On the robust autorotation of a samara-inspired rotor in gusty environments
Bibliographic record
Abstract
Abstract Autorotating samaras have evolved to propagate successfully to their germination sites with the help of wind. This wind, in turn, is inherently unsteady across an extensive range of scales in the atmospheric boundary layer. To generate lift, samaras rely on the formation of a stably-attached leading-edge vortex (LEV) on the suction side of their wings. The kinematics of autorotating samaras experiencing gusts were examined experimentally in order to provide insights into the aerodynamic mechanisms responsible for successful propagation. The gust response of seven mature Boxelder Maple ( Acer negundo ) samaras was investigated using a small unsteady wind tunnel able to create vertical gusts. Interestingly, the samaras were found to have a stable tip-speed ratio ( λ ) during the gust phase, thus suggesting that the LEV remained stably-attached. Inspired by samaras, we designed a three-bladed rotor that incorporates key aerodynamic and geometric properties of samaras so as to exhibit a stably-attached LEV. The gust response of the samara-inspired rotor was examined using a towing-tank facility. The gust was emulated in the towing tank by accelerating the rotor from an initial steady speed to a final steady speed. Different gust intensities were tested by varying the rotor’s normalized inertia number ( I *) by systematically increasing the rotor moment of inertia ( I ). Similar to the natural samaras, the rotor exhibited a robust tip-speed ratio during all simulated gusts. The rotor’s tip-speed ratio increased by a maximum of 11% and 6% during the slowest and fastest simulated gusts, respectively. By maintaining a stable tip-speed ratio during the gust, the samara-inspired rotor is thought to maintain stable LEVs resulting in stable autorotation. Therefore, by learning from the samara-inspired rotor, we suggest that samaras propagate successfully from their parent trees in unsteady (realistic) environments in part due to their robust autorotation properties.
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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 | 0.000 |
| 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 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".