Effects of external intermittency and mean shear on the spectral inertial-range exponent in a turbulent square jet
Bibliographic record
Abstract
This study investigates by experiment the dependence of the inertial-range exponent m of the streamwise velocity spectrum on the external intermittency factor γ (≡ the fraction of time the flow is fully turbulent) and the mean shear S in a turbulent square jet. Velocity measurements were made using hot-wire anemometry in the jet at 15 < x/D(e) < 40, where D(e) denotes the exit equivalent diameter, and for an exit Reynolds number of Re = 50,000. The Taylor microscale Reynolds number R(λ) varies from about 70 to 450 in the present study. The TERA (turbulent energy recognition algorithm) method proposed by Falco and Gendrich [in Near-Wall Turbulence: 1988 Zoran Zariç Memorial Conference, edited by S. J. Kline and N. H. Afgan (Hemisphere Publishing Corp., Washington, DC, 1990), pp. 911-931] is discussed and applied to estimate the intermittency factor from velocity signals. It is shown that m depends strongly on γ but negligibly on S. More specifically, m varies with γ following m=m(t)+(lnγ(-0.0173))(1/2), where m(t) denotes the spectral exponent found in fully turbulent regions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".