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International audience; Dynamic fuel cycle simulation codes model evolving nuclear fuel cycles, and calculate nuclides inventories and material flows in each unit of the cycle. In the nuclear fuel cycle simulation code CLASS (Core Library for Advanced Scenario Simulation), a Fuel Loading Model (FLM) builds a fresh fuel fulfilling the reactor criticality requirement, depending on the available fissile material. Then, a mean cross-sections predictor calculates the mean cross-sections required to perform the fuel depletion in a short calculation time. This work focuses on the elaboration of these models in the case of a PWR-MOXEUS fuel (MOX on Enriched Uranium Support), which allows plutonium mono-recycling and multi-recycling in PWR. These models are built using neural networks. These predictors are trained on a databank composed of 1000 PWR infinite assembly depletion calculations performed using the software MURE (MCNP Utility for Reactor Evolution) based on the transport code MCNP (Monte-Carlo N Particle). Several databanks are tested and the performance of the resulting predictors are compared. The FLM predicts the plutonium content, and potentially the uranium enrichment, required in the fresh fuel. This model is based on a calculation of the infinite multiplication factor performed with an accuracy close to MCNP statistical error. Mean cross-sections prediction allows a deviation lower than 5% on main plutonium isotopes at 75 GWd/t compared to the fuel depletion reference calculation. PWR MOXEUS models are also tested on a balancing scenario. The complex evolution of MOXEUS fresh fuel isotopic composition during the scenario is highlighted. Furthermore, equilibrium fresh fuel isotopic vectors are compared to another study on an equilibrium MOXEUS multi-recycling strategy calculation, showing a good general agreement.

International audience; The ASTRID French project aimed at designing, building and operating a sodium fast reactor cooled with liquid sodium. One of the goals of the project was to demonstrate the feasibility of the plutonium multi-recycling in a fast spectrum. Commissariat à l’énergie atomique et aux énergies alternatives (CEA - Atomic & Alternative Energies Commission) has recently announced the abandon of the project which involves that no sodium fast reactor project is planned in France. As a consequence, there is a real interest in assessing technical feasibility for alternative plutonium management. In this work, the plutonium multi-recycling in PWR is assessed from fuel cycle simulations performed with the library CLASS developed by CNRS/IN2P3. The technical conditions for plutonium incineration and stabilization are investigated. It is shown that plutonium can be stabilized with 30% of PWR using multi-reprocessed plutonium in MOX fuel, the rest being composed by PWR loaded with UOX. The transuranic (plutonium and minor actinides) stabilization involves a plutonium incineration. For this reason, around 50% of PWR using multi-reprocessed plutonium is required and the nuclear power has to decrease. In this paper, the methodology and the output analysis are described in detail.

International audience; Fuel cycle simulators are used worldwide to provide scientific assessment to fuel cycle future strategies. Those tools help understanding the fuel cycle physics and determining the most impacting drivers at the cycle scale. A standard scenario calculation is usually based on a set of operational assumptions, such as reactor Burn-Up, deployment history, cooling time, etc. Scenario output is then the evolution of isotopes mass in the facilities that composes the nuclear fleet. The increase of computing capacities and the use of neutron data fast predictors provide new opportunities in nuclear scenario studies. Indeed, a very high number of calculations is possible, which allows testing a high number of operational assumptions combinations. The global sensitivity analysis (GSA) formalism is specifically well adapted for this kind of problem. In this new framework, a scenario study is based on the sampling of operational data, which become input variables. A first result of a scenario study is the highlight of relations between operational input data and outputs. Input variable subspace that satisfy optimization criteria on an output, such as plutonium incineration or stabilization, can also be determined. In this paper, a focus is made on the methodology based on GSA. This innovative methodology is presented and applied to a simple fleet simulation composed of a PWR-UOx fuel and a PWR-MOx fuel. Calculations are done with the fuel cycle simulator CLASS developed by the CNRS/IN2P3 in collaboration with IRSN. The design of experiment is built from five fuel cycle input sampled variables. Sensitivity indices have been calculated on plutonium and minor actinide (MA) production. It shows that the PWR-UOx Burn-Up and the fraction of PWR-MOx fuel are the most important input variables that explain the plutonium production. For the MA production, main drivers depend strongly on isotopes. Sensitivity analysis also reveals input variable subspace responsible of simulation crash, what led to an important improvement of the model algorithms. An equilibrium condition on the plutonium mass in the stockpile used for building MOx fuel has been applied. The solution is represented as a subspace in the PWR-UOx Burn-Up and PWR-MOx fraction input space. For instance, achieving a plutonium equilibrium in a stockpile fed by a PWR-UOx that operates at 40 GWd/t requires a PWR-MOx fraction between 9 and 14%. This study also provides data related to plutonium incineration induced by the utilization of the MOx.

