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
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
