Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
793629 | Journal of Fluids and Structures | 2014 | 19 Pages |
The paper develops a reduction scheme based on the identification of continuous time recursive neural networks from input–output data obtained through high fidelity simulations of a nonlinear aerodynamic model at hand. The training of network synaptic weights is accomplished either with standard or automatic differentiation integration techniques. Particular emphasis is given to using such a reduced system in the determination of aeroelastic limit cycles. The related solutions are obtained with the adoption of two different approaches: one trivially producing a limit cycle through time marching simulations, and the other solving a periodic boundary value problem through a direct periodic time collocation with unknown period. The presented formulations are verified for a typical section and the BACT wing.