Current needs in design, optimization, and design space exploration or sensitivity analysis of fluid dynamic problems are pushing towards the search for Computational Fluid Dynamics (CFDs) simulation tools that present both low cost and high accuracy. In this line, the methodology presented here makes use of Artificial Neural Networks (ANNs) for enhancing the results obtained by low level fluid dynamic approximations in such a way that an accuracy level comparable to that obtained with more advanced CFD simulations is achieved while maintaining much lower computational costs. Contrasting with other traditional surrogate methods where the whole physical model is replaced by a soft computing estimator acting as a black box, in the proposed approach the ANNs are trained to generate an estimation of the error produced by the low cost simulation in order to correct its results.