Application of flux vector splitting methods with SST turbulence model to wall-bounded flows

Manokaran, K.; Ramakrishna, M.; Jayachandran, T.

doi:10.1016/j.compfluid.2020.104611

Cited by 8 publications

(1 citation statement)

References 12 publications

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“…The

k-\omega

model is used near the wall, and the

k-\varepsilon

turbulence model is used in the fully developed flow field far from the wall, which fully combines the advantages of the

k-\omega

model with high accuracy and robustness in the simulation of separated flow and the low sensitivity of the

k-\varepsilon

model to the initial parameters of turbulence (the boundary layer has nothing to do with the free incoming flow). Therefore, SST has become one of the most widely used turbulence models, and the specific introduction and derivation can be referred to References 10, 15, 24.…”

Section: Sst Turbulence Modelmentioning

confidence: 99%

Uncertainty quantification and identification of SST turbulence model parameters based on Bayesian optimization algorithm in supersonic flow

Yang,

Guo,

Zhang

et al. 2023

Numerical Methods in Fluids

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The Reynolds‐Averaged Navier–Stokes (RANS) model is the main model in engineering applications today. However, the normal value of the closure coefficient of the RANS turbulence model is determined based on some simple basic flows and may no longer be applicable for complex flows. In this paper, the closure coefficient of shear stress transport (SST) turbulence model is recalibrated by combining Bayesian method and particle swarm optimization algorithm, so as to improve the numerical simulation accuracy of wall pressure in supersonic flow. First, the obtained prior samples were numerically calculated, and the Sobol index of the closure coefficient was calculated by sensitivity analysis method to characterize the sensitivity of the wall pressure to the model parameters. Second, combined with the uncertainty of propagation parameters by non‐intrusive polynomial chaos (NIPC). Finally, Bayesian optimization is used to quantify the uncertainty and obtain the maximum likelihood function estimation and optimal parameters. The results show that the maximum relative error of wall pressure predicted by the SST turbulence model decreases from 29.71% to 9.00%, and the average relative error decreases from 9.86% to 3.67% through the parameter calibration of Bayesian optimization method. In addition, the system evaluated the calibration effect of three criteria, and the calibration results parameters under the three criteria were all better than the calculated results of the nominal values. Meanwhile, the velocity profile and density profile of the flow field were also analyzed. Finally, the same calibration method was applied to the supersonic hollow cylinder and BSL (Baseline) turbulence model, and the same calibration results were obtained, which verified the universality of the calibration method.

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“…The