Cluster‐based network model for drag reduction mechanisms of an actuated turbulent boundary layer

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.