An Aerodynamic Analysis Model for Bird-Inspired Morphing Wings: Leveraging Machine Learning for Performance Optimization

Many bird species are very adaptable; they modify wing shape in response to airflow, pressure, and flight conditions. This biological ability, which derives from the micro-morphological flexibility of bird wings, has led to new ideas in biomimetic aerodynamics. In this context, the current study introduces BioAeroMorph, a computational framework that uses machine learning regression to examine bio-inspired airfoils and their lift-to-drag optimisation. The system combines basic morphing wing principles with data-driven modeling to support adaptive aerodynamic decision-making.Our proposed framework combines (a) bio-inspired geometric parameterisation, (b) aerodynamic data generation using XFoil/XFLR5, (c) nonlinear regression modelling via Polynomial Regression and Random Forest and (d) performance-guided autonomous selection based on lift-to-drag (CL/CD) optimisation. This structure reflects key principles of efficient biological flight.Aerodynamic data synthesis forms the core of the framework. Three airfoils were analysed: a NACA 2412 reference, a falcon-inspired manoeuvrability profile, and a seagull-inspired gliding profile. Their aerodynamic polars (CL, CD, CL/CD) were obtained under constant Reynolds-number conditions, enabling controlled comparison between natural and conventional aerodynamic geometries.The trained models predict CL/CD for new geometric or flow conditions. Polynomial Regression captures global nonlinear aerodynamic relationships, while Random Forest remains robust under local variations and noise. Together they create a hybrid decision layer that evaluates candidate morphing configurations and, selects aerodynamically efficient options within constraints, forming an adaptive flight-performance set.BioAeroMorph’s optimisation loop emulates biological feedback: predicted aerodynamic responses, balanced against flight objectives (lift, drag, stability), iteratively refine candidate geometries. This establishes a lightweight computational morphing paradigm that avoids heavy CFD or structural optimisation costs. By framing morphing wings as data-driven aerodynamic decision systems, BioAeroMorph provides a scalable, low-cost approach for UAVs and autonomous flight platforms requiring real-time aerodynamic awareness.