Machine Learning Surrogates of a Fuel Matrix Degradation Process Model for Performance Assessment of a Nuclear Waste Repository

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.

Keywords:

• Machine learning
• repository simulation
• fuel matrix degradation process model
• surrogate
• nuclear waste

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).

Additional information

Funding

This work was funded by the DOE, Office of Nuclear Energy, Spent Fuel and Waste Science and Technology.