论文标题
使用无监督的机器学习对粘性和动荡的流动区域进行稳健检测
towards a robust detection of viscous and turbulent flow regions using unsupervised machine learning
论文作者
论文摘要
我们提出了一个不变的特征空间，用于检测粘性主导和湍流区域（即边界层和唤醒）。开发的方法使用应变和旋转速率张量的主要不变性作为无监督的机器学习高斯混合模型的输入。所选的特征空间独立于用于生成处理的数据的坐标框架，因为它依赖于应变和旋转速率的主要不变性，即伽利略不变。这种方法使我们能够识别两个不同的流动区域：一个粘性的旋转区域（边界层和唤醒区域）和一个无关，无旋转区域（外流动区）。我们在层流上测试了该方法，并在$ re = 40 $ = 40 $和$ re = 3900 $的圆形圆柱体上进行流动的湍流（使用大型涡模拟）情况。模拟是使用高阶淋巴结不连续的盖金光谱元素法（DGSEM）进行的。分析获得的结果以表明高斯混合物聚类提供了一种有效的识别方法，可以识别流动中的粘性和旋转区域。我们还包括与传统传感器的比较，以表明所提出的聚类不取决于使用传统传感器时要求的任意阈值的选择。
We propose an invariant feature space for the detection of viscous dominated and turbulent regions (i.e., boundary layers and wakes). The developed methodology uses the principal invariants of the strain and rotational rate tensors as input to an unsupervised Machine Learning Gaussian mixture model. The selected feature space is independent of the coordinate frame used to generate the processed data, as it relies on the principal invariants of strain and rotational rate, which are Galilean invariants. This methodology allows us to identify two distinct flow regions: a viscous dominated, rotational region (boundary layer and wake region) and an inviscid, irrotational region (outer flow region). We test the methodology on a laminar and a turbulent (using Large Eddy Simulation) case for flows past a circular cylinder at $Re=40$ and $Re=3900$. The simulations have been conducted using a high-order nodal Discontinuous Galerkin Spectral Element Method (DGSEM). The results obtained are analysed to show that Gaussian mixture clustering provides an effective identification method of viscous dominated and rotational regions in the flow. We also include comparisons with traditional sensors to show that the proposed clustering does not depend on the selection of an arbitrary threshold, as required when using traditional sensors.
