241
Views
3
CrossRef citations to date
0
Altmetric
Research Articles

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

ORCID Icon, , , , , & show all
Pages 1295-1318 | Received 20 Sep 2022, Accepted 28 Mar 2023, Published online: 16 May 2023

References

  • J. JERDEN, K. FREY, and W. EBERT, “A Multiphase Interfacial Model for the Dissolution of Spent Nuclear Fuel,” J. Nucl. Mater., 462, 135 (2015); https://doi.org/10.1016/j.jnucmat.2015.03.036.
  • P. E. MARINER et al., “Advances in Geologic Disposal Safety Assessment and an Unsaturated Alluvium Reference Case,” SFWD-SFWST-2018-000509, SAND2018-11858 R, Sandia National Laboratories (2018).
  • T. W. SIMPSON et al., “Design and Analysis of Computer Experiments in Multidisciplinary Design Optimization: A Review of How Far We Have Come or Not,” Proc. 12th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conf., Victoria, British Columbia, Canada, Paper 2008-5802, American Institute of Aeronautics and Astronautics (2008).
  • P. E. MARINER et al., “Progress in Deep Geologic Disposal Safety Assessment in the U.S. Since 2010,” SAND2019-12001 R, Appendix A, Sandia National Laboratories (2019).
  • F. KING and M. KOLAR, “Mathematical Implementation of the Mixed-Potential Model of Fuel Dissolution Model Version MPM-V1.0,” Report No. 06819-REP-01200-10005 R00, Ontario Hydro, Nuclear Waste Management Division (1999).
  • F. KING and M. KOLAR, “The Mixed-Potential Model for UO2 Dissolution MPM Versions V1.3 and V1.4,” Report No. 06819-REP-01200-10104 R00, Ontario Hydro, Nuclear Waste Management Division (2003).
  • J. JERDEN, V. K. GATTU, and W. EBERT, “Progress Report on Development of the Spent Fuel Degradation and Waste Package Degradation Models and Model Integration,” SFWD-SFWST-2017-000091, SFWD-SFWST-2017-000095, Argonne National Laboratory (2017).
  • J. JERDEN et al., “Used Fuel Degradation and Radionuclide Mobilization: Experimental Plan for Electrochemical Corrosion Studies,” M4FT-12AN0806011, Argonne National Laboratory (2012).
  • G. RADULESCU, “Radiation Transport Evaluations for Repository Science,” Letter Report ORNL/LTR-2011/294, Oak Ridge National Laboratory (2011).
  • E. BUCK et al., “Coupling the Mixed Potential and Radiolysis Models for Used Fuel Degradation,” FCRD-UFD-2013-000290, U.S. Department of Energy (2013).
  • T. SANTNER, B. WILLIAMS, and W. NOTZ, The Design and Analysis of Computer Experiments, Vol. 1, Springer, New York (2003).
  • C. E. RASMUSSEN and C. K. I. WILLIAMS, Gaussian Processes for Machine Learning, MIT Press (2006).
  • K. SARGSYAN, “Surrogate Models for Uncertainty Propagation and Sensitivity Analysis,” in Handbook of Uncertainty Quantification, pp. 673–698, Springer International Publishing (2017); https://doi.org/10.1007/978-3-319-12385-1_22.
  • C. B. STORLIE et al., “Implementation and Evaluation of Nonparametric Regression Procedures for Sensitivity Analysis of Computationally Demanding Models,” Reliab. Eng. Syst. Saf., 94, 11, 1735 (2009); https://doi.org/10.1016/j.ress.2009.05.007.
  • S. BEN-DAVID and S. SHALEV-SHWARTZ, Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press, Cambridge, United Kingdom (2014).
  • F. PEDREGOSA et al., “Scikit-Learn: Machine Learning in Python,” J. Mach. Learn. Res., 12, 85, 2825 (2011).
  • P. E. MARINER et al., “Surrogate Model Development of Spent Fuel Degradation for Repository Performance Assessment,” M3SF-20SN010304044. SAND2020-10797 R, Sandia National Laboratories (2020).
  • J. JERDEN, “FMD Process Model Behavior, Argonne National Laboratory” Personal Communication (2019).
  • M. B. KENNEL, “KDTREE 2: Fortran 95 and C++ Software to Efficiently Search for Near Neighbors in a Multi-Dimensional Euclidean Space,” Institute for Nonlinear Science, University of California (2004); https://arxiv.org/pdf/physics/0408067.pdf.
  • T. H. CORMEN et al., Introduction to Algorithms, 3rd ed., The MIT Press, Cambridge, Massachusetts (2009).
  • J. M. HODGES, “KDTREE 2 Library, GitHub, Inc.” Repository ( Archived 2009); https://github.com/jmhodges/kdtree2/tree/master/src-f90.
  • L. VANDER MAATEN and G. HINTON, “Visualizing Data Using t-SNE, ” J. Mach. Learn. Res., 9, 86, 2579 (2008).
  • T. HASTIE, R. TIBSHIRANI, and J. FRIEDMAN, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed., Springer, New York (2009).
  • K. P. MURPHY, Machine Learning: A Probabilistic Perspective, The MIT Press, Cambridge, Massachusetts (2012).
  • P. YIANILOS, “Data Structures and Algorithms for Nearest Neighbor Search in General Metric Spaces,” Proc. 4th Annual ACM-SIAM Symp. on Discrete Algorithms (SODA ‘93), p. 311, Society for Industrial and Applied Mathematics (1993).
  • J. H. FRIEDMAN, J. L. BENTLEY, and R. A. FINKEL, “An Algorithm for Finding Best Matches in Logarithmic Expected Time,” ACM Trans. Math. Software, 3, 3, 209 (1977); https://doi.org/10.1145/355744.355745.
  • M. ABADI et al., “TensorFlow: A System for Large-Scale Machine Learning,” presented at the 12th USENIX Conf. on Operating Systems Design and Implementation (OSDI’16), Savannah, Georgia, November 2–4, 2016.
  • T. TIELEMAN and G. HINTON, “Lecture 6.5-rmsprop, Coursera: Neural Networks for Machine Learning,” Technical Report, University of Toronto (2012).
  • P. C. LICHTNER et al., “PFLOTRAN: A Massively Parallel Reactive Flow and Transport Model for Describing Subsurface Processes,” PFLOTRAN (2020); https://pflotran.org/.
  • G. E. HAMMOND et al., “PFLOTRAN: Reactive Flow & Transport Code for Use on Laptops to Leadership-Class Supercomputers,” in Groundwater Reactive Transport Models, Chap. 6, pp. 141–159, Bentham Science Publishers (2012).
  • P. C. LICHTNER and G. E. HAMMOND, “Quick Reference Guide: PFLOTRAN 2.0 (LA-CC-09-047) Multiphase-Multicomponent-Multiscale Massively Parallel Reactive Transport Code,” Los Alamos National Laboratory (2012).
  • “PFLOTRAN Theory Guide,” PFLOTRAN; https://doc-dev.pflotran.org/theory_guide/pm_waste_form.html (current as of July 19, 2022).
  • “PFLOTRAN User’s Guide,” PFLOTRAN; https://doc-dev.pflotran.org/user_guide/cards/gdsa/waste_form_general_card.html (current as of July 19, 2022).
  • S. D. SEVOUGIAN et al., “GDSA Repository Systems Analysis FY19 Update,” SAND2019-11942R, Sandia National Laboratories (2019).
  • “Long-Term Safety for KBS-3 Repositories at Forsmark and Laxemar—A First Evaluation,” SKB TR-06-09, Svensk Kärnbränslehantering AB, Stockholm, Sweden (2006).

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.