Nonlinear aeroelastic reduced order modeling by recurrent neural networks

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...

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Bibliographic Details
Published in:Journal of fluids and structures 2014-07, Vol.48, p.103-121
Main Authors: Mannarino, Andrea, Mantegazza, Paolo
Format: Article
Language:English
Subjects:
Aeroelasticity
Automation
Computer simulation
Continuous time recurrent neural networks
Limit cycle oscillation
Mathematical models
Neural networks
Nonlinear aeroelasticity
Nonlinearity
Periodic collocation method
Reduced order
Time marching
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Summary: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.
ISSN:0889-9746
1095-8622
DOI:10.1016/j.jfluidstructs.2014.02.016