Lateral instability in fruit flies is determined by wing–wing interaction and wing elevation kinematics
Bibliographic record
Abstract
Understanding the uncontrolled passive dynamics of flying insects is important for evaluating the constraints under which the insect flight control system operates and for developing biomimetic robots. Passive dynamics is typically analyzed using computational fluid dynamics (CFD) methods, relying on the separation of the linearized hovering dynamics into longitudinal and lateral parts. While the longitudinal dynamics are relatively understood across several insect models, our current understanding of the lateral dynamics is lacking, with a nontrivial dependence on wing–wing interaction and on the details of wing kinematics. Particularly, the passive stability of the fruit fly, D. melanogaster, which is a central model in insect flight research, has so far been analyzed using simplified quasi-steady aerodynamics and synthetic wing kinematics. Here, we perform a CFD-based lateral stability analysis of a hovering fruit fly, using accurately measured wing kinematics, and considering wing–wing interaction. Lateral dynamics are unstable due to an oscillating–diverging mode with a doubling time of 17 wingbeats. These dynamics are determined by wing–wing interaction and the wing elevation kinematics. Finally, we show that the fly's roll controller, with its one wingbeat latency, is consistent with the lateral instability. This work highlights the importance of accurate wing kinematics and wing–wing interactions in stability analyses and forms a link between such passive instability and the insects' controller.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".