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Trajectory-optimized cluster-based network model for the sphere wake
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DOI：10.1063/5.0098655.png)
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We propose a novel trajectory-optimized cluster-based network model (tCNM) for nonlinear model order reduction from time-resolved data following Li et al. [ Cluster-based network model, J. Fluid Mech. 906, A21 (2021)] and improving the accuracy for a given number of centroids. The starting point is k-means++ clustering, which minimizes the representation error of the snapshots by their closest centroids. The dynamics is presented by flights between the centroids. The proposed trajectory-optimized clustering aims to reduce the kinematic representation error further by shifting the centroids closer to the snapshot trajectory and refining state propagation with trajectory support points. Thus, curved trajectories are better resolved. The resulting tCNM is demonstrated for the sphere wake for three flow regimes, including the periodic, quasi-periodic, and chaotic dynamics. The representation error of tCNM is five times smaller as compared to the approximation by the closest centroid. Thus, the error is at the same level as proper orthogonal decomposition (POD) of same order. Yet, tCNM has distinct advantages over POD modeling: it is human interpretable by representing dynamics by a handful of coherent structures and their transitions; it shows robust dynamics by design, i.e., stable long-time behavior; and its development is fully automatable, i.e., it does not require tunable auxiliary closure and other models. Published under an exclusive license by AIP Publishing.
Keyword:
PROPER ORTHOGONAL DECOMPOSITION
COHERENT STRUCTURES
FLOW
DYNAMICS
TURBULENCE
TRANSIENT
SYMMETRY
POD
期刊
IF:
4.3
论文数:
2.9W
被引数:
8.0W
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引用论文
Cluster-based network modeling-From snapshots to complex dynamical systems.png)
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