Flavio Donizeti Marques

This study investigates the application of dynamic neural networks for predicting unsteady aerodynamic loads on airfoils under dynamic stall conditions. The dynamic stall phenomenon is characterized by highly nonlinear and transient flow separation at high angles of attack. It poses significant challenges for traditional modeling approaches such as Computational Fluid Dynamics (CFD) and semi-empirical methods. To address these limitations, this work innovatively proposes a reduced-order model, trained exclusively on experimental data, that uses dynamic neural networks to rapidly and reliably evaluate aerodynamic loading on pitching airfoils under nonlinear dynamic stall conditions. Two neural network architectures, Time-Delay Neural Networks (TDNN) and Recurrent Neural Networks (RNN), are trained on experimental wind tunnel data for a NACA0012 airfoil undergoing pitching oscillations. The TDNN incorporates time-delayed inputs to capture temporal dependencies, while the RNN leverages internal feedback loops to model sequential aerodynamic behavior. Both models were trained using the Scaled Conjugate Gradient (SCG) algorithm and evaluated on their ability to predict lift, drag, and pitching moment coefficients. Results indicate that both architectures effectively capture dynamic stall’s nonlinear and hysteretic characteristics, with the RNN demonstrating marginally superior performance due to its inherent memory retention. The models achieved high accuracy in predicting lift and drag. However, challenges persisted in estimating the pitching moment, particularly in light stall regimes, likely due to noise sensitivity in moment data. Generalization tests on unseen cases confirmed the neural network models’ robustness in severe dynamic stall scenarios, including vortex shedding and flow reattachment.

High-aspect-ratio (AR) wings are the prominent feature of very flexible aircraft, and greater AR designs are being explored for enhanced flight efficiency in civil aviation. However, the impact of higher flexibility on the aeroelastic behavior of fixed-wing aircraft still needs to be fully understood. This paper assesses the effects of aerodynamic nonlinearities from flow separation on the maneuvering and gust response of a high-aspect-ratio flying wing using low-order models. Three aerodynamic formulations are evaluated: a linear model without stall effects, a piecewise linear model with steady-stall characteristics, and a nonlinear model accounting for dynamic-stall phenomena. All models perform similarly during maneuvering oscillations near stall conditions, but the dynamic-stall model foresees flight dynamic instabilities poststall, which are not seen in other models given the same maneuver input. Under a limited number of discrete and continuous gust disturbances, the model without stall effects behaves nonconservatively, whereas the steady-stall model is more conservative compared to the dynamic-stall model, with respect to the prediction of flight instabilities. These outcomes highlight the importance of accounting for aerodynamic nonlinearities in understanding the flight dynamics of very flexible aircraft under moderate-to-high angle-of-attack excitations.