  The accurate calculation and uncertainty quantification of the
characteristics of spent nuclear fuel (SNF) play a crucial role in ensuring the
safety, efficiency, and sustainability of nuclear energy production, waste
management, and nuclear safeguards. State of the art physics-based models,
while reliable, are computationally intensive and time-consuming. This paper
presents a surrogate modeling approach using neural networks (NN) to predict a
number of SNF characteristics with reduced computational costs compared to
physics-based models. An NN is trained using data generated from CASMO5 lattice
calculations. The trained NN accurately predicts decay heat and nuclide
concentrations of SNF, as a function of key input parameters, such as
enrichment, burnup, cooling time between cycles, mean boron concentration and
fuel temperature. The model is validated against physics-based decay heat
simulations and measurements of different uranium oxide fuel assemblies from
two different pressurized water reactors. In addition, the NN is used to
perform sensitivity analysis and uncertainty quantification. The results are in
very good alignment to CASMO5, while the computational costs (taking into
account the costs of generating training samples) are reduced by a factor of 10
or more. Our findings demonstrate the feasibility of using NNs as surrogate
models for fast characterization of SNF, providing a promising avenue for
improving computational efficiency in assessing nuclear fuel behavior and
associated risks.
