ORIGINAL RESEARCH article
Front. Robot. AI
Sec. Bio-Inspired Robotics
Optimizing Avian Flight Dynamics with a Synergetic Bio-Inspired and Machine Learning Approach
Waleed Khalid 1
Valentina Leontiuk 1
Innokentiy A. Kastalskiy 1,2
Viktor B Kazantsev 1,2,3
1. Moscow Institute of Physics and Technology, Dolgoprudny, Russia
2. Nacional'nyj issledovatel'skij Nizegorodskij gosudarstvennyj universitet imeni N I Lobacevskogo, Nizhny Novgorod, Russia
3. Central'nyj naucno-issledovatel'skij institut robototehniki i tehniceskoj kibernetiki, Saint Petersburg, Russia
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Abstract
This study presents a composite numerical and machine learning framework to enhance the 3 aerodynamic performance of a bio-inspired flapping wing. The wing kinematics were extracted 4 from biological flight data using a multi-step process: the DeepLabCut tool was applied to 5 extract body point coordinates from avian flight videos, followed by data digitization in Google 6 Colab and trajectory post-processing in Python. These kinematics were then prescribed to 7 a three-dimensional wing model for high-fidelity unsteady Reynolds-Averaged Navier-Stokes 8 (URANS) simulations of incompressible turbulent flow in ANSYS Fluent, utilizing a user-defined 9 function (UDF) and a sliding mesh technique. Validated against existing experimental data, the 10 numerical model serves as a reliable data generation tool. Subsequently, a machine learning 11 model was developed to explore the design space and identify kinematic parameters that 12 optimize aerodynamic efficiency. The results demonstrate that the proposed framework effectively 13 bridges biological observation with computational optimization, offering a robust approach for the 14 performance enhancement of bio-inspired flapping wings.
Summary
Keywords
bio-inspired optimization, Biomimetics, Bird flight, Computational Fluid Dynamics (CFD), computational modeling, hybrid modeling, machine learning, numerical simulation
Received
15 January 2026
Accepted
19 February 2026
Copyright
© 2026 Khalid, Leontiuk, Kastalskiy and Kazantsev. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Waleed Khalid
Disclaimer
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