Aeroservoelastic Testing of a Sidewall Mounted Free Flying Wind-Tunnel Model
Bibliographic record
Abstract
A team comprised of the Air Force Research Laboratory (AFRL), Northrop Grumman, Lockheed Martin, and the NASA Langley Research Center conducted three j wind-tunnel tests in the Transonic Dynamics Tunnel to demonstrate active control technologies relevant to large, exible vehicles. In the rst of these three tests, a semispan, aeroelastically scaled, wind-tunnel model of a ying wing SensorCraft vehi- cle was mounted to a force balance to demonstrate gust load alleviation. In the second and third tests, the same wing was mated to a new, multi-degree-of-freedom, sidewall mount. This mount allowed the half-span model to translate vertically and pitch at the wing root, allowing better simulation of the full span vehicle's rigid-body modes. Gust Load Alleviation (GLA) and Body Freedom Flutter (BFF) suppression were successfully demonstrated. The rigid body degrees-of-freedom required that the model be own in the wind tunnel using an active control system. This risky mode of testing necessitated that a model arrestment system be integrated into the new mount. The safe and successful completion of these free-flying tests required the development and integration of custom hardware and software. This paper describes the many systems, software, and procedures that were developed as part of this effort.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".