‘Switchblade’: Wide-Mission Performance Design of a Multi-Variant Unmanned Aerial System
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2021-0213.vid Reconfigurable systems are meant to provide users with increased flexibility, while enabling reduced manufacturing costs due to the use of shared parts between system variants. This paper aims to present the conceptual design of a family of unmanned aerial vehicles (UAVs), known as ’Switchblade’, developed for wide multi-mission capability. Four UAV variants are designed for distinct flight performance: low-speed high endurance (LSHE), high-speed long range (HSLR), and vertical takeoff and landing (VTOL) enabled variants of each. Module commonality is maximized in order to reduce complexity and development costs. The design approach employs the concept of ’parent-variants,’ which drives design and performance analysis for all variants. This is illustrated in the paper with specific examples of propulsion and longitudinal stability analysis. Preliminary computational fluid dynamics (CFD) simulations of the aerodynamic characteristics of the LSHE and HSLR variants were carried out. The results suggest that the computed lift-to-drag ratios, L/D between the CFD results and the analytical approximations using finite wing theory are in reasonable agreement.
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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 | 0.000 |
| 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) | 0.002 | 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".