Airworthiness Evaluation of a Scaled Joined-Wing Aircraft
Bibliographic record
Abstract
The proposed Boeing SensorCraft concept is a High Altitude, Long Endurance Platform intended to provide persistent intelligence, surveillance and reconnaissance capabilities. The concept employs a joined-wing configuration that has potential aerodynamic and structural benefits, as well as the capability to achieve 360 sensor coverage due to confomal sensor arrays distributed throughought the wings. Along with these unique advantages comes several challenges, specifically a predicted non-linear geometric response that may lead to phenomena such as aft wing buckling. In order to further investigate these potential problems it is desired to build and flight test a 1/9 scale, aeroelastically tailored vehicle. The complex nature of building and operating a flexible, aeroelastically scaled aircraft, along with additional challenges such as flightworthiness and control, have necessitated an incremental approach to the flight test program. The work presented in this paper is focused on the instrumentation, ground testing and flight operation of an initial 1/9 scale, geometrically equivelent Joined Wing Sensoraft vehicle. The work also summarizes the results of the initial flight test campaign, some lessons learned and concludes with the recommendation to proceed with the next phase of the project which will include rewinging the aircraft with aeroelastically scaled wings and subsequent flight testing.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".