论文标题
经过长圆柱结构的流动建模
Modelling of Flow Past Long Cylindrical Structures
论文作者
论文摘要
湍流在工程和环境中是基础的，但是它们混乱和三维（3-D）的性质使它们在计算上的模拟昂贵。在这项工作中，研究了一种降低技术来利用提出同质方向的流动，例如挤出的二维（2-D）几何形状的唤醒流。首先，我们检查了均匀方向跨度对不可压缩流的尾流动力学的效果，经过$ re = 10^4 $的圆形圆柱体。发现即使在高度狭窄的域中，实心壁的存在也会诱导3-D结构。如果圆柱跨度为直径的50 \％，则3-D结构通过大规模的Kármán涡流迅速二维化，这是由于跨度比天然唤醒模式B不稳定性波长短[...]。有了这种物理理解，提出了一个2-D数据驱动的模型，该模型（如3-D尾流中）所示。 2-D模型是根据基于流量的局部跨度平均值的新型流量分解得出的，得出了跨度平均的Navier-Stokes（SANS）方程[...]。使用机器学习（ML）模型来提供与SANS方程的关闭。在A-Priori框架中，ML模型对SSR项进行了准确的预测，与标准的涡流模型相比，该模型完全无法捕获闭合项结构[...]。在A-posteriori分析中，尽管我们发现对动态系统的长期ML预测的已知稳定性问题的证据，但封闭的SANS方程仍然能够预测尾流指标和诱发力，其误差为1-10％。这与标准2-D模拟相比，这大约提高了数量级，同时将3D模拟的计算成本降低了99.5％。
Turbulent flows are fundamental in engineering and the environment, but their chaotic and three-dimensional (3-D) nature makes them computationally expensive to simulate. In this work, a dimensionality reduction technique is investigated to exploit flows presenting an homogeneous direction, such as wake flows of extruded two-dimensional (2-D) geometries. First, we examine the effect of the homogeneous direction span on the wake turbulence dynamics of incompressible flow past a circular cylinder at $Re=10^4$. It is found that the presence of a solid wall induces 3-D structures even in highly constricted domains. The 3-D structures are rapidly two-dimensionalised by the large-scale Kármán vortices if the cylinder span is 50\% of the diameter or less, as a result of the span being shorter than the natural wake Mode B instability wavelength[...]. With this physical understanding, a 2-D data-driven model that incorporates 3-D effects, as found in the 3-D wake flow, is presented. The 2-D model is derived from a novel flow decomposition based on a local spanwise average of the flow, yielding the spanwise-averaged Navier-Stokes (SANS) equations[...]. A machine-learning (ML) model is employed to provide closure to the SANS equations. In the a-priori framework, the ML model yields accurate predictions of the SSR terms, in contrast to a standard eddy-viscosity model which completely fails to capture the closure term structures[...]. In the a-posteriori analysis, while we find evidence of known stability issues with long-time ML predictions for dynamical systems, the closed SANS equations are still capable of predicting wake metrics and induced forces with errors from 1-10%. This results in approximately an order of magnitude improvement over standard 2-D simulations while reducing the computational cost of 3-D simulations by 99.5%.
