Abstract
Abstract
Highlights
We propose a cluster-based network model (CNM) from time-resolved snapshot data exemplified for a laminar mixing layer and an actuated turbulent boundary layer
This study aims at a cluster-based network model (CNM) with improved dynamics resolution following Fernex et al (2019)
The dynamics of cluster-based Markov model (CMM) is illustrated for the first cluster probability p1 and the first proper orthogonal decomposition (POD) mode amplitude a1 inferred from the flow state (2.19)
Summary
We propose a cluster-based network model (CNM) from time-resolved snapshot data exemplified for a laminar mixing layer and an actuated turbulent boundary layer. The goal is purely data-driven reduced-order modelling trading the physical insights from first principles, e.g. the Galerkin method (see e.g. Holmes et al 2012), with simplicity, robustness and closeness to the original data
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