Cardboard Drone Airfoil Shape Optimization based on Physics-Informed Machine Learning

This research focuses on optimizing the airfoil shape tailored for cardboard drones, a significant asset in the Russia-Ukraine war due to their cost-effectiveness and operational impact. Despite their advantages in storage and transport, these drones require manual assembly, with considerable time spent on connecting wing ribs and spars. To enhance both ease of assembly and aerodynamic performance, we formulated an airfoil shape optimization problem incorporating manufacturability constraints specific to cardboard drone construction. We utilized NeuralFoil, a physics-informed machine learning approach, to overcome the limitations of traditional tools. By integrating NeuralFoil with a gradient-based optimization method, the proposed approach resulted in a new airfoil that not only improves aerodynamic efficiency over existing shapes but also simplifies the assembly process.