Data-driven sensor placement for fluid flows

Sensor placement for complex fluid flows is an important and challenging problem. In this talk, we present a completely data-driven and computationally efficient method for sensor placement in fluid flows. Our method leverages recent advances in data-driven reduced-order modeling and minimizes an empirical measure of the error covariance matrix. We also propose an augmented objective function for feedback control applications. We demonstrate the performance of our method for reconstruction and prediction of the complex linearized Ginzburg–Landau equation in the globally unstable regime. We also construct a low-dimensional observer-based feedback controller for the flow over an inclined flat plate that is able to suppress the wake vortex shedding in the presence of system and measurement noise.