Nuclear Engineering and Design ( IF 1.7 ) Pub Date : 2021-06-21 , DOI: 10.1016/j.nucengdes.2021.111307 Yu Duan , Ji Soo Ahn , Matthew D. Eaton , Michael J. Bluck
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 (). 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 . Four prior probability density functions (such as , , and ) of are considered.
It is demonstrated in this study that the uncertainty of a predicted QoI due to varying is regionally dependent. The flow statistics in the near wake of the prism body are subject to larger variance due to the uncertainty in . The influence of rapidly decays as the location moves downstream. The response of different QoIs to the changing varies greatly. Therefore, the calibration of 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.
中文翻译:
SAS-SST 模拟中由未知高波数阻尼因子引起的不确定性的量化
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。)。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。. 四个先验概率密度函数(如, , 和 ) 的 被考虑。
本研究表明,由于变化的影响,预测的 QoI 的不确定性 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。. 的影响随着位置向下游移动,迅速衰减。不同 QoI 对变化的反应漂浮不定。因此,校准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。