论文标题
在涡流主导的流中对运动学和诱导单个涡流的空气动力学负载的定量分析：计算和数据驱动方法
Quantitative analysis of the kinematics and induced aerodynamic loading of individual vortices in vortex-dominated flows: a computation and data-driven approach
论文作者
论文摘要
提出了一种基于物理学的数据驱动的计算框架，用于涡旋运动学和涡流诱导的载荷负载的定量分析。这种流的特征是少数涡流结构的主要影响，但是这些流的复杂性使得很难对单个涡流水平进行这种影响进行定量分析。此处介绍的方法将机器学习启发的聚类方法与对空气动力载荷的严格数学分配结合在一起，以启用涡旋运动学和涡流诱导的空气动力学负载的详细定量分析。我们通过将其应用于165个不同的Navier-Stokes的集合中，通过正弦式俯仰式机翼将其应用于165个不同的Navier-Stokes模拟，从而证明了这种方法的实用性。由当前方法启用的洞察力包括识别该流程中混乱的时期途径，以及对领先涡流在驱动航空弹性螺距振荡中所起的作用的精确量化。
A physics-based data-driven computational framework for the quantitative analysis of vortex kinematics and vortex-induced loads in vortex-dominated problems is presented. Such flows are characterized by the dominant influence of a small number of vortex structures, but the complexity of these flows makes it difficult to conduct a quantitative analysis of this influence at the level of individual vortices. The method presented here combines machine learning-inspired clustering methods with a rigorous mathematical partitioning of aerodynamic loads to enable detailed quantitative analysis of vortex kinematics and vortex-induced aerodynamic loads. We demonstrate the utility of this approach by applying it to an ensemble of 165 distinct Navier-Stokes simulations of flow past a sinusoidally pitching airfoil. Insights enabled by the current methodology include the identification of a period-doubling route to chaos in this flow, and the precise quantification of the role that leading-edge vortices play in driving aeroelastic pitch oscillations.
