A proposal for automated turbulence modelling

Abstract

Solving the Reynolds-averaged Navier-Stokes equations (RANS) closed with an eddy viscosity computed through a turbulence model is still the leading approach for Computational Fluid Dynamics simulations. Unfortunately, universal models with good predictive capabilities over a wide range of flows are not available. In this work, we propose the use of machine learning to improve existing RANS models. The approach does not require high-fidelity training data. A convolutional neural network is used to identify and segment at runtime the flow field into different zones, each resembling one item of a predefined list of elementary flows. The turbulence model applied in each zone is taken from an equally predefined set of classic models, each specifically tuned to work best for one elementary flow, free from the requirement of universality. The idea is first presented in general terms, and then demonstrated via a preliminary implementation, where only three elementary flows are considered, and three turbulence models are used. Test cases show that, already in this oversimplified form, automated zonal modelling outperforms the baseline RANS models without computational overhead.