A turbulent eddy-viscosity surrogate modeling framework for Reynolds-Averaged Navier-Stokes simulations

Understand

The Reynolds-averaged Navier-Stokes (RANS) equations for steady-state assessment of incompressible turbulent flows remain the workhorse for practical computational fluid dynamics (CFD) applications.

• Consequently, improvements in speed or accuracy have the potential to affect a diverse range of applications.
• We introduce a machine learning framework for the {surrogate modeling of steady-state turbulent eddy viscosities for RANS simulations, given the initial conditions.
• This modeling strategy} is assessed for parametric interpolation, while numerically solving for the pressure and velocity equations to steady state, thus representing a framework that is hybridized with machine learning.