Thermal stratification prediction in reactor system based on CFD simulations accelerated by a data-driven coarse-grid turbulence model

Thermal stratification in large enclosures is an integral phenomenon to nuclear reactor system safety. Currently,
the effective model for thermal stratification utilizes a multi-scale method that integrates 1-D system-level and 3-
D CFD code, which offers thermal stratification details while supplying system-level data across various domains.
Nonetheless, harmonizing two codes that operate on different spatial and temporal scales presents a significant
challenge, with high-resolution CFD simulations requiring substantial computational resources. This study
introduced a data-driven coarse-grid turbulence model based on local flow characteristics at a significantly
coarser scale, targeting improved efficiency and accuracy in reactor safety analysis concerning thermal strati-
fication. A machine learning framework has been introduced to expedite the RANS-solving process by coupling
OpenFOAM and TensorFlow, which entails training a deep neural network with fine-grid CFD-generated data to
predict turbulent eddy viscosity. The feasibility of the developed data-driven turbulence model was proven
through the SUPERCAVNA experimental facility problem validation.