Application of Bayesian Inference to the Flutter Margin Method: New Developments
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Bibliographic record
Abstract
Zimmerman and Weissenburger flutter margin method is extended to account for modal parameter uncertainties by applying a Bayesian estimation technique to obtain the probability distribution function of the flutter speed. In previous work, a least-squares estimation technique was applied to obtain the posterior pdf of the flutter speed. The limitation of this technique is the assumption that the flutter margin at each airspeed is strictly Gaussian. In this paper, the joint distribution of the modal parameters (and consequently the flutter margin) is obtained from preflutter measured system responses using a full Bayesian analysis utilizing Markov Chain Monte Carlo sampling technique. The flutter margin pdfs are then utilized to obtain the posterior probability density function of the flutter speed. Results are presented for a two-degrees-of-freedom numerical model, for which the true flutter speed is known.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it