Nonlinear Generalized Lifting-Line Coupling Algorithms for Pre/Poststall Flows
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Bibliographic record
Abstract
Numerical algorithms and solutions of generalized nonlinear lifting-line theory over an elliptical wing are examined, with emphasis on near/poststall flows. First, a thorough analysis on the circulation-based and angle-of-attack-based correction methods ( and methods, respectively) highlights their respective numerical poststall characteristics. The stability of the method is demonstrated, producing single and multiple solutions in the pre- and poststall regions, respectively. Second, artificial dissipation added to the method is shown to be an effective means of controlling the poststall flow region. Finally, a strongly coupled algorithm is presented, allowing to bypass the interpolation phase via the use of Legendre polynomials. The model sheds light on poststall flow behavior, in agreement with several papers studying formation of stall cell patterns.
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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 | 0.000 | 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) | 0.000 | 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