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Turbulence modeling in a Reynolds Averaged Navier–Stokes (RANS) setting has traditionally evolved through a combination of theory, mathematics, and empiricism. The problem of closure, resulting from the averaging process, requires an infusion of information into the various models that is often managed in an ad-hoc way or that is focused on particular classes of problems, thus diminishing the predictive capabilities of a model in other flow contexts. In this work, a proof-of-concept of a new data-driven approach of turbulence model development is presented. The key idea in the proposed framework is to use supervised learning algorithms to build a representation of turbulence modeling closure terms. The learned terms are then inserted into a Computational Fluid Dynamics (CFD) numerical simulation with the aim of offering a better representation of turbulence physics. But while the basic idea is attractive …