  A new method for optimal sensor placement based on variable importance of
machine learned models is proposed. With its simplicity, adaptivity, and low
computational cost, the method offers many advantages over existing approaches.
The new method is implemented on the flow over an airfoil equipped with a
Coanda actuator. The analysis is based on flow field data obtained from 2D
unsteady Reynolds averaged Navier-Stokes (URANS) simulations with different
actuation conditions. The optimal sensor locations is compared against the
current de-facto standard of maximum POD modal amplitude location, and against
a brute force approach that scans all possible sensor combinations. The results
show that both the flow conditions and the type of sensor have an effect on the
optimal sensor placement, whereas the choice of the response function appears
to have limited influence.
