  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. Relying on coarse grids increases the discretization
error. Hence, a method is suggested to produce a surrogate model that predicts
the CG-CFD local errors to correct the variables of interest. Given
high-fidelity data, a surrogate model is trained to predict the CG-CFD local
errors as a function of the coarse grid local features. ML regression
algorithms are utilized to construct a surrogate model that relates the local
error and the coarse grid features. This method is applied to a
three-dimensional flow in a lid driven cubic cavity domain. The performance of
the method was assessed by training the surrogate model on the flow full field
spatial data and tested on new data (from flows of different Reynolds number
and/or computed by different grid sizes). The proposed method maximizes the
benefit of the available data and shows potential for a good predictive
capability.
