Network community-based model reduction for vortical flows
Network community-based model reduction for vortical flows
复制标题
基于网络社区的涡流模型简化
DOI：
10.1103/physreve.97.063103
复制
发表时间：
2018
影响因子：
2.4
通讯作者：
Taira, Kunihiko
中科院分区：
文献类型：
--
作者：
Gopalakrishnan Meena, Muralikrishnan;Nair, Aditya G.;Taira, Kunihiko
建立了一种基于网络的降阶模型，用于捕捉高维非定常涡旋流中相干结构之间的关键相互作用。目前的方法是数据启发和建立在网络理论技术的基础上，以确定由具有相似动力学行为的涡元素组成的重要涡群落。高维流场的整体基于相互作用的物理被提炼成涡旋群落质心，大大降低了系统维数。利用这些涡旋相互作用，所提出的方法被应用于建立涡旋流动群落间动力学的降阶模型，并预测尾流中物体的升力和阻力。我们通过精确捕捉离散点涡集合的宏观动力学，以及圆柱体和带轮尼襟翼的翼型的复杂非定常气动力来证明这些模型的能力。由于其系统约简的积分性质，本公式对模拟实验噪声和湍流具有鲁棒性。
A network community-based reduced-order model is developed to capture key interactions among coherent structures in high-dimensional unsteady vortical flows. The present approach is data-inspired and founded on network-theoretic techniques to identify important vortical communities that are comprised of vortical elements that share similar dynamical behavior. The overall interaction-based physics of the high-dimensional flow field is distilled into the vortical community centroids, considerably reducing the system dimension. Taking advantage of these vortical interactions, the proposed methodology is applied to formulate reduced-order models for the inter-community dynamics of vortical flows, and predict lift and drag forces on bodies in wake flows. We demonstrate the capabilities of these models by accurately capturing the macroscopic dynamics of a collection of discrete point vortices, and the complex unsteady aerodynamic forces on a circular cylinder and an airfoil with a Gurney flap. The present formulation is found to be robust against simulated experimental noise and turbulence due to its integrating nature of the system reduction.