Abstract
Spent nuclear fuel repository simulations are currently not able to incorporate detailed fuel matrix degradation (FMD) process models due to their computational cost, especially when large numbers of waste packages breach. The current paper uses machine learning to develop artificial neural network and k-nearest neighbor regression surrogate models that approximate the detailed FMD process model while being computationally much faster to evaluate. Using fuel cask temperature, dose rate, and the environmental concentrations of CO32−, O2, Fe2+, and H2 as inputs, these surrogates show good agreement with the FMD process model predictions of the UO2 degradation rate for conditions within the range of the training data. A demonstration in a full-scale shale repository reference case simulation shows that the incorporation of the surrogate models captures local and temporal environmental effects on fuel degradation rates while retaining good computational efficiency.
Acronyms
ANN | = | : artificial neural network |
CV | = | : cross validation |
DOE | = | : U.S. Department of Energy |
FDR | = | : fractional degradation rate |
FEP | = | : features, events, and processes |
FMD | = | : fuel matrix degradation |
H2O2 | = | : hydrogen peroxide |
kNNr | = | : k-nearest-neighbors regression |
MAE | = | : mean absolute error |
MAPE | = | : mean absolute percentage error |
MSE | = | : mean-squared error |
MTHM | = | : metric ton of heavy metal |
NMP | = | : noble metal–bearing particle |
OoR | = | : out of reactor |
PWR | = | : pressurized water reactor |
QoI | = | : quantity of interest |
ReLU | = | : rectified linear unit |
t-SNE | = | : t-distributed stochastic neighbor embedding |
Acknowledgments
The authors would like to thank Deborah Phipps for her expertise on and assistance with formatting the manuscript.
This paper has been authored by an employee of National Technology & Engineering Solutions of Sandia, LLC under contract DE-NA0003525 with the DOE. The employee owns all right, title, and interest in and to the paper and is solely responsible for its contents. The U.S. government retains and the publisher, by accepting the paper for publication, acknowledges that the U.S. government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this paper or allow others to do so, for U.S. government purposes. The DOE will provide public access to the results of federally sponsored research in accordance with the DOE Public Access Plan at https://www.energy.gov/downloads/doe-public-access-plan.
This paper describes objective technical results and analysis. Any subjective views or opinions that might be expressed in this paper do not necessarily represent the views of the DOE or the U.S. government.
Disclosure Statement
No potential conflict of interest was reported by the author(s).