A Machine Learning Strategy to Assist Turbulence Model Development

Abstract

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, modeling unknown terms by increasingly large amounts of data from higher-fidelity simulations (LES, DNS, etc) or even experiment, the details of how to make the approach viable are not at all obvious.In this work, we investigate the feasibility of such an approach by attempting to reproduce, through a machine learning methodology, the results obtained with the well-established Spalart-Allmaras model.In other words, the key question that we seek to answer is the following: Given a number of observations of CFD solutions using the Spalart-Allmaras model (our truth model), can we reproduce those solutions using machine-learning techniques without knowledge of the structure, functional form, and coefficients of the actual model?We discuss the challenges of applying machine learning techniques in a fluid dynamic setting and possible successful approaches.We also explore the potential for machine learning as an enhancement to or replacement for traditional turbulence models.Our results highlight the potential and viability of machine learning approaches to aid turbulence model development.