Prediction of MgO volume fraction in an ADS fresh fuel for the scenario code CLASS |
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| Affiliation: | 1. Subatech, EMN-IN2P3/CNRS-Université, Nantes, F-44307, France;2. IPNO, CNRS-IN2P3/Univ. Paris Sud, France;3. LPSC, CNRS-IN2P3/UJF/INPG, France;1. Research Laboratory for Nuclear Reactors, Tokyo Institute of Technology, 2-12-1, N1-7, O-okayama, Meguro-ku, Tokyo 152-8550, Japan;2. Department of Mechanical Engineering, Muroran Institute of Technology, 27-1, Mizumoto-cho, Muroran, Hokkaido 050-8585, Japan;1. Department of Physics, Air University, PAF Complex, E-9, Islamabad 44000, Pakistan;2. Department of Nuclear Engineering, Pakistan Institute of Engineering & Applied Sciences (PIEAS), Post Office Nilore, Islamabad 45650, Pakistan;3. Department of Physics & Applied Mathematics, Pakistan Institute of Engineering & Applied Sciences (PIEAS), Post Office Nilore, Islamabad 45650, Pakistan;1. Lithuanian Energy Institute, Nuclear Engineering Laboratory, 3 Breslaujos St., Kaunas LT-44403, Lithuania;2. Kaunas University of Technology, Department of Thermal and Nuclear Energy, 56 – 438 Studentu St., Kaunas LT-51424, Lithuania;1. Key Laboratory of Neutronics and Radiation Safety, Institute of Nuclear Energy Safety Technology, Chinese Academy of Sciences, Hefei, Anhui, 230031, China;2. University of Science and Technology of China, Hefei, Anhui, 230027, China;1. Institute of Nuclear and New Energy Technology, Collaborative Innovation Center of Advanced Nuclear Energy Technology, Key Laboratory of Advanced Reactor Engineering and Safety of Ministry of Education, Tsinghua University, Beijing 100084, China;2. Institute of Modern Physics, Chinese Academy of Sciences, Lanzhou 730000, China |
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| Abstract: | Subcritical reactors, also called Accelerator Driven Systems (ADS), are specifically studied for their capacity in transmuting Minor Actinides (MA). Nuclear fuel cycle scenarios involving MA transmutation in ADS are widely researched. The nuclear fuel cycle simulation tool code CLASS (Core Library for Advanced Scenarios Simulations) is dedicated to the inventory evolution calculation induced by a complex nuclear fleet. For managing reactors, the code CLASS includes physic models. Loading models aim to provide the fuel composition at beginning of cycle according to the stocks isotopic composition and the reactors requirements. A cross section predictor aims to provide mean cross sections needed for solving Bateman equations. Physic models are built from reactors calculation set ahead of the scenario calculation. An ADS standard composition at BOC is a mixture of plutonium and MA oxide. The high number of fissile isotopes present in the subcritical core leads to an issue for building an ADS fuel loading model. A high number of isotopic vector at BOC is needed to get an exhaustive simulation set. Also, ADS initial reactivity is adjusted with an inert matrix which induces an additional degree of freedom. The building of an ADS fuel loading model for CLASS requires two steps. For any heavy nuclide composition at beginning of cycle, the core reactivity must be imposed at a subcritical level. Also, the reactivity coefficient evolution should be maintained during the irradiation. In this work, the MgO volume fraction is adjusted to reach the first requirement. The methodology based on a set of reactor simulations and neural network utilization to predict the MgO volume fraction needed to reach a wanted keff for any initial composition is presented. Also, a complete neutronic study is done that highlight the effect on MgO on neutronic parameters. Reactor simulations are done with the transport code MCNP6 (Monte Carlo N particle transport code). The ADS geometry is based on the EFIT (European Facility for Industrial-Scale Transmutation) concept. The simulation set is composed of more than 8000 randomized runs from which a neural network has been built. The resulting MgO prediction method allows reaching a keff at 0.96 and the distribution standard deviation is around 200 pcm. |
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| Keywords: | ADS Minor actinides Plutonium Transmutation MgO inert matrix Neural network |
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