International audience; In contrast to the straight final disposal solution, countries like France have opted to reprocess their nuclear reactors spent fuel and defined another way to take care of sensitive elements such as the plutonium or minor actinides. Even in countries which have chosen to reprocess their spent fuel, americium is still considered as a final disposal waste. Among the minor actinides, americium will remain the main contributor to the toxicity and the decay heat of the spent fuel for thousand of years. Therefore it is important to reduce its quantity. At this time, only fast neutron future reactors are accepted to be efficient enough to transmute the americium from the thermal reactors spent fuel. As we can presume these future reactors will not be available before many decades, a new strategy which consists in recycling americium together with plutonium in pressurize water reactors mixed oxide fuel is proposed. In this paper the benefit and after-effect of this waiting strategy is analyzed. It demonstrates that the americium is indeed transmuted in a PWR quite efficiently (transmutation rate of around 43%) however the spent fuel is, as expected, more concentrated in curium of heavier nuclei. The impact on the fuel cycle (transportation, cooling time) is investigated showing that the key point would be the fabrication of the MOx-Am fuel.

International audience; Dynamic fuel cycle simulation tools calculate nuclei inventories and mass flows evolution in an entire fuel cycle, from the mine to the final disposal. Usually, the fuel depletion in reactor is handled by a fuel loading model and a mean cross section predictor. In the case of a PWR–MOX, a fuel loading model provides from a plutonium stock the plutonium fraction in the fresh fuel needed to reach a specific burnup. A mean cross section predictor aims to assess isotopic cross sections required for building Bateman equations for any fresh fuel composition with a sufficient accuracy and a reasonable computing time. This paper presents a methodology based on neural networks for building a fuel loading model and a cross section predictor for a PWR reactor loaded with MOX fuel. The mean error of the plutonium content prediction from the fuel loading model is 0.37%. Furthermore, the mean cross section predictor allows completion of the fuel depletion calculation in less than one minute with excellent accuracy. A maximum deviation of 3% on main nuclei is obtained at the end of cycle between inventories calculated from neural networks and from the reference coupled neutron transport/fuel depletion calculation.

International audience; 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.

International audience; Radioprotection measures needed in the nuclear fuel cycle require accurate knowledge of the radioactive sources involved. For innovative nuclear reactors such as Generation IV designs radiation sources (alpha, beta, gamma and neutron) in the spent fuel need to be calculated in order to understand the radioprotection needed in all aspects of the fuel cycle (transport, reprocessing, fuel fabrication, waste storage, etc.). For this purpose we have developed CHARS a set of source characterization tools coupled to our code MURE (MCNP Utilities for Reactors Evolution) which is a precision research code for fuel evolution. MURE determines inventories of around 800 nuclei during irradiation and cooling via a series of MCNP5 calculations and numerical integration of Bateman's equations. With the CHARS package using the ENSDF libraries, it's possible to generate alpha, beta and gamma spectra of the nuclear fuel at the end of cycle from any given reactor design. These complex source definitions are then used to generate automatic MCNP5 inputs for radioprotection calculations, allowing us to undertake a wide range of radioprotection studies. The first use of these tools was to estimate additional shielding in the French fuel cycle in case of switching from the current uranium (U/Pu) cycle to the thorium (Th/U) cycle. Irradiated thorium-based fuels produce small quantities of 232U, which has a relatively short half-life (69 years) and emits a hard gamma of 2.6 MeV at the end of its decay chain. U(n,2n) and 232Th(n,2n) followed by 231Pa(n,γ). From the results of radioprotection calculations we estimate additional thickness of shielding required for the back end ofthe fuel cycle and show that the greatest constraints occur for the fuel manufacturing. On the other hand the neutron yield for some thorium-based fuels will be lower in Th/U than in U/Pu cycle and as a result necessary neutronic shielding will be reduce