Prototyping of a Machine Learning–Based Burnup Measurement Capability for Pebble Bed Reactor Fuel

References

• E. J. MULDER and W. A. BOYES, “Neutronics Characteristics of a 165 MWth Xe-100 Reactor,” Nucl. Eng. Des., 357, 110415 (2020); https://dx.doi.org/10.1016/j.nucengdes.2019.110415.
   Web of Science ®Google Scholar
• Z. ZHANG et al., “Design Aspects of the Chinese Modular High-Temperature Gas-Cooled Reactor HTR-PM,” Nucl. Eng. Des., 236, 5–6, 485 (2006); https://dx.doi.org/10.1016/j.nucengdes.2005.11.024.
   Web of Science ®Google Scholar
• S. VACCARO et al., “PWR and BWR Spent Fuel Assembly Gamma Spectra Measurements,” Nucl. Instrum. Methods Phys. Res., 833, 208 (2016); https://dx.doi.org/10.1016/j.nima.2016.07.032.
   Web of Science ®Google Scholar
• S. A. ANSARI et al., “Burnup Studies of Spent Fuels of Varying Types and Enrichment,” Ann. Nucl. Energy, 34, 8, 641 (2007); https://dx.doi.org/10.1016/j.anucene.2007.02.010.
   Web of Science ®Google Scholar
• I. C. GAULD and U. MERTYUREK, “Validation of BWR Spent Nuclear Fuel Isotopic Predictions with Applications to Burnup Credit,” Nucl. Eng. Des., 345, 110 (2019); https://dx.doi.org/10.1016/j.nucengdes.2019.01.026.
   Web of Science ®Google Scholar
• C. T. NGUYEN et al., “Monitoring Burn-Up of Spent Fuel Assemblies by Gamma Spectrometry,” IEEE Trans. Nucl. Sci., 60, 2, 1107 (2013); https://dx.doi.org/10.1109/TNS.2013.2241790.
   Web of Science ®Google Scholar
• F. VITULLO et al., “Statistical Burnup Distribution of Moving Pebbles in the High-Temperature Reactor HTR-PM,” ASME J. Nucl. Rad. Sci., 6, 2, 021108 (2020); https://dx.doi.org/10.1115/1.4044910.
   Web of Science ®Google Scholar
• D. KOVACIC et al., “Advanced Reactor Safeguards: Nuclear Material Control and Accounting for Pebble Bed Reactors,” ORNL/SPR-2020/1849, Oak Ridge National Laboratory (2021).
   Google Scholar
• W. H. YAN et al., “Prototype Studies on the Nondestructive Online Burnup Determination for the Modular Pebble Bed Reactors,” Nucl. Eng. Des., 267, 172 (2014); https://dx.doi.org/10.1016/j.nucengdes.2013.12.036.
   Web of Science ®Google Scholar
• B. SU et al., “Design and Construction of a Prototype Advanced On-Line Fuel Burn-Up Monitoring System for the Modular Pebble Bed Reactor,” University of Cincinnati (2004).
   Google Scholar
• C. X. SOTO et al., “A Better Method to Calculate Fuel Burnup in Pebble Bed Reactors Using Machine Learning,” Nucl. Technol., 209, 9, 1282 (2023); https://dx.doi.org/10.1080/00295450.2023.2200573.
   Web of Science ®Google Scholar
• A. I. HAWARI and J. CHEN, “Computational Investigation of On-Line Interrogation of Pebble Bed Reactor Fuel,” IEEE Trans. Nucl. Sci., 52, 5, 1659 (2005); https://dx.doi.org/10.1109/TNS.2005.856760.
   Web of Science ®Google Scholar
• J. M. HARP, P. A. DEMKOWICZ, and J. D. STEMPIEN, “Fission Product Inventory and Burnup Evaluation of the AGR-2 Irradiation by Gamma Spectrometry,” Nucl. Eng. Des., 329, 134 (2018); https://dx.doi.org/10.1016/j.nucengdes.2017.08.005.
   Web of Science ®Google Scholar
• O. DIM et al., “Verification of TRISO Fuel Burnup Using Machine Learning Algorithms,” Proc. INMM & ESARDA Joint Virtual Annual Mtg., August 23–26 and August 30–September 1, 2021 (2021).
   Google Scholar
• D. MITCHELL and J. MATTINGLY, “Gamma Detector Response and Analysis Software (GADRAS) v. 16.0,” Sandia National Laboratories (2009); https://www.osti.gov/biblio/1231259.
   Google Scholar
• J. LEPPÄNEN et al., “The Serpent Monte Carlo Code: Status, Development, and Applications in 2013,” Ann. Nucl. Energy, 82, 142 (2015); https://dx.doi.org/10.1016/j.anucene.2014.08.024.
   Web of Science ®Google Scholar
• Y. ROBERT and M. FRATONI, “Evaluation of Serpent Capabilities for Hyperfidelity Depletion of Pebble Bed Cores,” Proc. 2021 28th Int. Conf on Nuclear Engineering, August 4–6, 2021 (2021).
   Google Scholar
• M. MATSUMOTO and T. NISHIMURA, “Mersenne Twister: A 623-Dimensionally Equidistributed Uniform Pseudo-Random Number Generator,” TOMACS, 8, 1, 3 (1998); https://dx.doi.org/10.1145/272991.272995.
   Google Scholar
• S. A. GERASIMOV, “Simple Method for Background Subtraction in Gamma-Ray Spectra,” Appl. Radiat. Isot., 43, 12, 1529 (1992); https://dx.doi.org/10.1016/0883-2889(92)90186-I.
   Web of Science ®Google Scholar
• F. PEDREGOSA et al., “Scikit-Learn: Machine Learning in Python,” JMIR, 12, 85, 2825 (2011).
   Google Scholar
• K. KUGELER and Z. ZHANG, “Modular High-Temperature Gas-Cooled Reactor Power Plant,” Springer (2019).
   Google Scholar
• L. BREIMAN, “Random Forests,” Mach. Learn., 45, 1, 5 (2001); https://dx.doi.org/10.1023/A:1010933404324.
   Web of Science ®Google Scholar