Towards inferring reactor operations from high-level waste

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 wa...

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Published inNuclear engineering and technology Vol. 56; no. 7; pp. 2704 - 2710
Main Authors Jung, Benjamin, Figueroa, Antonio, Göttsche, Malte
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.07.2024
Elsevier
한국원자력학회
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ISSN1738-5733
2234-358X
DOI10.1016/j.net.2024.02.031

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Summary: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.
Bibliography:KISTI1.1003/JNL.JAKO202424857738144
ISSN:1738-5733
2234-358X
DOI:10.1016/j.net.2024.02.031