Linear Regression and Machine Learning for Nuclear Forensics of Spent Fuel from Six Types of Nuclear Reactors
Shengli Chen, Tianxiang Wang, Zhong Zhang, Runfeng Li, Yuan Su, Ruiyi Zhang, Cenxi Yuan, Chunyu Zhang, Jianyu Zhu
Abstract
The illicit trafficking of radioactive materials, especially weapon-grade uranium or plutonium, is a significant security threat. Nuclear forensics helps trace the illicit trafficking of radioactive materials. The present study develops the methods for the forensics of the possible origins of fuels irradiated in nuclear reactors, which are the most powerful sources producing radioactive materials, including plutonium. Three key factors are significant for irradiated fuel forensics, namely, initial 235U enrichment, burnup, and the type of irradiation nuclear reactors. The methods for the first two are determined based on experimental data of six nuclear-reactor technologies and are further verified using the neutron-transport-depletion coupling simulation of the two major commercial reactor technologies, a pressurized-water reactor (PWR) and a boiling-water reactor (BWR). In addition, three machine-learning techniques are applied to discriminate between a PWR and a BWR, which are quite similar in neutronic properties, with nice accuracy and generalization ability. In summary, the presently determined methods provide a reliable pathway to predict the origins of spent nuclear fuels.3 MoreReceived 18 August 2022Revised 7 November 2022Accepted 1 February 2023DOI:https://doi.org/10.1103/PhysRevApplied.19.034028© 2023 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasFissionNeutron physicsNuclear decayNuclear powerRadioactive wasteTheory, design & simulation of reactorsTechniquesNuclear data analysis & compilationNuclear PhysicsEnergy Science & Technology