Analysis and Optimization of Rotor-Rotor-Structure Aerodynamic Interactions of a Quadcopter Drone using High-Fidelity CFD and Artificial Intelligence: Application to Precision Agriculture
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This article proposes a method for analyzing and optimizing complex aerodynamic interactions within a precision agriculture quadcopter. The objective is to reduce thrust losses and instabilities generated by rotor-rotor-structure interference and wake effects disrupting liquid distribution. The study combines high-fidelity computational fluid dynamics (CFD) with artificial intelligence. Unsteady URANS simulations generated a 35,000-point database capturing turbulent kinetic energy. This dataset trained a Deep Learning surrogate model approximating Navier-Stokes equations with an exceptional correlation coefficient. Crucially, a acceleration factor reduces diagnostic time from 3600 seconds to 0.4 milliseconds per configuration. This performance enabled a genetic algorithm to explore the Pareto front, proving that optimizing rotor spacing and using NACA arm airfoils reduces interference by 22% and improves energy efficiency by 14.8%. The analysis incorporates frequency-field spectroscopy (FFT), identifying vibrational peaks within 0.1 Hz, while flight robustness is validated by the Lyapunov stability criterion against liquid sloshing. This optimization minimizes droplet drift, keeping the coefficient of variation below 10%. This AI-CFD coupling transforms the drone into a proactive digital twin capable of real-time trajectory adjustments, ensuring optimal crop coverage and a minimized environmental footprint.
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