Bibliographic record
Abstract
This paper is about an application of an energy approach to computational aeroelasticity. A frequency-domain calculation of aerodynamic work is presented in a form that has not been previously discussed. In large aeroelastic systems such as aircraft flutter models, the obtained expression allows us to quantify the roles played in flutter by the generalized coordinates, the phases between them, and the generalized aerodynamic forces. This is exemplified in a body-freedom flutter analysis of a flying-wing aircraft: the X-56A. Another interesting feature that is proposed is a diagram of the aerodynamic work as a function of airspeed. Such functions allow an observance of an evolution with the airspeed of the terms that are most reflective of the aeroelastic stability changes: for example, a phase between the two dominant modes. These diagrams can complement typical flutter trends in analysis documentation and aid in flutter suppression. The approach also permits a perspective on flutter as an interaction of aircraft surfaces rather than vibrational modes. Once a sensitivity of an aeroelastic eigenvalue to a surface area is measured with the presented approach, it can be used in the aircraft design to mitigate flutter and other undesirable aeroelastic responses associated with lightly damped eigenvalues. An illustration of this idea is provided. Finally, energy-based computations allow posing energy-efficient active flutter suppression problems. This has been presented before in the literature. Examples of this aspect are made here with aircraft models (for the first time, as far as the author knows).
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".