Towards inferring reactor operations from high-level waste
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초록
Nuclear archaeology research provides scientific methods to reconstruct the operating histories of fissile material production facilities to account for past fissile material production. While it has typically focused on analyzing material in permanent reactor structures, spent fuel or high-level waste also hold information about the reactor operation. In this computational study, we explore a Bayesian inference framework for reconstructing the operational history from measurements of isotope ratios from a sample of nuclear waste . We investigate two different inference models. The first model discriminates between three potential reactors of origin (Magnox, PWR, and PHWR) while simultaneously reconstructing the fuel burnup, time since irradiation, initial enrichment, and average power density. The second model reconstructs the fuel burnup and time since irradiation of two batches of waste in a mixed sample. Each of the models is applied to a set of simulated test data, and the performance is evaluated by comparing the highest posterior density regions to the corresponding parameter values of the test dataset. Both models perform well on the simulated test cases, which highlights the potential of the Bayesian inference framework and opens up avenues for further investigation.
Abstract
Nuclear archaeology research provides scientific methods to reconstruct the operating histories of fissile material production facilities to account for past fissile material production. While it has typically focused on analyzing material in permanent reactor structures, spent fuel or high-level waste also hold information about the reactor operation. In this computational study, we explore a Bayesian inference framework for reconstructing the operational history from measurements of isotope ratios from a sample of nuclear waste . We investigate two different inference models. The first model discriminates between three potential reactors of origin (Magnox, PWR, and PHWR) while simultaneously reconstructing the fuel burnup, time since irradiation, initial enrichment, and average power density. The second model reconstructs the fuel burnup and time since irradiation of two batches of waste in a mixed sample. Each of the models is applied to a set of simulated test data, and the performance is evaluated by comparing the highest posterior density regions to the corresponding parameter values of the test dataset. Both models perform well on the simulated test cases, which highlights the potential of the Bayesian inference framework and opens up avenues for further investigation.
참고문헌 (16)
- Alexander Glaser, "Verifiable Declarations of Fissile Material Stocks: Challenges and Solutions, FM(C)T Meeting Series", pp.25~38 (2017), UNIDIR
- A Graphite Isotope Ratio Method: A Primer on Estimating Plutonium Production in Graphite Moderated Reactors, "Christopher J. Gesh", Pacific Northwest National Laboratory, ~, pp.Technical Report PNNL-14568 (2004)
- Alex Gasner, "Nuclear archaeology for heavy-water-moderated plutonium production reactors", Sci. Global Secur, 19(3), pp.223~233 (2011)[DOI]
- Patrick J. O’Neal, "A machine learning method for the forensics attribution of separated plutonium", Nucl. Sci. Eng, 196(7), pp.811~823 (2022)[DOI]
- Shengli Chen, "Linear regression and machine learning for nuclear forensics of spent fuel from six types of nuclear reactors", Phys. Rev. A, 19(3), pp.034028~ (2023)[DOI]
- Antonio Figueroa, "Nuclear archaeology: Reconstructing reactor histories from reprocessing waste", ESARDA Bull, (59), pp.38~46 (2019)
- Antonio Figueroa, "Nuclear archaeology based on measurements of reprocessing waste: first experimental results", INMM, 2021, ~
- A. Gelman, "Bayesian Data Analysis"
- John Salvatier, "Probabilistic programming in Python using PyMC3", PeerJ Comput. Sci, 2, pp.e55~ (2016)[DOI]
- Antonio Figueroa, "Gaussian processes for surrogate modeling of discharged fuel nuclide compositions", Ann. Nucl. Energy, 156, pp.108085~ (2021)[DOI]
- Jaakko Leppänen, "The serpent Monte Carlo code: status, development and applications in 2013", Ann. Nucl. Energy, 82, pp.142~150 (2015)[DOI]
- Paul K. Romano, "OpenMC : A state-of-the-art Monte Carlo code for research and development", Ann. Nucl. Energy, 82, pp.90~97 (2015)[DOI]
- Julien de Troullioud de Lanversin, "ONIX : An opensource depletion code", Ann. Nucl. Energy, 151, pp.107903~ (2021)[DOI]
- Alex Ulianov, "The ICPMS signal as a Poisson process : A review of basic concepts", J. Anal. At. Spectrom, 30(6), pp.1297~1321 (2015)[DOI]
- D. A. Brown, "ENDF", B-VIII.0: the 8 th major release of the nuclear reaction data library with CIELO-project cross sections, new standards and thermal scattering data, 2018, pp.148, 1~142
- D. A. Brown, "Status of the ENDF", B Nuclear Data Library, 2014, pp.118, 98~103