Abstract The accurate simulation of complex turbulent flow has remained a challenging issue in computational fluid dynamics and plays a crucial role in engineering practice. Due to computational limitations, the Reynolds Averaged Navier-Stokes (RANS) simulation method continues to be the preferred choice for engineers to solve practical problems. Nonetheless, there are concerns regarding the applicability of RANS models to complex flows such as those with large-scale separation or jet interaction, as recommended coefficients may not be suitable. This work aims to perform a specified analysis of parameters' uncertainty in the Speziale-Sarkar-Gatsk/Launder-Reece-Rodi (SSG/LRR)-ω turbulence model. This work employs the non-intrusive polynomial chaos (NIPC) method to establish a surrogate model between parameters and computational results for uncertainty quantification. Sensitivity analysis is conducted via the Sobol index to identify key parameters critical for different flow structures. Then, Bayesian inference is applied for calibrating parameters by leveraging calculation results and two groups of experimental data, respectively. The results show that the calibrated values for the key parameters could significantly improve prediction accuracy. Subsequently, the work also delves into the inadequacies of SSG/LRR-ω models when applied to separated flows, revealing an underestimation of Reynolds stress in the separation zone as a primary source of poor predictions. Furthermore, it is considered that using a set of parameter combinations with a larger value of C 3 ω after the separation of the flow will obtain better calculation results in the separation zone.

    Highlights Uncertainty quantification is performed by a surrogate model constructed by the non-invasive polynomial chaos method. The Bayesian method is employed to evalute and correct the SSG/LRR-ω model. The specific reasons for the inaccurate predictions of the model applied to separated flows are analyzed. The improvement measures of the SSG/LRR-ω model are discussed.


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    Title :

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


    Contributors:
    Yang, Jin-tao (author) / Li, Yao (author) / Li, Jin-ping (author) / Yan, Chao (author)

    Published in:

    Acta Astronautica ; 211 ; 177-191


    Publication date :

    2023-06-05


    Size :

    15 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English