Cluster-based network model. (13th November 2020)

• Record Type: Journal Article
• Authors: Li, Hao; Fernex, Daniel; Semaan, Richard; Tan, Jianguo; Morzyński, Marek; Noack, Bernd R.
• Is Part Of: Journal of fluid mechanics. Volume 906(2021)
• Publication Date: 2020-11-13
• Subjects: low-dimensional models, -- shear layers, -- turbulent boundary layers; Fluid mechanics -- Periodicals; 532.005
• DOI: 10.1017/jfm.2020.785
• Languages: English
• ISSNs: 0022-1120

Abstract

Abstract: Abstract : We propose an automatable data-driven methodology for robust nonlinear reduced-order modelling from time-resolved snapshot data. In the kinematical coarse-graining, the snapshots are clustered into a few centroids representing the whole ensemble. The dynamics is conceptualized as a directed network, where the centroids represent nodes and the directed edges denote possible finite-time transitions. The transition probabilities and times are inferred from the snapshot data. The resulting cluster-based network model constitutes a deterministic–stochastic grey-box model resolving the coherent-structure evolution. This model is motivated by limit-cycle dynamics, illustrated for the chaotic Lorenz attractor and successfully demonstrated for the laminar two-dimensional mixing layer featuring Kelvin–Helmholtz vortices and vortex pairing, and for an actuated turbulent boundary layer with complex dynamics. Cluster-based network modelling opens a promising new avenue with unique advantages over other model-order reductions based on clustering or proper orthogonal decomposition.