Validation and verification of a conceptual design tool for evaluating small-scale, supersonic, unmanned aerial vehicles
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2021-2415.vid A verification and validation assessment of an open-source framework, the Stanford University Aerospace Vehicle Environment (SUAVE), and its individual modules, for the multidisciplinary design and optimization of small-scale, supersonic, unmanned aerial vehicles (UAVs) was performed. Multi-fidelity modules for aerodynamics, stability, propulsion, and weight estimation are compared to experimental wind-tunnel data, flight data, and commercial supplier data for supersonic aircraft, high-speed UAVs, and turbojet propulsion systems. An improved weight estimation module is proposed for small-scale, supersonic UAVs. Academic designs for supersonic UAVs are analyzed and compared to a conceptual design from the University of Calgary, for a variety of metrics. These UAV designs are compared at a range of scales, and the impact of different conceptual design methods on their performance is compared. Design improvements, and potential pitfalls related to conceptual design accuracy are discussed.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".