  We improve upon two key aspects of the Menter shear stress transport (SST)
turbulence model: (1) We propose a more robust adverse pressure gradient sensor
based on the strength of the pressure gradient in the direction of the local
mean flow; (2) We propose two alternative eddy viscosity models to be used in
the adverse pressure gradient regions identified by our sensor. Direct
numerical simulations of the Boeing Gaussian bump are used to identify the
terms in the baseline SST model that need correction, and a posteriori
Reynolds-averaged Navier-Stokes calculations are used to calibrate coefficient
values, leading to a model that is both physics driven and data informed. The
two sensor-equipped models are applied to two thick airfoils representative of
modern wind turbine applications, the FFA-W3-301 and the DU00-W-212, with
maximum thicknesses of 30% and 20% of their chord lengths, respectively. While
the baseline SST model predicts stall (onset of separation) $3^\circ$ to
$5^\circ$ late for all cases considered, the proposed models predict stall
within the margins of experimental uncertainty, which greatly improves the
prediction of the maximum lift generated. For the FFA airfoil, the models also
improve the prediction of the linear region of the lift curve likely due to
their improved prediction of a pressure-side separation at low angles of
attack. The models are shown to generalize well across the two airfoil
geometries (despite their difference in thickness) and across almost a factor
of 10 in variations in chord-based Reynolds numbers from $1.6\times10^6$ to
$1.5\times10^7$.
