Premium
Cluster‐based network model for drag reduction mechanisms of an actuated turbulent boundary layer
Author(s) -
Fernex Daniel,
Semaan Richard,
Albers Marian,
Meysonnat Pascal S.,
Schröder Wolfgang,
Ishar Rishabh,
Kaiser Eurika,
Noack Bernd R.
Publication year - 2019
Publication title -
pamm
Language(s) - English
Resource type - Journals
ISSN - 1617-7061
DOI - 10.1002/pamm.201900219
Subject(s) - boundary layer , centroid , drag , turbulence , cluster analysis , cluster (spacecraft) , reduction (mathematics) , transition point , computer science , boundary (topology) , set (abstract data type) , control theory (sociology) , algorithm , statistical physics , mechanics , physics , control (management) , mathematics , artificial intelligence , geometry , mathematical analysis , programming language
Abstract We introduce a novel data‐driven reduced‐order modeling approach, a Cluster‐Based Network Model ( CBNM ). Starting point is a set of time‐resolved snapshots associated with one or multiple control laws. These snapshots are coarse‐grained into dozens of centroids using k ‐means++ clustering. The dynamics is modelled in a network between these centroids comprising the transition probability and corresponding transit time. The transition parameters depend on the control law. CBNM is successfully applied to an actuated turbulent boundary layer flow. The results show that CBNM is an attractive alternative to POD models as the model is human interpretable and dynamically robust by construction.