Self-preservation scaling of turbulence in free axisymmetric compressible jets
Bibliographic record
Abstract
The influence of compressibility on shear flow turbulence is investigated within a self-preservation framework. This study focuses on the axisymmetric jet to examine compressibility effects in a slowly spatially evolving flow, unlike mixing layers, where the convective Mach number remains constant. Revisiting self-preservation, an a priori description of the compressible scaling for Reynolds stresses and higher-order velocity moments is developed. Turbulence moments are found to scale with powers of the spreading rate, suggesting Reynolds stress anisotropy results from compressibility effects consistent with self-preservation of the governing equations. Particle image velocimetry measurements for Mach 0.3 and perfectly expanded Mach 1.25 jets confirm the scaling predictions. The attenuation function, $\varPhi (M_c)$ , describing the relationship between the convective Mach number, $M_c$ , and the spreading rate, follows a similar trend in jets and mixing layers, where a higher $M_c$ results in reduced spreading rates. In the jet where $M_c$ decays, the relationship between the local $M_c$ and turbulence attenuation remains captured through $\varPhi (M_c)$ , which scales proportionally with the spreading rate. A new scale is introduced, where the pressure in the mean momentum equation is substituted. The difference between the streamwise and radial-Reynolds-normal stresses was found to be a scale which is independent of Mach number and spreading rate. Further analysis of the Reynolds-stress-transport budget shows that internal redistribution of energy occurs within the Reynolds-normal stresses, and the role of pressure modification in turbulence attenuation supports previous observations. These findings confirm that the compressible axisymmetric jet exhibits self-preservation, with scaling extending into supersonic regimes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".