Aerodynamic efficiency of gliding birds vs comparable UAVs: a review
Bibliographic record
Abstract
Here, we reviewed published aerodynamic efficiencies of gliding birds and similar sized unmanned aerial vehicles (UAVs) motivated by a fundamental question: are gliding birds more efficient than comparable UAVs? Despite a multitude of studies that have quantified the aerodynamic efficiency of gliding birds, there is no comprehensive summary of these results. This lack of consolidated information inhibits a true comparison between birds and UAVs. Such a comparison is complicated by variable uncertainty levels between the different techniques used to predict avian efficiency. To support our comparative approach, we began by surveying theoretical and experimental estimates of avian aerodynamic efficiency and investigating the uncertainty associated with each estimation method. We found that the methodology used by a study affects the estimated efficiency and can lead to incongruent conclusions on gliding bird aerodynamic efficiency. Our survey showed that studies on live birds gliding in wind tunnels provide a reliable minimum estimate of a birds' aerodynamic efficiency while simultaneously quantifying the wing configurations used in flight. Next, we surveyed the aeronautical literature to collect the published aerodynamic efficiencies of similar-sized, non-copter UAVs. The compiled information allowed a direct comparison of UAVs and gliding birds. Contrary to our expectation, we found that there is no definitive evidence that any gliding bird species is either more or less efficient than a comparable UAV. This non-result highlights a critical need for new technology and analytical advances that can reduce the uncertainty associated with estimating a gliding bird's aerodynamic efficiency. Nevertheless, our survey indicated that species flying within subcritical Reynolds number regimes may inspire UAV designs that can extend their operational range to efficiently operate in subcritical regimes. The survey results provided here point the way forward for research into avian gliding flight and enable informed UAV designs.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".