  Reynolds Averaged Navier Stokes (RANS) modelling is notorious for introducing
the model-form uncertainty due to the Boussinesq turbulent viscosity
hypothesis. Recently, the eigenspace perturbation method (EPM) has been
developed to estimate the RANS model-form uncertainty. This approach estimates
model-form uncertainty through injecting perturbations to the predicted
Reynolds stress tensor. However, there is a need for a reliable machine
learning method for estimating the perturbed amplitude of the Reynolds stress
tensor. Machine learning models are often too complex and data intensive for
this application. We propose a lightweight convolutional neural network (CNN)
approach to learn a correction function for RANS from paired-samples of RANS
and DNS simulation results. The CNN learned RANS correction function
successfully facilitates the RANS uncertainty quantification (UQ), and our
findings suggest that the lightweight CNN approach is effective in combining
RANS and DNS simulation results to enrich the existing perturbation method in
estimating the RANS UQ more precisely.
