In the last few years reduced order modeling (ROM) of aerodynamics became more and more popular in the aeroelasticity community. Different reduced order modeling approaches are developed, for instance harmonic balance, center manifold, normal form and numerical continuation methods. An introduction to these methods can be found in Henshaw et al. [2]. Other methods create a linear state-space formulation via the eigenvalue realization algorithm (ERA) used for example by Lucia et al. [11].


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    Title :

    Nonlinear Reduced Order Modeling for Aeroelastic Simulation with Neural Networks


    Additional title:

    Notes Numerical Fluid Mech.


    Contributors:


    Publication date :

    2013-01-01


    Size :

    19 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Nonlinear Reduced Order Modeling for Aeroelastic Simulation with Neural Networks

    Lindhorst, K. / Haupt, M.C. / Horst, P. | British Library Conference Proceedings | 2013


    Reduced Order Dynamic Aeroelastic Model Development and Integration with Nonlinear Simulation

    Winther, B. / Goggin, P. / Dykman, J. et al. | British Library Conference Proceedings | 1996