Direct drive or slider-crank? Comparing motor-actuated flapping-wing micro aerial vehicles
Bibliographic record
Abstract
For flapping-wing micro aerial vehicles, the common approach to converting the rotational motion of a DC motor to the reciprocal flapping motion is using a slider-crank mechanism. However, frictional losses in sliders and rotational joints can hinder the performance of such a system. An alternative is a direct drive system where the wings are directly connected to a DC motor that has been driven by an AC signal. These two approaches are compared in this paper, to evaluate their performances and assess which one provides a better solution for flapping-wing micro drones. The electromechanical model of the two systems is used in this paper to compare their performances. System parameters for both types of drones were derived through a multi-variable optimisation process using the same DC motor. The comparisons are made in terms of input power requirement, aerodynamic power, system efficiency, and lift. The direct drive model can generate about 16% higher average lift at 5 V with 50% lower input electrical power. It has 29% larger aerodynamic power and the system efficiency is 16.0% higher than that of the slider-crank model.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".