The exploration and trade-off analysis of different aerodynamic design configurations requires solving optimization problems. The major bottleneck to assess the optimal design is the large number of time-consuming evaluations of high-fidelity computational fluid dynamics (CFD) models, necessary to capture the non-linear phenomena and discontinuities that occur at higher Mach number regimes. To address this limitation, we introduce an original non-myopic multifidelity Bayesian framework aimed at including expensive high-fidelity CFD simulations for the optimization of the aerodynamic design. Our scheme proposes a novel two-step lookahead policy to maximize the improvement of the solution quality considering the rewards of future steps, and combines it with utility functions informed by the fluid dynamic regime and the information extracted from data, to wisely select the aerodynamic model to interrogate. We validate the proposed framework for the case of a constrained drag coefficient optimization problem of a NACA 0012 airfoil, and compare the results to other popular multifidelity and single-fidelity optimization frameworks. The results suggest that our strategy outperforms the other approaches, allowing to significantly reduce the drag coefficient through a principled selection of limited evaluations of the high-fidelity CFD model.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    NM-MF: Non-Myopic Multifidelity Framework for Constrained Multi-Regime Aerodynamic Optimization


    Beteiligte:

    Erschienen in:

    AIAA Journal ; 61 , 3 ; 1270-1280


    Erscheinungsdatum :

    01.03.2023




    Medientyp :

    Aufsatz (Konferenz) , Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch






    Multilevel-Multifidelity Acceleration of PDE-Constrained Optimization

    Monschke, Jason A. / Eldred, Michael S. | AIAA | 2017


    Multifidelity Aerodynamic Optimization of a Helicopter Rotor Blade

    Bailly, Joëlle / Bailly, Didier | AIAA | 2019