This study presents the development of a methodology for the construction of data-driven, parametric, multifidelity reduced-order models to emulate aerodynamic flowfields with nonlinear and discontinuous features. Realistic computational budgets often constrain the size of the high-fidelity data set required to build a model with the desired predictive accuracy. In such cases, multifidelity models can be advantageous, as they leverage an abundance of inexpensive low-fidelity data in conjunction with high-fidelity training data to improve the model’s predictive accuracy. This study formulates a multifidelity reduced-order modeling method that uses nonlinear dimension reduction and Procrustes manifold alignment to project and transform data from disparate sources such that they lie in a common latent space. An initial feasibility assessment of the method is performed for emulating the flow over a two-dimensional transonic airfoil and a high-speed blunt body. It is observed that, for problems with high input space dimension and complex features, the predictive accuracy of the multifidelity nonlinear reduced-order models improves substantially over their linear counterparts. However, multifidelity linear models were superior to equivalent nonlinear models for smaller input space dimensions, which may provide a useful intuition for practitioners when constructing reduced-order models for their respective problems.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Manifold Alignment-Based Nonintrusive and Nonlinear Multifidelity Reduced-Order Modeling


    Beteiligte:

    Erschienen in:

    AIAA Journal ; 61 , 1 ; 454-474


    Erscheinungsdatum :

    01.01.2023




    Medientyp :

    Aufsatz (Konferenz) , Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    NONLINEAR MULTI-FIDELITY REDUCED ORDER MODELING METHOD USING MANIFOLD ALIGNMENT

    Decker, Kenneth / Iyengar, Nikhil / Perron, Christian et al. | TIBKAT | 2021


    Nonlinear Multi-Fidelity Reduced Order Modeling Method using Manifold Alignment

    Decker, Kenneth / Iyengar, Nikhil / Perron, Christian et al. | AIAA | 2021


    Applications of Multifidelity Reduced Order Modeling to Single and Multiphysics Problems

    Song, Pengchao / Wang, X.Q. / Mignolet, Marc P. | AIAA | 2020


    Multifidelity Reduced-Dimensional Modeling for Shock-fin Interaction

    Acharya, Ragini / Hartman, Kevin / Narayanaswamy, Venkateswaran | AIAA | 2022