Abstract The paper presents a method for handling geometrical uncertainties. In this case, discretization of continuous uncertainty field leads to a large set of correlated uncertainties/random variables. In order to reduce dimensionality of the problem, the authors propose a method that takes advantage of both the probabilistic information (covariance) and the local behavior of the objective (up to a second-order derivatives). The proposed method is verified for the UMRIDA BC-03 test case (UMRIDA Consortium, Test case description innovative database for UQ and RDM, 2014). The method is shown to outperform the Karhunen-Loeve decomposition and the analysis based purely on the Hessian matrix. The method allows to keep the same level of accuracy with a significant reduction of the number of uncertainties.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Second-Order Derivatives for Geometrical Uncertainties




    Publication date :

    2018-07-21


    Size :

    15 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Geometrical Interpretation of Second-Order PMD

    Karlsson, M. | British Library Online Contents | 2006



    Estimation of the Impact of Geometrical Uncertainties on Aerodynamic Coefficients Using CFD

    Chalot, F. / Dinh, Q. / Herbin, E. et al. | British Library Conference Proceedings | 2008


    Estimation of the Impact of Geometrical Uncertainties on Aerodynamic Coefficients Using CFD

    Chalot, Frederic / Dinh, Quang / Herbin, Erick et al. | AIAA | 2008