Network community-based model reduction for vortical flows
arXiv:1805.01031 · doi:10.1103/PhysRevE.97.063103
Abstract
A network community-based reduced-order model is developed to capture key interactions amongst 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.
References in corpus (28)
- Fast unfolding of communities in large networks
- The structure and function of complex networks
- Finding and evaluating community structure in networks
- Modularity and community structure in networks
- Community detection in graphs
- Finding community structure in very large networks
- Error and attack tolerance of complex networks
- Fast algorithm for detecting community structure in networks
- Finding community structure in networks using the eigenvectors of matrices
- Discovering governing equations from data: Sparse identification of nonlinear dynamical systems
- Resolution limit in community detection
- Analysis of weighted networks
- Community Structure in Time-Dependent, Multiscale, and Multiplex Networks
- Statistical Mechanics of Community Detection
- Structural Vulnerability of the North American Power Grid
- Community structure in directed networks
- Detecting fuzzy community structures in complex networks with a Potts model
- Community detection in networks with positive and negative links
- Spectral methods for network community detection and graph partitioning
- Cluster-based reduced-order modelling of a mixing layer
- A Spectral Clustering Approach to Lagrangian Vortex Detection
- Network inference - with confidence - from multivariate time series
- Network Structure of Two-Dimensional Decaying Isotropic Turbulence
- Multiway spectral community detection in networks
- Coherent structure coloring: identification of coherent structures from sparse data using graph theory
- Network-theoretic approach to sparsified discrete vortex dynamics
- Complex Networks Unveiling Spatial Patterns in Turbulence
- Airfoil wake modification with Gurney flap at Low-Reynolds number
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- Network broadcast analysis and control of turbulent flows
- Cluster-based hierarchical network model of the fluidic pinball -- Cartographing transient and post-transient, multi-frequency, multi-attractor behaviour
- Modal Analysis of Fluid Flows: Applications and Outlook
- Randomized methods to characterize large-scale vortical flow network
- Crossroads of the mesoscale circulation
- Wall-induced anisotropy effects on turbulent mixing in channel flow: A network-based analysis
- Network-theoretic modeling of fluid-structure interactions
- Evolution of clusters of turbulent reattachment due to shear layer instability in flow past a circular cylinder
- Network-based analysis of fluid flows: Progress and outlook
- Revealing essential dynamics from high-dimensional fluid flow data and operators
- Vortical interactions in turbulent thermoacoustic systems