Optimal estimation for Large-Eddy Simulation of turbulence and application to the analysis of subgrid models
Optimal estimation for Large-Eddy Simulation of turbulence and application to the analysis of subgrid models
复制标题
DOI：
10.1063/1.2357974
复制
发表时间：
2006-06
期刊：
影响因子：
--
通讯作者：
A. Moreau;O. Teytaud;J. Bertoglio
中科院分区：
文献类型：
--
作者：
A. Moreau;O. Teytaud;J. Bertoglio
最优估计工具应用于湍流大涡模拟的亚网格模型研究。引入了最优估计器的概念，并在子网格模型先验测试的应用中分析了其属性。注意力集中在各向同性湍流中标量场情况下的库克和赖利模型。使用 DNS 数据，通过计算 (i) 广义最优估计量和 (ii) 仅由该假设带来的误差来估计 beta 假设的相关性。使用各种变量集和各种技术（直方图和神经网络）计算子网格方差的最佳估计量。结果表明，最优估计器允许对模型进行彻底的探索。神经网络在该框架中被证明是相关且非常有效的，并建议进一步使用。
The tools of optimal estimation are applied to the study of subgrid models for Large-Eddy Simulation of turbulence. The concept of optimal estimator is introduced and its properties are analyzed in the context of applications to a priori tests of subgrid models. Attention is focused on the Cook and Riley model in the case of a scalar field in isotropic turbulence. Using DNS data, the relevance of the beta assumption is estimated by computing (i) generalized optimal estimators and (ii) the error brought by this assumption alone. Optimal estimators are computed for the subgrid variance using various sets of variables and various techniques (histograms and neural networks). It is shown that optimal estimators allow a thorough exploration of models. Neural networks are proved to be relevant and very efficient in this framework, and further usages are suggested.