Paper ID: 2303.12260

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

John Graff, Albert Medina, Francis Lagor

Estimation of unsteady flow fields around flight vehicles may improve flow interactions and lead to enhanced vehicle performance. Although flow-field representations can be very high-dimensional, their dynamics can have low-order representations and may be estimated using a few, appropriately placed measurements. This paper presents a sensor-selection framework for the intended application of data-driven, flow-field estimation. This framework combines data-driven modeling, steady-state Kalman Filter design, and a sparsification technique for sequential selection of sensors. This paper also uses the sensor selection framework to design sensor arrays that can perform well across a variety of operating conditions. Flow estimation results on numerical data show that the proposed framework produces arrays that are highly effective at flow-field estimation for the flow behind and an airfoil at a high angle of attack using embedded pressure sensors. Analysis of the flow fields 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.

Submitted: Mar 22, 2023