Mesoscale simulations of fission gas release (FGR) in nuclear fuel provide a
powerful tool for understanding how microstructure evolution impacts FGR, but
they are computationally intensive. In this study, we present an alternate,
data-driven approach, using deep learning to predict instantaneous FGR flux
from 2D nuclear fuel microstructure images. Four convolutional neural network
(CNN) architectures with multiscale regression are trained and evaluated on
simulated FGR data generated using a hybrid phase field/cluster dynamics model.
All four networks show high predictive power, with R2 values above 98%.
The best performing network combine a Convolutional Block Attention Module
(CBAM) and InceptionNet mechanisms to provide superior accuracy (mean absolute
percentage error of 4.4%), training stability, and robustness on very low
instantaneous FGR flux values.Comment: Submitted at Journal of Nuclear Materials, 20 pages, 10 figures, 3
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