Research on improving the performance of real-time feedback control of the flow velocity field behind a cylinder using sparse processing PIV and plasma actuators

We investigated ways to improve the control performance of a feedback control system for flow velocity fields, using sparse processing PIV (SPPIV) as the state observer and a plasma actuator (PA) as the controller. SPPIV is a method for estimating the entire flow field from a few observation points. The flow velocity field information obtained is applied to a linear low-dimensional model, and the PA is driven in real time based on the control rule derived from this model. In this study, we conducted a parameter study on the number of estimation modes and control rules to verify changes in control effectiveness. The control target was to suppress the Karman vortex behind a cylindrical model, and by quantitatively evaluating control effectiveness with a focus on the asymmetry of the flow field, we clarified that setting appropriate parameters is crucial for improving control effectiveness.