  CFD is widely used in physical system design and optimization, where it is
used to predict engineering quantities of interest, such as the lift on a plane
wing or the drag on a motor vehicle. However, many systems of interest are
prohibitively expensive for design optimization, due to the expense of
evaluating CFD simulations. To render the computation tractable, reduced-order
or surrogate models are used to accelerate simulations while respecting the
convergence constraints provided by the higher-fidelity solution. This paper
introduces CFDNet -- a physical simulation and deep learning coupled framework,
for accelerating the convergence of Reynolds Averaged Navier-Stokes
simulations. CFDNet is designed to predict the primary physical properties of
the fluid including velocity, pressure, and eddy viscosity using a single
convolutional neural network at its core. We evaluate CFDNet on a variety of
use-cases, both extrapolative and interpolative, where test geometries are
observed/not-observed during training. Our results show that CFDNet meets the
convergence constraints of the domain-specific physics solver while
outperforming it by 1.9 - 7.4x on both steady laminar and turbulent flows.
Moreover, we demonstrate the generalization capacity of CFDNet by testing its
prediction on new geometries unseen during training. In this case, the approach
meets the CFD convergence criterion while still providing significant speedups
over traditional domain-only models.
