The Bayesian uncertainty quantification technique has become well-established in turbulence modeling over the past few years. However, it is computationally expensive to construct a globally accurate surrogate model for Bayesian inference in a high-dimensional design space, which limits uncertainty quantification for complex flow configurations. Borrowing ideas from stratified sampling and inherited sampling, an adaptive model refinement approach is proposed in this work, which concentrates on asymptotically improving the local accuracy of the surrogate model in the high-posterior-density region by adaptively appending model evaluation points. To achieve this goal, a modification of inherited Latin hypercube sampling is proposed and then integrated into the Bayesian framework. The effectiveness and efficiency of the proposed approach are demonstrated through a two-dimensional heat source inversion problem and its extension to a high-dimensional design space. Compared with the prior-based method, the adaptive model refinement approach has the ability to obtain more reliable inference results using fewer evaluation points. Finally, the approach is applied to parametric uncertainty quantification of the Menter shear-stress transport turbulence model for an axisymmetric transonic bump flow and provides convincing numerical results.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Adaptive Model Refinement Approach for Bayesian Uncertainty Quantification in Turbulence Model


    Contributors:
    Zeng, Fanzhi (author) / Zhang, Wei (author) / Li, Jinping (author) / Zhang, Tianxin (author) / Yan, Chao (author)

    Published in:

    AIAA Journal ; 60 , 6 ; 3502-3516


    Publication date :

    2022-03-10


    Size :

    15 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





    Uncertainty quantification of SSG/LRR-ω turbulence model closure coefficients

    Yang, Jin-tao / Li, Yao / Li, Jin-ping et al. | Elsevier | 2023


    Uncertainty Quantification of k-ω Turbulence Model for Hypersonic Flow

    Zhao, Yatian / Liu, Hongkang / Yan, Chao | IEEE | 2018


    CALIBRATION OF AN EXTENDED EDDY VISCOSITY TURBULENCE MODEL USING UNCERTAINTY QUANTIFICATION

    Subbian, Gokul / Souza, Ana Carolina Botelho E. / Radespiel, Rolf et al. | TIBKAT | 2020


    Calibration of an extended Eddy Viscosity Turbulence Model using Uncertainty Quantification

    Subbian, Gokul / Botelho e Souza, Ana Carolina / Radespiel, Rolf et al. | AIAA | 2020