  Reynolds-Averaged Navier-Stokes(RANS) method will still play a vital role in
the following several decade in aerospace engineering. Although RANS models are
widely used, empiricism and large discrepancies between models reduce the
reliability of simulating complex flows. Therefore, in recent years,
data-driven turbulence model has aroused widespread concern in fluid mechanics.
Based on the experimental/numerical simulation results, this approach aims to
modify or construct the turbulence model for specific purposes by machine
learning technologies. In this paper, we take the results calculated by SA
model as training data. Different from low Reynolds number turbulent flows, the
data from high Reynolds number flows shows an apparent scaling effect, thus
leading to difficulties in the data-driven modeling. In order to improve the
fitting accuracy, we divided the flow field into near-wall region, wake region,
and far-field region, and built individual model for every region. In this
paper, we adopted the radial basis function neural network (RBFNN) and some
auxiliary optimization algorithms to reconstruct a mapping function between
mean variables and the eddy viscosity. Since this model reflects the
relationship between local flow characteristics and turbulent eddy viscosity,
it is independent on the airfoil shape and flow condition. The training data in
this paper is generated from only three subsonic flow calculations of NACA0012
airfoil. By coupling the proposed approach with N-S equations, we calculated
various flow cases as well as two different airfoils and showed the eddy
viscosity contours, velocity profiles along the normal direction of wall and
skin friction coefficient distributions, etc. Compared with the SA model, the
results show a reasonable accuracy and better efficiency, which indicates the
positive prospect of data-driven methods in turbulence modeling.
