Study of Insect Impact on an Aerodynamic Body Using a Rotary Wing Simulator
Bibliographic record
Abstract
Laminar flow aircraft may potentially save fuel and reduce the emission of pollutants and greenhouse gases. However, laminar flow aircraft face challenges caused by contaminations on the wings, such as insect impact residue. To study insect residue on an aircraft airfoil, a new setup was developed that used rotary wings and shot an insect toward the leading edge. This setup kept insects intact before impact while airflow was maintained throughout the experiment. Additionally, the setup enabled the long-term observation of the impact residue while the test speed was adjusted. Two experiments were carried out to investigate inconsistencies from past studies about insect rupture velocity and the effect of airflow on residue. Drosophila Hydei was the insect used, and aluminum was used as the baseline substrate, which was also coated with polyurethane, acrylic, and two superhydrophobic coatings. Instead of a threshold velocity for the minimum rupture velocity of the insect, a range from initial insect rupture to the velocity at which insects ruptured in all instances was determined (i.e., 17–30 m/s). Furthermore, the presence of a coating (polyurethane) on the airfoil did not affect the minimum rupture velocity. It was observed that airflow, which has been previously mentioned as a mitigation method, did not change the residue amount after coagulation for all coatings.
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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) | 0.002 | 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".