论文标题
研究Karhunen-Loeve分解的能量收敛，应用于高雷诺德 - 噪声 - 空的压力驱动边界层的大涡模拟
Study of the energy convergence of the Karhunen-Loeve decomposition applied to the large-eddy simulation of a high-Reynolds-number pressure-driven boundary layer
论文作者
论文摘要
我们研究了高雷诺德 - 空位压力驱动的边界层中湍流速度场的karhunen-loève分解的能量收敛，这是模式数量的函数。能量最佳的karhunen-loève（kl）分解是从“无限”雷诺数的壁模型的大型模拟中获得的。通过对水平均匀方向使用傅立叶模式明确地使用傅立叶模式，我们能够构建完整等级的基础，我们证明我们的结果已经达到统计收敛。 KL尺寸对应于捕获总湍流动能的90％所需的每单位音量数量的数量，被发现为$ 2.4 \ times 10^5 |ω|/h^3 $（带有$ | |ω| $域量和$ h $ $ h $边界层高度）。这显着高于当前估计值，这些估计主要基于快照方法。在我们的分析中，我们仔细纠正子网格量表对这些估计的影响。
We study the energy convergence of the Karhunen-Loève decomposition of the turbulent velocity field in a high-Reynolds-number pressure-driven boundary layer as a function of the number of modes. An energy-optimal Karhunen-Loève (KL) decomposition is obtained from wall-modeled large-eddy simulations at "infinite" Reynolds number. By explicitly using Fourier modes for the horizontal homogeneous directions, we are able to construct a basis of full rank, and we demonstrate that our results have reached statistical convergence. The KL dimension, corresponding to the number of modes per unit volume required to capture 90% of the total turbulent kinetic energy, is found to be $2.4 \times 10^5 |Ω|/H^3$ (with $|Ω|$ the domain volume and $H$ the boundary layer height). This is significantly higher than current estimates, which are mostly based on the method of snapshots. In our analysis, we carefully correct for the effect of subgrid scales on these estimates.
