  Wing-body junction flows occur when a boundary layer encounters an airfoil
mounted on the surface. The corner flow near the trailing edge is challenging
for the linear eddy viscosity Reynolds Averaged Navier-Stokes (RANS) models,
due to the interaction of two perpendicular boundary layers which leads to
highly anisotropic Reynolds stress at the near wall region. Recently, Xiao et
al. proposed a physics-informed Bayesian framework to quantify and reduce the
model-form uncertainties in RANS simulations by utilizing sparse observation
data. In this work, we extend this framework to incorporate the use of wall
function in RANS simulations, and apply the extended framework to the RANS
simulation of wing-body junction flow. Standard RANS simulations are performed
on a 3:2 elliptic nose and NACA0020 tail cylinder joined at their maximum
thickness location. Current results show that both the posterior mean velocity
and the Reynolds stress anisotropy show better agreement with the experimental
data at the corner region near the trailing edge. On the other hand, the prior
velocity profiles at the leading edge indicate the restriction of uncertainty
space and the performance of the framework at this region is less effective. By
perturbing the orientation of Reynolds stress, the uncertainty range of prior
velocity profiles at the leading edge covers the experimental data. It
indicates that the uncertainty of RANS predicted velocity field is more related
to the uncertainty in the orientation of Reynolds stress at the region with
rapid change of mean strain rate. The present work not only demonstrates the
capability of Bayesian framework in improving the RANS simulation of wing-body
junction flow, but also reveals the major source of model-form uncertainty for
this flow, which can be useful in assisting RANS modeling.
