Abstract Quantitatively analyze the uncertainties in turbulence models is of great meaning for research and engineering. In this paper, we develop a Bayesian uncertainty analysis framework based on copulas. The proposed framework is applied to the parametric uncertainty quantification and calibration of the shear-stress transport turbulence model for the flow over a Gaussian bump. A copula function is used to establish the correlation between variables and construct the weight function, which enables greater consideration of Bayesian inference. Prior analysis is conducted based on Sobol indices with the nonintrusive polynomial chaos method, demonstrating the important effects of κ , a 1 , and β on the flow, both for the wall force coefficients and the Reynolds shear stress. Next, the wall force coefficients and Reynolds shear stress are used as training sets to verify the effectiveness of the method. The results show that modeling the relationship based on copulas is necessary and effective, and that the weight function adaptively balances the influence of physical quantities at different inference locations. The proposed method effectively corrects the computational deviations of the corresponding physical quantities. In addition, the confidence interval for the posterior sample is increased, enhancing the likelihood of obtaining results close to the actual values.

    Highlights A copula-based Bayesian uncertainty quantification framework with adaptive weight function is proposed. Gaussian bump is a recently proposed meaningful geometry shape, attracting wide concerns, but has not been studied enough by now. Detailed and innovative prior and posterior analysis is conducted. The physical mechanisms of the parameters for separated flow are analyzed.


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    Title :

    Copula-based Bayesian uncertainty quantification framework of SST turbulence model for flow over a Gaussian bump


    Contributors:
    Li, Yao (author) / Zhang, Jin-rong (author) / Wu, Wan-tong (author) / Jiang, Zhen-hua (author) / Tang, Deng-gao (author) / Yan, Chao (author)

    Published in:

    Acta Astronautica ; 216 ; 229-245


    Publication date :

    2024-01-03


    Size :

    17 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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