Coarse-Grid Computational Fluid Dynamic (CG-CFD) Error Prediction using Machine Learning

Understand

Despite the progress in high performance computing, Computational Fluid Dynamics (CFD) simulations are still computationally expensive for many practical engineering applications such as simulating large computational domains and highly turbulent flows.

• One of the major reasons of the high expense of CFD is the need for a fine grid to resolve phenomena at the relevant scale, and obtain a grid-independent solution.
• The fine grid requirements often drive the computational time step size down, which makes long transient problems prohibitively expensive.
• In the research presented, the feasibility of a Coarse Grid CFD (CG-CFD) approach is investigated by utilizing Machine Learning (ML) algorithms.