Full-scale aeroelastic simulations of hovering bat flight
Bibliographic record
Abstract
In this paper, we study the aeroelastic behavior of a hovering bat using a three-dimensional variational fluid-flexible multibody framework. The aeroelastic framework consists of solving the coupled nonlinear interactions of flexible multiple components of the bat wing with the unsteady aerodynamics. To begin, we carry out the mesh convergence and compare the results of the presented formulation with the previous works on a flexible two-dimensional membrane subjected to low Reynolds number flow. We investigate the flapping dynamics of a full-scale bat using wing geometry and physical properties similar to the Pallas' long tongued bat Glossophaga soricina. The aeroelastic flexibility of the wing varies along its span and chord tending to a realistic bat wing, in contrast to the wing with uniform flexibility. We find that the flexible wings generate more unsteady lift compared to the rigid counterpart owing to the high wing-tip velocity due to the elastic deformation of the wings. Furthermore, we examine the time-varying vortex patterns and compare them with the experimental observations. We consider the effect of the anisotropic flexibility of the bone fingers and wing membranes on the aerodynamic lift as well as the vortex patterns generated by the flapping mechanism. Insights gained from the present study will be beneficial to develop novel designs for enhancing the maneuverability and flight agility of next-generation engineered flying vehicles (e.g., drones and micro-air vehicles) at low Reynolds number.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".