Inviscid Spatial Linear Stability Analysis of Separated Shear Layers Based on Experimental Data
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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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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) | 0.002 | 0.000 |
Machine scores (provisional)
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Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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