This paper aims to quantify the uncertainty in the SAS-SST simulation of a prism bluff-body flow due to varying the higher-wavenumber damping factor ($C_s$). Instead of performing the uncertainty quantification on the CFD simulation directly, a surrogate modelling approach is adopted. The mesh sensitivity is first studied and the numerical error due to the mesh is approximated accordingly. The Gaussian processes/Kriging method is used to generate surrogate models for quantities of interest (QoIs). The suitability of the surrogate models is assessed using the leave-one-out cross-validation tests (LOO-CV). The stochastic tests are then performed using the cross-validated surrogate models to quantify the uncertainty of QoIs by varying $C_s$. Four prior probability density functions (such as $U\left(0,1\right)$, $N\left({0.5,0.1}^2\right)$, $Beta\left(2,2\right)$ and $Beta\left(5,1.5\right)$) of $C_s$ are considered.

It is demonstrated in this study that the uncertainty of a predicted QoI due to varying $C_s$ is regionally dependent. The flow statistics in the near wake of the prism body are subject to larger variance due to the uncertainty in $C_s$. The influence of $C_s$ rapidly decays as the location moves downstream. The response of different QoIs to the changing $C_s$ varies greatly. Therefore, the calibration of $C_s$ only using observations of one variable may bias the results. Last but not least, it is important to consider different sources of uncertainties within the numerical model when scrutinising a turbulence model, as ignoring the contributions to the total error may lead to biased conclusions.

本文旨在量化 SAS-SST 模拟棱柱形钝体流中由于高波数阻尼因子变化而产生的不确定性。$C_{秒}$）。不是直接对 CFD 模拟执行不确定性量化，而是采用代理建模方法。首先研究网格敏感性并相应地近似由于网格引起的数值误差。高斯过程/克里金法用于生成感兴趣量 (QoI) 的替代模型。使用留一法交叉验证测试 (LOO-CV) 评估替代模型的适用性。然后使用交叉验证的替代模型执行随机测试，通过改变不同的变量来量化 QoI 的不确定性。$C_{秒}$. 四个先验概率密度函数（如$你\left(0,1\right)$, $N\left({0.5,0.1}^2\right)$, $乙\mathrm{电子}吨\mathrm{一种}\left(2,2\right)$ 和 $乙\mathrm{电子}吨\mathrm{一种}\left(5,1.5\right)$） 的 $C_{秒}$ 被考虑。

本研究表明，由于变化的影响，预测的 QoI 的不确定性 $C_{秒}$具有地域依赖性。由于不确定性，棱柱体附近尾流的流动统计数据有较大的方差。$C_{秒}$. 的影响$C_{秒}$随着位置向下游移动，迅速衰减。不同 QoI 对变化的反应$C_{秒}$漂浮不定。因此，校准$C_{秒}$仅使用对一个变量的观察可能会使结果产生偏差。最后但并非最不重要的一点是，在检查湍流模型时考虑数值模型中不确定性的不同来源很重要，因为忽略对总误差的贡献可能会导致有偏见的结论。