Peer-reviewed veterinary case report
How can machine learning improve bird flight?
By Khalid W et al.·2026·Moscow Institute of Physics and Technology (MIPT)·View original on Europe PMC →
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Original publication title: Optimizing avian flight dynamics with a synergetic bio-inspired and machine learning approach.
- Species:
- bird
Plain-English summary
This study looked at ways to improve how bird-like flapping wings perform in the air using a combination of computer simulations and machine learning. Researchers first analyzed videos of birds flying to gather data on their wing movements, which they then used to create a detailed computer model of a wing. They tested this model to see how well it could predict how air flows around the wing. After validating their model with real-world data, they used machine learning to find the best wing movements for better flight performance. The results showed that this approach successfully combined observations from nature with advanced computer techniques to enhance the design of flapping wings.
Abstract
This study presents a composite numerical and machine learning framework to enhance the aerodynamic performance of a bio-inspired flapping wing. The wing kinematics were extracted from biological flight data using a multi-step process: the DeepLabCut tool was applied to extract body point coordinates from avian flight videos, followed by data digitization in Google Colab and trajectory post-processing in Python. These kinematics were then prescribed to a three-dimensional wing model for high-fidelity unsteady Reynolds-Averaged Navier-Stokes (URANS) simulations of incompressible turbulent flow in ANSYS Fluent, utilizing a user-defined function (UDF) and a sliding mesh technique. Validated against existing experimental data, the numerical model serves as a reliable data generation tool. Subsequently, a machine learning model was developed to explore the design space and identify kinematic parameters that optimize aerodynamic efficiency. The results demonstrate that the proposed framework effectively bridges biological observation with computational optimization, offering a robust approach for the performance enhancement of bio-inspired flapping wings.
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Search related cases →Original publication on Europe PMC: https://europepmc.org/article/MED/42022894