This work aims to explore the potential of Gaussian processes to reduce the computational load of robust optimisation problems faced with the risk function methodology. In particular, the focus is on the conditional value-at-risk function (CVaR) and shows how a reduced number of samples can be used to obtain an approximation of CVaR usable in a robust optimisation loop based on evolutionary algorithms. The method shows its effectiveness through the application to the robust aerodynamic shape design of an airfoil in the transonic regime.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Gaussian Processes for CVaR Approximation in Robust Aerodynamic Shape Design


    Additional title:

    Space Technol.Proceedings



    Conference:

    International Conference on Uncertainty Quantification & Optimisation ; 2020 ; Brussels, Belgium November 17, 2020 - November 20, 2020



    Publication date :

    2021-07-16


    Size :

    20 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Gaussian Processes for CVaR Approximation in Robust Aerodynamic Shape Design

    Morales, Elisa / Quagliarella, Domenico / Tognaccini, Renato | TIBKAT | 2021


    Risk Measures Applied to Robust Aerodynamic Shape Design Optimization

    Quagliarella, Domenico / Morales Tirado, Elisa / Bornaccioni, Andrea | Springer Verlag | 2020


    Aerodynamic Shape Design Using Hybrid Evolutionary Computation and Fitness Approximation

    Quagliarella, Domenico / Iannelli, Pierluigi / Vitagliano, Pier Luigi et al. | AIAA | 2004


    Uncertainty Propagation for Robust Aerodynamic Shape Optimization

    Papadimitriou, Dimitrios I. / Papadimitriou, Costas | AIAA | 2014