Optimization of unsteady aerodynamic forces for aeroservoelastic analysis
Bibliographic record
Abstract
Optimizationofunsteadyaero dynamicforcesforaeroservo elasticanalysisRuxandraMihaela Botez11Iulian Cotoi1AbstractThe standard Minimum State approximation for the unsteady aero dynamic forces is the metho d givingthe lowest dimension of the state-space realization for the study of the linear stability of a exible aircraft.Cases when one or more aero dynamic elements are di cult to t but to o imp ortantto weigh less can o ccur(see [13]).Weovercome this problem by tting each element with a suitable rational Pade approximation.Furthermore,wedevelopapro cedureforobtaining aminimal state-spaceapproximation withtechniquesfrom systemtheory.Keywords:Unsteadyaero dynamic forces,Minimum Stateapproximation, Minimal Realization.1Intro ductionTheaeroservo elasticinteractionsconcernmainlytheteractionbetweenthreemainfollowingdisci-plines:aero dynamics,aero elasticityandservo-controls.Progressinthismultidisciplinaryareahasde-manded optimization of aero dynamic metho ds in their capability to generate s-domain aero dynamics fromk-domain aero dynamics.Aeroservo elasticity establishes the s-domain as a base, which can b e obtained from the k-domain aero-dynamicsbymeansof severalrational approximation metho ds[6, 11, 14],suchas:TheconventionalLeast SquareLS1Ecole de Technologie Sup erieure, 1100 rue Notre-Dame Ouest, Montreal, Queb ec, H3C 1K3.1
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".