  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.
