  This work presents a methodology to predict a near-optimal spacing function,
which defines the element sizes, suitable to perform steady RANS turbulent
viscous flow simulations. The strategy aims at utilising existing high fidelity
simulations to compute a target spacing function and train an artificial neural
network (ANN) to predict the spacing function for new simulations, either
unseen operating conditions or unseen geometric configurations. Several
challenges induced by the use of highly stretched elements are addressed. The
final goal is to substantially reduce the time and human expertise that is
nowadays required to produce suitable meshes for simulations. Numerical
examples involving turbulent compressible flows in two dimensions are used to
demonstrate the ability of the trained ANN to predict a suitable spacing
function. The influence of the NN architecture and the size of the training
dataset are discussed. Finally, the suitability of the predicted meshes to
perform simulations is investigated.
