Journal
JOURNAL OF FLUID MECHANICS
Volume 906, Issue -, Pages -Publisher
CAMBRIDGE UNIV PRESS
DOI: 10.1017/jfm.2020.785
Keywords
low-dimensional models; shear layers; turbulent boundary layers
Categories
Funding
- Graduate Student Research Innovation Project of Hunan Province [CX2018B027]
- China Scholarship Council (CSC) [CSC201803170267]
- German science foundation (DFG) [SE 2504/2-1]
- French National Research Agency [ANR-17-ASTR-0022]
- Bernd Noack Cybernetics Foundation
- National Natural Science Foundation of China [91441121]
- Polish Ministry of Science and Higher Education (MNiSW) [05/54/DSPB/6492]
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An automatable data-driven methodology for robust nonlinear reduced-order modelling is proposed, utilizing kinematical coarse-graining and directed network construction to resolve coherent-structure evolution.
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.
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