Information-Based Sensor Placement for Data-Driven Estimation of Unsteady Flows

Abstract

Estimation of unsteady flowfields around flight vehicles may improve flow interactions and lead to enhanced vehicle performance. Although flowfield representations can be very high-dimensional, their dynamics can sometimes have low-order representations that may be estimated using a few, appropriately placed measurements. This paper presents a sensor-selection framework for the intended application of data-driven, flowfield estimation. This framework combines data-driven modeling, steady-state Kalman filter design, and sparse, sequential sensor selection. This paper also uses the sensor selection framework to design sensor arrays that can perform well for a collection of operating conditions. Flow estimation results on numerical data show that the proposed framework produces arrays that are highly effective at flowfield estimation for the flow behind and an airfoil at a high angle of attack using embedded pressure sensors. Analysis of the flowfields reveals that paths of impinging stagnation points along the airfoil’s surface during a shedding period of the flow are highly informative locations for placement of pressure sensors.