Turbulence closure modeling with data-driven techniques: physical compatibility and consistency considerations

Understand

A recent thrust in turbulence closure modeling research is to incorporate machine learning (ML) elements, such as neural networks, for the purpose of enhancing the predictive capability to a broader class of flows.

• Such a turbulence closure framework entails solving a system of equations comprised of ML functionals coupled with traditional (physics-based - PB) elements.
• While combining closure elements from fundamentally different ideologies can lead to unprecedented progress, there are many critical challenges that must be overcome.
• This study examines three such challenges: (i) Physical compatibility (or lack thereof) between ML and PB constituents of the modeling system of equations; (ii) Internal (self) consistency of the ML training process; and (iii) Formulation of an optimal objective (or loss) function for training.