Design and Evaluation of Aeroelastically Tuned Joined-Wing SensorCraft Flight Test Article
Bibliographic record
Abstract
The Boeing Joined Wing SensorCraft is a High Altitude Long Endurance (HALE) Intelligence Surveillance and Reconnaissance (ISR) platform. Boeing′s approach in utilizing a joined-wing configuration offers potential aerodynamic and structural benefits including unmatched ISR capabilities complementing the overall mission. However, associated with these advantages is the potential for nonlinear aeroelastic response. In an effort to compliment computational studies, the design, construction, and flight testing of a 1/9th scale, aeroelastically tuned model of the Joined-Wing SensorCraft has been the subject of an ongoing international collaboration aimed at experimentally demonstrating the nonlinear aeroelastic response. To accurately measure and capture the configuration′s potential for structural nonlinearity, the test article must exhibit equivalent structural flexibility and be designed to meet airworthiness standards. Previous work has demonstrated airworthiness through the successful flight of a Geometrically Scaled Remotely Piloted Vehicle, but its capability to demonstrate geometric nonlinearities in flight has yet to be determined. Current work involves evaluation of an aeroelstically tuned design through finite element modeling and experimentation. Initial investigation using lower order models point to the possibility of using the existing forward wings, combined with tailored aft wings. The focus of this work is the evaluation suitability of these wings in terms of the structural limits, based on high fidelity modeling and ground test validation.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".