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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 k eff 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 k eff at 0.96 and the distribution standard deviation is around 200 pcm. [ABSTRACT FROM AUTHOR]

Highlights • New flexible model for a wide range of Sodium cooled Fast Reactors inside CLASS. • Simplifications options lead to bias lower than 1% on actinides composition. • Precision of the meta-model better than 4% on major actinides in spent fuel. • Breeding ratios of designs covered go from 0.88 (burner) to 1.41 (breeder). • Biggest impact on breeding from blankets, major impact from isotopic composition. Abstract Since Sodium cooled Fast Reactors are present in many scenarios and strategies for the future of nuclear energy while not having a specific design established yet, we created a new fast and flexible model for the dynamic fuel cycle simulation tools CLASS. It includes a depletion meta-model and a fuel loading method based on artificial neural networks. It is able to represent a wide range of Sodium cooled Fast Reactor designs using oxide fuels and a wide range of fuel management strategies within fuel cycle simulation tools. A comprehensive analysis of simplification options has been made in order to choose the right level of complexity for the reference full core depletion calculations performed with the MURE code used for the training of the meta-model. The process from these reference calculations to the final meta-model is explained and a specific focus is given to the operations going from detailed full core depletion results to global results suitable for neural networks training. Details on the creation process for neural networks based predictors, one for each average cross-section, and their training on full core depletion calculations are given as well as the implementation within the CLASS code. The irradiation meta-model achieves good precision on all major and minor actinides present in spent fuel. The designs and loaded fuel covered by the model allow significant burner to strong breeder strategies. A sensitivity analysis shows that the number of fertile blankets is the primary contributor for breeding capabilities, but effects of isotopic composition are also significant. A test scenario illustrates the model capacity to simulate burner and breeder designs. [ABSTRACT FROM AUTHOR]

A dynamic fuel cycle simulation code models all the ingoing and outgoing material flow in all facilities of a nuclear reactor’s fleet as well as their evolutions through the different nuclear processes (irradiation, decay, chemical separation, etc.). One of the main difficulties encountered when performing such calculation comes from the fuel fabrication of reprocessed fuel such as MOX fuel. Indeed, the MOX fuel is fabricated using a plutonium base completed with depleted uranium. The amount of plutonium in the fuel will directly impact the neutron multiplication factor and its evolution through irradiation, so the duration to keep the fuel in the reactor. The present paper presents the study of different PWR MOX fuel fabrication polynomial models. Those models will allow the prediction of the amount of plutonium needed to reach a wanted burnup from the plutonium isotopics. After defining a method to generate a training sample, that is to say the set of fuel depletion calculations used to fit the polynomial models, this papers will discuss their performances on 3 different applications. On the two tested models, one linear and one quadratic, while the linear model fail to properly describe the amount of plutonium needed, the fuel fabricated, using the quadratic one, reaches the wanted burnup with a discrepancy below 2%. [ABSTRACT FROM AUTHOR]