Control moment coefficient methodology validation for eVTOL sizing
Bibliographic record
Abstract
This paper presents a novel approach for the preliminary design of electric Vertical Takeoff and Landing (eVTOL) aircraft that utilizes the new Control Moment Coefficient (CMC) to size electric motors and to determine the rotor location and incidence angle. The CMC is determined for both thrust and arm length in eVTOL aircraft design, and is used to measure the moment produced by the rotors in the roll, pitch, and yaw axes. Analyzing its dimensionless value thus allows insights into an eVTOL aircraft's controllability. To test our methodology, two eVTOL aircraft were used in flight tests, one of which had up to 126% higher CMC values than the other. The results of the flight tests showed that a higher CMC value yielded many benefits, including an increased margin of safety between the rotors and the saturation level, reduced tracking error, and reduced control effort (or energy consumption).Furthermore, the 126% increase in the dimensionless CMC related to the pitch resulted in a 30% increase in the Pulse-Width Modulation - PWM margin of safety of the rotors at the saturated level while still maintaining a reasonable tracking error and a 97% decrease in the pitch control effort. Our research suggests that incorporating higher CMCs into the preliminary design of an eVTOL aircraft can significantly improve its safety and controllability. We hope that our findings will encourage further exploration of this promising approach in future.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".