Machine Learning Applications in Aeronautics: Real-Time Noise Visualization and Structural Optimization for Adaptive Wing Designs

Abstract

Atmospheric turbulence remains one of the leading contributors to loss of control in small fixed-wing aircraft, and conventional fixed-geometry wings respond to disturbances only after a measurable attitude error has already developed. This paper presents an embedded, machine-learning-based feedback system that visualizes roll, pitch, and yaw disturbances in real time using Python and adjusts elevator and rudder deflection to accelerate recovery. Seven wing configurations, high, mid, low, dihedral, anhedral, gull, and inverted gull, were fabricated from balsa wood and styrofoam and evaluated on an instrumented test stand under repeatable disturbance conditions. A small feed-forward neural network was trained on logged roll, pitch, and yaw error signals to predict corrective elevator and rudder angles, and its output was compared against an uncorrected baseline and against a simple embedded proportional controller. Across 12 trials per configuration, the high wing produced the shortest mean recovery time in all three axes (roll: 1.1 s, pitch: 1.3 s, yaw: 1.0 s) and the highest mean lift coefficient (1.28), while the anhedral wing was consistently the slowest to recover. The trained network reduced the residual mean squared error of the predicted control surface angles by 88 percent after 40 training epochs, averaged across all seven configurations, and five-fold cross-validation confirmed that this improvement generalized rather than overfitting to a single flight condition. These results suggest that pairing a self-stabilizing high-wing geometry with a lightweight, real-time learning controller can materially shorten the window in which an aircraft is vulnerable to a turbulence-induced upset, with direct implications for small unmanned aircraft safety.