Numerical Study of Instability Mechanisms Leading to Transition in Separation Bubbles
Bibliographic record
Abstract
In this paper, transition in a separation bubble is examined through numerical simulation. The flow Reynolds number and streamwise pressure distribution are typical of the conditions encountered on the suction side of low-pressure turbine blades of gas-turbine engines. The spatial and temporal resolutions utilized in the present computations correspond to a coarse direct numerical simulation, wherein the majority of turbulence scales, including the inertial subrange, are adequately resolved. The accuracy of the simulation results is demonstrated through favorable comparisons to experimental data corresponding to the same flow conditions. The results of the simulation show linear Tollmien-Schlichting (T-S) instability growth downstream of the point of separation, leading to the roll up of spanwise vorticity into discrete vortical structures, characteristic of Kelvin-Helmholtz (K-H) instability growth. The extent of cross-stream momentum exchange associated with packets of amplified T-S waves is examined, along with details of the time-periodic breakdown into turbulence occurring upon the development of the K-H instability. Reynolds-averaged properties of the separation bubble are presented and provide evidence of the strong three-dimensional nature of the reattachment process.
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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".