NASA Langley Research Center's Contributions to International Active Buffeting Alleviation Programs
Bibliographic record
Abstract
Buffeting is an aeroelastic phenomenon which plagues high performance aircraft, especially those with twin vertical tails like the F/A-18, at high angles of attack. This buffeting is a concern from fatigue and inspection points of view. By means of wind-tunnel and flight tests, this phenomenon is well studied to the point that buffet loads can be estimated and fatigue life can be increased by structural enhancements to the airframe. In more recent years, buffeting alleviation through active control of smart materials has been highly researched in wind-tunnel proof-of-concept demonstrations and full-scale ground tests using the F/A-18 as a test bed. Because the F/A-18 resides in fleets outside as well as inside the United States, these tests have evolved into international collaborative research activities with Australia and Canada, coordinated by the Air Force Research Laboratory (AFRL) and conducted under the auspices of The Technical Cooperation Program (TTCP). With the recent successes and advances in smart materials, the main focus of these buffeting alleviation tests has also evolved to a new level: utilize the F/A-18 as a prototype - to mature smart materials for suppressing vibrations of aerospace structures. The role of the NASA Langley Research Center (LaRC) in these programs is presented.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".