Moving Surface Actuation, Effects of Frequency-based Shear Layer Excitation on the Response of a Bluff Body Wake

Abstract

The effect of actuation frequency, using moving surface actuation, is investigated for a square cylinder bluff body wake. Pressure sensor data are used to optimize actuation characteristics through the implementation of an NSGA-II evolutionary algorithm. Velocity field data are obtained using Particle Image Velocimetry (PIV) for baseline and optimized actuation cases. A Proper Orthogonal Decomposition (POD) analysis shows that the vortex shedding frequency shifts between frequencies associated with the actuation, moving between regions of lock-on and quasi-periodicity. Additionally, the POD shows that the energy contained in the coherent shedding motion is reduced through actuation, while the total energy in the velocity field stays relatively constant. A reconstruction of the first 10 POD modes indicates that the coherent contribution to the Reynolds stresses significantly decreases compared to the non-actuated case. The mechanism for drag reduction is investigated using the shed circulation flux and Kochin’s drag formulation model. The drag obtained using PIV measurements and Kochin’s formulation is consistent with trends observed for the base pressure as a function of actuation frequency.

Closed‐loop adaptive model predictive control of a bluff body wake

“…Each trial contained 4346 snapshots captured at a rate of 1507 frame pairs/second, with images within a pair separated by dt = 70 µs. Additional PIV details can be found in [3]. Fourier analysis and proper orthogonal decomposition [5] are used to analyse the velocity field data.…”

“…These shear layer excitations manipulate the circulation generation and transport to exploit flow instabilities and alter the wake dynamics such as vortex shedding. Frequency-based actuation can achieve authority of the flow [3] with lower energy input compared to low frequency actuation. A LSTM model relating cylinder RPM and obstacle surface pressure will be trained from experimental data and used to achieve flow control using MPC.…”