Bayesian uncertainty quantification applied to RANS turbulence models
Bayesian uncertainty quantification applied to RANS turbulence models
复制标题
DOI：
10.1088/1742-6596/318/4/042032
复制
发表时间：
2011-12
期刊：
影响因子：
--
通讯作者：
Todd A. Oliver;R. Moser
中科院分区：
文献类型：
--
作者：
Todd A. Oliver;R. Moser
提出了一种贝叶斯不确定性量化方法，并将其应用于充分发展的槽道流RANS湍流模型。该方法的目的是捕捉不确定性，由于不确定的参数和模型不足。通过将湍流模型的参数视为随机变量来表示参数不确定性。为了捕捉模型的不确定性，四个随机扩展的四个涡粘性湍流模型。根据贝叶斯定理，使用DNS数据校准十六个耦合模型，产生后验概率密度函数。此外，竞争的模型进行了比较，在两个项目：后验概率和预测的数量感兴趣。后验概率表示根据贝叶斯定理，数据偏好哪个模型，而预测允许评估模型差异对感兴趣的数量的影响有多强。槽流的情况下，结果表明，随机模型和湍流模型的影响预测量的兴趣。后验可检验性有利于非齐次随机模型与Chien k-ε模型的耦合.在用Reτ = 944和Reτ = 2003的数据校准后，该模型给出了Reτ = 5000时中心线速度的预测，其不确定度约为± 4%。
A Bayesian uncertainty quantification approach is developed and applied to RANS turbulence models of fully-developed channel flow. The approach aims to capture uncertainty due to both uncertain parameters and model inadequacy. Parameter uncertainty is represented by treating the parameters of the turbulence model as random variables. To capture model uncertainty, four stochastic extensions of four eddy viscosity turbulence models are developed. The sixteen coupled models are calibrated using DNS data according to Bayes' theorem, producing posterior probability density functions. In addition, the competing models are compared in terms of two items: posterior plausibility and predictions of a quantity of interest. The posterior plausibility indicates which model is preferred by the data according to Bayes' theorem, while the predictions allow assessment of how strongly the model differences impact the quantity of interest. Results for the channel flow case show that both the stochastic model and the turbulence model affect the predicted quantity of interest. The posterior plausibility favors an inhomogeneous stochastic model coupled with the Chien k-ϵ model. After calibration with data at Reτ = 944 and Reτ = 2003, this model gives a prediction of the centerline velocity at Reτ = 5000 with uncertainty of approximately ± 4%.