Data-driven modeling of coarse mesh turbulence for reactor transient analysis using convolutional recurrent neural networks

•Data-driven coarse mesh turbulence model based on deep neural networks that can learn from high-resolution CFD data.•The proposed Dense-CNN/LSTM architecture can efficiently learn the spatial-temporal information from transient CFD results.•Good agreement observed between model predictions and testing CFD data on reactor loss-of-flow transient case study.•Evaluated model’s generalization capability by exploring intrisic data similarity.