Numerical Study of Turbulent Spot Development in a Separated Shear Layer
Bibliographic record
Abstract
The development of turbulent spots in a separation bubble under elevated freestream turbulence levels is examined through direct numerical simulation. The flow Reynolds number, freestream turbulence level, and streamwise pressure distribution are typical of the conditions encountered on the suction side of low-pressure turbine blades of gas-turbine engines. Based on the simulation results, the spreading and propagation rates of the turbulent spots and their internal structure are documented, and comparisons are made to empirical correlations that are used for predicting the transverse growth and streamwise propagation characteristics of turbulent spots. The internal structure of the spots is identified as a series of vortex loops that develop as a result of low-velocity streaks generated in the shear layer. A frequency that is approximately 50% higher than that of the Kelvin-Helmholtz instability is identified in the separated shear layer, which is shown to be associated with convection of these vortex loops through the separated shear layer. While freestream turbulence is noted to promote breakdown of the laminar separated shear layer into turbulence through the generation of turbulent spots, evidence is found to suggest co-existence of the Kelvin-Helmholtz instability, including the possibility of breakdown to turbulence through this mechanism.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".