  Gas-particle flows are commonly simulated through two-fluid model at
industrial-scale. However, these simulations need very fine grid to have
accurate flow predictions, which is prohibitively demanding in terms of
computational resources. To circumvent this problem, the filtered two-fluid
model has been developed, where large-scale flow field is numerically resolved
and small-scale fluctuations are accounted for through subgrid-scale modeling.
In this study, we have performed fine-grid two-fluid simulations of dilute
gas-particle flows in periodic domains and applied explicit filtering to
generate datasets. Then, these datasets have been used to develop artificial
neural network (ANN) models for closures such as the filtered drag force and
solid phase stress for the filtered two-fluid model. The set of input variables
for the subgrid drag force ANN model that has been found previously to work
well for dense flow regimes is found to work as well for the dilute regime. In
addition, we present a Galilean invariant tensor basis neural network (TBNN)
model for the filtered solid phase stress which can capture nicely the
anisotropic nature of the solid phase stress arising from subgrid-scale
velocity fluctuations. Finally, the predictions provided by this new TBNN model
are compared with those obtained from a simple eddy-viscosity ANN model.
