考虑数值离散误差的湍流模型选择引入的不确定度量化

doi:10.7527/S1000-6893.2021.25741

Abstract

Abstract: Various sources of uncertainty exist in numerical simulations of fluid mechanics. Characterization of the effects induced by uncertain factors on numerical results scientifically and quantitatively is extremely important for model validation, design, optimization and performance assessment processes of relevant products. Discretization errors, model selection and model prediction bias are three significant sources of uncertainties in numerical simulations. To take the three uncertain factors into account simultaneously, an improved Bayesian model averaging approach is proposed in this paper. The new approach begins with discretization error estimation, using curve fits between numerical predictions and grid scale to obtain the confidence interval of exact solutions to each model in possible model sets. The nested loop and conditional optimization algorithm combined with the traditional Bayesian model averaging approach are then used to construct the probability box for quantity-of-interest. Bounds of cumulative distribution function are then used for confidence interval estimation. The new approach is applied in the simulation of the low speed flow over NACA0012 airfoil and the transonic flow over CHN-T1, and the confidence intervals of the lift and drag coefficients are estimated.

Key words: National Numerical Windtunnel Project, uncertainty quantification, Bayesian model averaging, numerical discretization error, turbulence model

CLC Number:

V211.3

CHEN Jiangtao, ZHANG Chao, WU Xiaojun, ZHAO Wei. Quantification of turbulence model-selection uncertainties considering discretization errors[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(9): 625741-625741.

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Quantification of turbulence model-selection uncertainties considering discretization errors

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