asymptotic value of the streamwise turbulent intensity, all decrease as Re increases for Re ≤ 30 000; however, they become approximately Re-independent for Re > 30 000. It follows that the critical Reynolds number should occur at Recr = 30 000. Attention is given to the exponents associated with the compensated axial velocity spectra that show that the inertial subrange calculated according to isotropic, homogeneous turbulence emerges at x/De = 30 for all Re; however, if the scaling exponent is altered from m = −5/3 to between −1.56 < m < −1.31 the “inertial” range emerges at lower values of Re and the exponent is Re dependent. It is also found that the exponent agrees very well with Mydlarski and Warhaft correlation m = (5/3)(1−3.15Rλ−2/3), where Rλ is the Taylor Reynolds number, obtained for turbulence decay behind a grid.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".