The European Journal of Physics N (EPJ-N)
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CESAR5.3: Isotopic depletion for Research and Testing Reactor decommissioning
No full textCESAR stands in French for “simplified depletion applied to reprocessing”. The current version is now number 5.3 as it started 30 years ago from a long lasting cooperation with ORANO, co-owner of the code with CEA. This computer code can characterize several types of nuclear fuel assemblies, from the most regular PWR power plants to the most unexpected gas cooled and graphite moderated old timer research facility. Each type of fuel can also include numerous ranges of compositions like UOX, MOX, LEU or HEU. Such versatility comes from a broad catalog of cross section libraries, each corresponding to a specific reactor and fuel matrix design. CESAR goes beyond fuel characterization and can also provide an evaluation of structural materials activation. The cross-sections libraries are generated using the most refined assembly or core level transport code calculation schemes (CEA APOLLO2 or ERANOS), based on the European JEFF3.1.1 nuclear data base. Each new CESAR self shielded cross section library benefits all most recent CEA recommendations as for deterministic physics options. Resulting cross sections are organized as a function of burn up and initial fuel enrichment which allows to condensate this costly process into a series of Legendre polynomials. The final outcome is a fast, accurate and compact CESAR cross section library. Each library is fully validated, against a stochastic transport code (CEA TRIPOLI 4) if needed and against a reference depletion code (CEA DARWIN). Using CESAR does not require any of the neutron physics expertise implemented into cross section libraries generation. It is based on top quality nuclear data (JEFF3.1.1 for ∼400 isotopes) and includes up to date Bateman equation solving algorithms. However, defining a CESAR computation case can be very straightforward. Most results are only 3 steps away from any beginner's ambition: Initial composition, in core depletion and pool decay scenario. On top of a simple utilization architecture, CESAR includes a portable Graphical User Interface which can be broadly deployed in R&D or industrial facilities. Aging facilities currently face decommissioning and dismantling issues. This way to the end of the nuclear fuel cycle requires a careful assessment of source terms in the fuel, core structures and all parts of a facility that must be disposed of with “industrial nuclear” constraints. In that perspective, several CESAR cross section libraries were constructed for early CEA Research and Testing Reactors (RTR’s). The aim of this paper is to describe how CESAR operates and how it can be used to help these facilities care for waste disposal, nuclear materials transport or basic safety cases. The test case will be based on the PHEBUS Facility located at CEA − Cadarache
Recent advances in beta decay measurements
No full textThree observables of interest for present and future reactors depend on the β decay data of the fission products: the reactor decay heat, antineutrinos from reactors and delayed neutron emission. Concerning the decay heat, significant discrepancies still exist between summation calculations in − their two main ingredients: the decay data and the fission yields − performed using the most recent evaluated databases available. It has been recently shown that the associated uncertainties are dominated by the ones on the decay data. But the results subtantially differ taking into account or not the correlations between the fission products. So far the uncertainty propagation does not include as well systematic effects on nuclear data such as the Pandemonium effect which impacts a large number of nuclei contributing to the decay heat. The list of nuclei deserving new TAGS measurements has been updated recently in the frame of IAEA working groups. The issues listed above impact in the same way the predicted energy spectra of the antineutrinos from reactors computed with the summation method, the interest of which has been recently reinforced by the Daya Bay latest publication. Nuclear data should definitely contribute to refine and better control these calculations. Lastly, a lot of nuclear data related to delayed neutrons are missing in nuclear databases. Despite the progresses already done these last years with new measurements now requiring to be included in evaluated databases, the experimental efforts which still need to be done are significant. These different issues will be addressed here before to comment on recent experimental results and on their impacts on the quoted observables. Some perspectives will also be presented. Solving the issues listed above will require to bring together experimental, simulation, evaluation and theoretical activities
Development and validation of uncertainty neutron transport calculations at an industrial scale
No full textEvaluating uncertainties on nuclear parameters such as reactivity is a major issue for conception of nuclear reactors. These uncertainties mainly come from the lack of knowledge on nuclear and technological data. Today, the common method used to propagate nuclear data uncertainties is Total Monte Carlo [
