Trim Solutions of Multirotor Vehicles using a Fast Performance Prediction Method
Bibliographic record
Abstract
A fast multirotor performance prediction method is presented. The method uses an algorithm to determine the flight performance and trim solutions of multirotor vehicles in steady, level flight. The method considers parasitic drag, force trim, fuselage interference, rotor interference, moment trim, and power prediction. In order to validate the method, vehicle lift, drag, and pitching moment predictions are compared to experimental data from NASA Ames for the 3DR Solo, a commercially available vehicle. The performance comparison with wind tunnel data show similar lift, drag and pitching moment trends when using estimated rotor and vehicle geometries. In addition, the predicted rotor speeds, vehicle power, and vehicle pitch are compared to flight test data of the Aeryon SkyRanger. The lead and rear rotor speed results show that the application of moment trim into the performance model provides rotor speed estimates that reflect the differential rotor speeds the flight test. An orientation study is conducted to explore the effects of rotor and fuselage interference velocities on rotor performance and the performance differences of a four-rotor vehicle flying in diamond and square configurations. Finally, a mass offset study is presented to predict the changes in rotor speed distribution of a SkyRanger vehicle when a 100 g mass is added to the support arm, which simulates asymmetry in centre of gravity location. The predicted performance results show overlapping results with flight testing with and without the mass offset at airspeeds below 5 m/s. At higher airspeeds, the rotor speed predictions that are established by moment trim requirements reflect the rotor speed trends shown from flight test data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".