• Nuclear Engineering and Technology
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• 제56권2호
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• pp.601-610
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• 2024

This paper gives an extensive analysis on the characterization of xenon isotopic ratios for various nuclear reactors and nuclear explosions through neutronic depletion codes. The results of the characterization can be used for discriminating the sources of the xenon isotopes' release among the nuclear explosions and nuclear reactors. The considered sources of the xenon radionuclides do not only include PWR, CANDU, and nuclear explosions using uranium and plutonium bombs, but also IRT-200 and 5MWe Yongbyon (MAGNOX reactor) research reactors operated in North Korea. A new data base (DB) on xenon isotopic activity ratios was produced using the results of the characterization, which can be used in discrimination of the sources of xenon isotopes. The results of the study show that 5MWe Yongbyon reactor has quite different characteristics in ¹³⁵Xe/¹³³Xe ratio from the PWRs and the nuclear reactors have different characteristics in ¹³⁵Xe/¹³³Xe ratios from the nuclear explosions.

• Nuclear Engineering and Technology
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• 제56권4호
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• pp.1372-1384
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• 2024

The discrimination of the source for xenon gases' release can provide an important clue for detecting the nuclear activities in the neighboring countries. In this paper, three machine learning techniques, which are logistic regression, support vector machine (SVM), and k-nearest neighbors (KNN), were applied to develop the predictive models for discriminating the source for xenon gases' release based on the xenon isotopic activity ratio data which were generated using the depletion codes, i.e., ORIGEN in SCALE 6.2 and Serpent, for the probable sources. The considered sources for the neighboring countries of South Korea include PWRs, CANDUs, IRT-2000, Yongbyun 5 MWe reactor, and nuclear tests with plutonium and uranium. The results of the analysis showed that the overall prediction accuracies of models with SVM and KNN using six inputs, all exceeded 90%. Particularly, the models based on SVM and KNN that used six or three xenon isotope activity ratios with three classification categories, namely reactor, plutonium bomb, and uranium bomb, had accuracy levels greater than 88%. The prediction performances demonstrate the applicability of machine learning algorithms to predict nuclear threat using ratios of xenon isotopic activity.