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Sensitivity analysis of large-eddy simulations to subgrid-scale-model parametric uncertainty using polynomial chaos

机译:基于多项式混沌的大涡模拟对亚网格尺度模型参数不确定性的敏感性分析

摘要

We address the sensitivity of large-eddy simulations (LES) to parametric uncertainty in the subgrid-scale model. More specifically, we investigate the sensitivity of the LES statistical moments of decaying homogeneous isotropic turbulence to the uncertainty in the Smagorinsky model free parameter Cs (i.e. the Smagorinsky constant). Our sensitivity methodology relies on the non-intrusive approach of the generalized Polynomial Chaos (gPC) method; the gPC is a spectral non-statistical numerical method well-suited to representing random processes not restricted to Gaussian fields. The analysis is carried out at Reλ, =, 100 and for different grid resolutions and Cs distributions. Numerical predictions are also compared to direct numerical simulation evidence. We have shown that the different turbulent scales of the LES solution respond differently to the variability in Cs. In particular, the study of the relative turbulent kinetic energy distributions for different Cs distributions indicates that small scales are mainly affected by changes in the subgrid-model parametric uncertainty.
机译:我们讨论了大涡模拟(LES)对子网格规模模型中参数不确定性的敏感性。更具体地说,我们研究了均质各向同性湍流的LES统计矩对Smagorinsky模型自由参数Cs(即Smagorinsky常数)中的不确定性的敏感性。我们的敏感性方法依赖于广义多项式混沌(gPC)方法的非介入方法; gPC是一种光谱非统计数值方法,非常适合表示不限于高斯场的随机过程。针对不同的网格分辨率和Cs分布,以Reλ= 100进行分析。还将数值预测与直接数值模拟证据进行比较。我们已经表明,LES解的不同湍流尺度对Cs的变异性有不同的响应。特别是,对不同Cs分布的相对湍动能分布的研究表明,小尺度主要受亚网格模型参数不确定性变化的影响。

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