Using Machine Learning to Augment Coarse-Grid Computational Fluid Dynamics Simulations

Understand

Simulation of turbulent flows at high Reynolds number is a computationally challenging task relevant to a large number of engineering and scientific applications in diverse fields such as climate science, aerodynamics, and combustion.

• Turbulent flows are typically modeled by the Navier-Stokes equations.
• Direct Numerical Simulation (DNS) of the Navier-Stokes equations with sufficient numerical resolution to capture all the relevant scales of the turbulent motions can be prohibitively expensive.
• Simulation at lower-resolution on a coarse-grid introduces significant errors.