Inviscid Spatial Linear Stability Analysis of Separated Shear Layers Based on Experimental Data
Bibliographic record
Abstract
A comparative analysis of approaches for performing inviscid spatial linear stability analysis on experimentally measured separated shear layer profiles is carried out. It is shown that stability predictions are sensitive to both velocity profile data scatter and the analysis approach. Stability analysis is applied directly to separated shear layer profiles measured in previous studies, without curve fitting the velocity data. For low levels of data scatter, the analysis yields realistic predictions of the frequency of maximum disturbance growth rate. However, for measurements with higher data scatter, unrealistic growth rate spectra are predicted, suggesting a need for curve fitting the discrete velocity profile. Of the ten curve fits investigated, five fits are identified which result in stability predictions with low sensitivity to velocity profile data scatter. It is demonstrated that, for a given measured velocity profile, greater variation in stability predictions results from the choice of curve fit than from the data scatter commonly observed in separated shear layer velocity profile measurements. The curve fits are further evaluated based on stability predictions for several experimental data sets. The results provide an estimate of the uncertainty in predictions from inviscid spatial linear stability analysis of measured separated shear layer profiles.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".