Spallation reactions. A successful interplay between modeling and applications

The spallation reactions are a type of nuclear reaction which occur in space by interaction of the cosmic rays with interstellar bodies. The first spallation reactions induced with an accelerator took place in 1947 at the Berkeley cyclotron (University of California) with 200 MeV deuterons and 400 MeV alpha beams. They highlighted the multiple emission of neutrons and charged particles and the production of a large number of residual nuclei far different from the target nuclei. The same year R. Serber describes the reaction in two steps: a first and fast one with high-energy particle emission leading to an excited remnant nucleus, and a second one, much slower, the de-excitation of the remnant. In 2010 IAEA organized a worskhop to present the results of the most widely used spallation codes within a benchmark of spallation models. If one of the goals was to understand the deficiencies, if any, in each code, one remarkable outcome points out the overall high-quality level of some models and so the great improvements achieved since Serber. Particle transport codes can then rely on such spallation models to treat the reactions between a light particle and an atomic nucleus with energies spanning from few tens of MeV up to some GeV. An overview of the spallation reactions modeling is presented in order to point out the incomparable contribution of models based on basic physics to numerous applications where such reactions occur. Validations or benchmarks, which are necessary steps in the improvement process, are also addressed, as well as the potential future domains of development. Spallation reactions modeling is a representative case of continuous studies aiming at understanding a reaction mechanism and which end up in a powerful tool.Comment: 59 pages, 54 figures, Revie

Economic Module Development for the Fuel Cycle Simulation Tool CLASS

International audienc

MgO effect on an ADS neutronic parameters

ISBN 978-1-49-51-6286-2International audienceAccelerator Driven Systems (ADS) are specifically studied for their capacity intransmuting Minor Actinides (MA). Electronuclear scenarios involving MA transmutation in ADSare widely researched. The dynamic fuel cycle simulation code CLASS (Core Library forAdvanced Scenarios Simulations) is used for predicting the inventory evolution induced by acomplex nuclear fleet. For managing reactors, the code CLASS is based on physic models. A FuelLoading Model (FLM) provides the fuel composition at Beginning Of Cycle (BOC) according tothe storages composition and the reactor requirements. A Cross Section Predictor (CSP) estimatesmean cross sections needed for solving evolution equations. Physic models are built from reactorscalculation set ahead of the scenario calculation. An ADS standard composition at BOC is amixture of plutonium and MA oxide. The high number of fissile isotopes present in the sub-criticalcore leads to an issue for building an ADS FLM. A high number of isotopic vectors at BOC isneeded to get an exhaustive simulation set. Also, ADS initial reactivity is adjusted with an inertmatrix that induces an additional degree of freedom. The building of an ADS FLM for CLASSrequires two steps. For any heavy nuclide composition at beginning of cycle, the core reactivitymust be imposed at a sub-critical level. Also, the reactivity coefficient evolution should bemaintained during the irradiation. For building a FLM, a simulation set has been built. Reactorsimulations 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. A complete neutronicstudy is presented that highlight the effect on MgO on neutronic parameters

Prediction of MgO volume fraction in an ADS fresh fuel for the scenario code CLASS

International audienceSubcritical 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

MOX fuel enrichment prediction in PWR using polynomial models

International audienceA dynamic fuel cycle simulation code models all the ingoing and outgoing material flow in all facilities ofa 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 calculationcomes from the fuel fabrication of reprocessed fuel such as MOX fuel. Indeed, the MOX fuel isfabricated using a plutonium base completed with depleted uranium. The amount of plutonium in thefuel will directly impact the neutron multiplication factor and its evolution through irradiation, so theduration to keep the fuel in the reactor. The present paper presents the study of different PWR MOX fuelfabrication polynomial models. Those models will allow the prediction of the amount of plutoniumneeded to reach a wanted burnup from the plutonium isotopics. After defining a method to generate atraining sample, that is to say the set of fuel depletion calculations used to fit the polynomial models, thispapers will discuss their performances on 3 different applications. On the two tested models, one linearand one quadratic, while the linear model fail to properly describe the amount of plutonium needed, thefuel fabricated, using the quadratic one, reaches the wanted burnup with a discrepancy below 2%

Mean cross section prediction in PWR-MOX using neural network

ISBN 978-1-49-51-6286-2International audienceFuel depletion calculation codes require one-group mean cross sections of manynuclides to solve Bateman's equations. In such codes, the mean cross sections are assed by themean of iterative calls of Boltzmann equation solver thanks to neutron transport codes. This is atime consuming task, especially with Monte Carlo codes such as MCNP. This paper presents amethodology based on neural network for building a cross section predictor for a PWR reactorloaded with any MOX fuel. This approach allows performing fuel depletion calculation in lessthan one minute with an excellent accuracy. A maximum deviation of 3% on actinides is obtainedat the end of cycle between inventories calculated from neural networks and from the referencecoupled neutron transport / fuel depletion calculation

Mean cross section prediction in PWR-MOX using neural network

ISBN 978-1-49-51-6286-2International audienceFuel depletion calculation codes require one-group mean cross sections of manynuclides to solve Bateman's equations. In such codes, the mean cross sections are assed by themean of iterative calls of Boltzmann equation solver thanks to neutron transport codes. This is atime consuming task, especially with Monte Carlo codes such as MCNP. This paper presents amethodology based on neural network for building a cross section predictor for a PWR reactorloaded with any MOX fuel. This approach allows performing fuel depletion calculation in lessthan one minute with an excellent accuracy. A maximum deviation of 3% on actinides is obtainedat the end of cycle between inventories calculated from neural networks and from the referencecoupled neutron transport / fuel depletion calculation

A neural network approach for burn-up calculation and its application to the dynamic fuel cycle code CLASS

International audienceDynamic 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

Assessment of plutonium inventory management in the french nuclear fleet with the fuel cycle simulator CLASS

International audienceThe 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

Global and flexible models for Sodium-cooled Fast Reactors in fuel cycle simulations

International audienceSince 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