A framework to develop data-driven turbulence models for flows with organised unsteadiness

Abstract

Turbulence modelling development has received a boost in recent years through assimilation of machine learning methods and increasing availability of high-fidelity datasets. This paper presents an approach that develops turbulence models for flows exhibiting organised unsteadiness. The novel framework consists of three parts. First, using triple decomposition, the high-fidelity data is split into organised motion and stochastic turbulence. A data-driven approach is then used to develop a closure only for the stochastic part of turbulence. Finally, unsteady calculations are conducted, which resolve the organised structures and model the unresolved turbulence using the developed bespoke turbul..