Conceptual design methods for small-scale supersonic uncrewed aerial vehicles
Bibliographic record
Abstract
An investigation of conceptual design methods used for small-scale supersonic uncrewed aerial vehicles (SSUAV) was performed to facilitate future SSUAV design work. Verification and validation analyses of the Stanford University Aerospace Vehicle Environment (SUAVE) was conducted for various fidelity aerodynamics, stability, and propulsion modules. A new weights module, tailored for SSUAV concepts, was developed and implemented into SUAVE. The performance of a new SSUAV concept, the University of Calgary multipurpose unmanned fixed-wing advanced supersonic aircraft (MUFASA), was assessed and compared to two existing designs (GOJETT and M2011). Performance metrics of takeoff distance, maximum flight Mach number, and cruise range were used. As each vehicle design is different, a system was setup to compare them across differing scales. A variety of factors related to this scaling system were examined for their influence on vehicle performance metrics, including off-design turbojet performance, available fuel volume, and predicted empty weights. GOJETT was found to be feasible (capable of completing a full supersonic mission) at a wide range of sizes, while MUFASA required an increase from the existing vehicle size to be feasible. The M2011 did not have any feasible sizes under the system used. The smallest feasible SSUAV was found to have a takeoff mass of 13.41kg.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".