  Advanced nuclear reactors often exhibit complex thermal-fluid phenomena
during transients. To accurately capture such phenomena, a coarse-mesh
three-dimensional (3-D) modeling capability is desired for modern
nuclear-system code. In the coarse-mesh 3-D modeling of advanced-reactor
transients that involve flow and heat transfer, accurately predicting the
turbulent viscosity is a challenging task that requires an accurate and
computationally efficient model to capture the unresolved fine-scale
turbulence. In this paper, we propose a data-driven coarse-mesh turbulence
model based on local flow features for the transient analysis of thermal mixing
and stratification in a sodium-cooled fast reactor. The model has a coarse-mesh
setup to ensure computational efficiency, while it is trained by fine-mesh
computational fluid dynamics (CFD) data to ensure accuracy. A novel neural
network architecture, combining a densely connected convolutional network and a
long-short-term-memory network, is developed that can efficiently learn from
the spatial-temporal CFD transient simulation results. The neural network model
was trained and optimized on a loss-of-flow transient and demonstrated high
accuracy in predicting the turbulent viscosity field during the whole
transient. The trained model's generalization capability was also investigated
on two other transients with different inlet conditions. The study demonstrates
the potential of applying the proposed data-driven approach to support the
coarse-mesh multi-dimensional modeling of advanced reactors.
