Comparative Analysis of Sizing Methodologies for High-Reliability Multicopters
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2022-3883.vid The current developments in advanced motors, battery technology and artificial intelligence lead to the evolution of multicopter drone applications. Such applications range from firefighting to search and rescue and even air taxi, all of which require routine operations in densely populated areas. However, the main limitation to applications in such urban areas is their safety requirements, which require introducing robust or fault-tolerant designs relying on redundancies. Redundancies in multicopter design are very challenging as they can critically hinder their performance by significantly increasing their mass. Moreover, no published methodology addresses safety and reliability effectively. The presented work aims to make multicopter designs both performant and safe for safety-critical applications by implementing reliability and redundancy techniques in the sizing process. Adding these redundancy techniques within the methodologies will contribute to their overall integration in the sizing process and performance analysis. Then, the effect of reliability on performance could be evaluated and captured earlier in the design process. For this matter, three sizing methodologies of multicopters are reviewed and analyzed using a uniform N2 diagram representation. The methodologies illustrated in this work are chosen to represent the major types of methodologies, whether using empirical, analytical or off-the-shelf database tools. Their various attributes and ability to incorporate redundancy techniques are evaluated and illustrated through traditional and coaxial configurations case studies. The case study results finally illustrate the effects of high-reliability on performance and the need for multicopter sizing methodologies implementing redundancies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".