Machine Learning-Based Prediction of Unsteady Aerodynamic Forces for Flight Dynamics in Modern Aviation Systems

Abstract

Conventional aerodynamic prediction methods have traditionally relied on computationally expensive computational fluid dynamics (CFD) simulations or simplified linear models that fail to capture complex flow phenomena. This research presents a novel machine learning framework for real-time prediction of unsteady aerodynamic forces critical for modern flight dynamics systems. The proposed methodology combines recurrent neural network architectures with physics-informed constraints to accurately model nonlinear aerodynamic behaviors across diverse flight regimes. Extensive validation using high-fidelity CFD datasets demonstrates that our approach achieves prediction accuracy within 3.2\% of benchmark solutions while reducing computational requirements by approximately 98.7\%. The framework successfully captures complex phenomena including dynamic stall, vortex-induced vibrations, and transonic buffeting effects that traditional reduced-order models fail to represent. Implementation on embedded flight hardware shows real-time performance capabilities for integration within next-generation flight control systems. This research establishes a foundation for machine learning augmentation of flight dynamics modeling, with significant implications for autonomous aircraft design, flight envelope protection, and adaptive control systems operating in challenging aerodynamic environments.