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We employ a novel computational modelling framework to perform high-fidelity direct numerical simulations of aero-structural interactions in bat-inspired membrane wings. The wing of a bat consists of an elastic membrane supported by a highly articulated skeleton, enabling localised control over wing movement and deformation during flight. By modelling these complex deformations, along with realistic wing movements and interactions with the surrounding airflow, we expect to gain new insights into the performance of these unique wings. Our model achieves a high degree of realism by incorporating experimental measurements of the skeleton’s joint movements to guide the fluid–structure interaction simulations. The simulations reveal that different segments of the wing undergo distinct aeroelastic deformations, impacting the flow dynamics and aerodynamic loads. Specifically, the simulations show significant variations in the effectiveness of the wing in generating lift, drag and thrust forces across different segments and regions of the wing. We employ a force partitioning method to analyse the causality of pressure loads over the wing, demonstrating that vortex-induced pressure forces are dominant while added-mass contributions to aerodynamic loads are minimal. This approach also elucidates the role of various flow structures in shaping pressure distributions. Finally, we compare the fully articulated, flexible bat wing with equivalent stiff wings derived from the same kinematics, demonstrating the critical impact of wing articulation and deformation on aerodynamic efficiency.
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A Model‐Based Deep‐Learning Approach to Reconstructing the Highly Articulated Flight Kinematics of Bats
ABSTRACT Bats are capable of highly dexterous flight maneuvers that rely heavily on highly articulated hand skeletons and malleable wing membranes. To understand the underlying mechanisms, large amounts of detailed data on bat flight kinematics are required. Conventional methods to obtain these data have been based on tracing landmarks and require substantial manual effort. To generate 3D reconstructions of the entire geometry of a flying bat in a fully automated fashion, the current work has developed an approach where the pose of a trainable articulated mesh template that is based on the bat's anatomy is optimized to fit a set of binary silhouettes representing views from different directions of the flying bat. This is followed by post‐processing to smooth the reconstructed kinematics and simulate the non‐rigid motion of the wing membranes. To evaluate the method, 10 flight sequences that represent several flight maneuvers (e.g., straight flight, takeoff, u‐turn) and were recorded in a flight tunnel instrumented with 50 synchronized cameras have been reconstructed. A total of 4975 reconstructions are generated in this fashion and subject to qualitative and quantitative evaluations with promising results. The reconstructions are to be used for quantitative analyses of the maneuvering kinematics and the associated aerodynamics.
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- Award ID(s):
- 2419088
- PAR ID:
- 10600202
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Applied AI Letters
- Volume:
- 6
- Issue:
- 2
- ISSN:
- 2689-5595
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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