Uncertainty quantification works relevant to fission yields and decay data
No full textIn the present paper, firstly, we review our previous works on uncertainty quantification (UQ) of reactor physics parameters. This consists of (1) development of numerical tools based on the depletion perturbation theory (DPT), (2) linearity of reactor physics parameters to nuclear data, (3) UQ of decay heat and its reduction, and (4) correlation between decay heat and β-delayed neutrons emission. Secondly, we show results of extensive calculations about UQ on decay heat with several different numerical conditions by the DPT-based capability of a reactor physics code system CBZ
Extension of Bayesian inference for multi-experimental and coupled problem in neutronics − a revisit of the theoretical approach
No full textBayesian methods are known for treating the so-called data re-assimilation. The Bayesian inference applied to core physics allows to get a new adjustment of nuclear data using the results of integral experiments. This theory leading to reassimliation encompasses a broader approach. In previous papers, new methods have been developed to calculate the impact of nuclear and manufacturing data uncertainties on neutronics parameters. Usually, adjustment is performed step by step with one parameter and one experiment by batch. In this document, we rewrite Orlov theory to extend to multiple experimental values and parameters adjustment. We found that the multidimensional system expression looks like can be written as the monodimensional system in a matrix form. In this extension, correlation terms appears between experimental processes (manufacturing and measurements) and we discuss how to fix them. Then formula are applied to the extension to the Boltzmann/Bateman coupled problem, where each term could be evaluated by computing depletion uncertainties, studied in previous papers
A study of the construction of the correlation matrix of
No full textTwo blind analyses for 241Pu(nth,f) isobaric fission yields have been conducted, one analysis using a mix of a Monte-Carlo and an analytical method, the other one relying only on analytical calculations. The calculations have been derived from the same analysis path and experimental data, obtained on the LOHENGRIN mass spectrometer at the Institut Laue-Langevin. The comparison between the two analyses put into lights several biases and limits of each analysis and gives a comprehensive vision on the construction of the correlation matrix. It gives confidence in the analysis scheme used for the determination of the fission yields and their correlation matrix
Estimating model bias over the complete nuclide chart with sparse Gaussian processes at the example of INCL/ABLA and double-differential neutron spectra
No full textPredictions of nuclear models guide the design of nuclear facilities to ensure their safe and efficient operation. Because nuclear models often do not perfectly reproduce available experimental data, decisions based on their predictions may not be optimal. Awareness about systematic deviations between models and experimental data helps to alleviate this problem. This paper shows how a sparse approximation to Gaussian processes can be used to estimate the model bias over the complete nuclide chart at the example of inclusive double-differential neutron spectra for incident protons above 100 MeV. A powerful feature of the presented approach is the ability to predict the model bias for energies, angles, and isotopes where data are missing. The number of experimental data points that can be taken into account is at least in the order of magnitude of 104 thanks to the sparse approximation. The approach is applied to the Liège intranuclear cascade model coupled to the evaporation code ABLA. The results suggest that sparse Gaussian process regression is a viable candidate to perform global and quantitative assessments of models. Limitations of a philosophical nature of this (and any other) approach are also discussed
Single Event Effect cross section calibration and application to quasi-monoenergetic and spallation facilities
No full textWe describe an approach to calibrate Single Event Effect (SEE)-based detectors in monoenergetic fields and apply the resulting semi-empiric responses to more general mixed-field cases in which a broad variety of particle species and energy spectra are present. The calibration of the response functions is based both on experimental proton (30–200 MeV) and neutron (5–300 MeV) data and considerations derived from Monte Carlo simulations using the FLUKA Monte Carlo code. The application environments include the quasi-monoenergetic neutrons at RCNP, the atmospheric-like VESUVIO spallation spectrum and the CHARM high-energy accelerator test facility. The agreement between the mixed-field response and that predicted through the mono-energetic calibration is within ±30% for the broad variety of cases considered and thus regarded as highly successful for mixed-field monitoring applications
ARIADNE – a program estimating covariances in detail for neutron experiments
No full textThe python program ARIADNE is a tool developed for evaluators to estimate detailed uncertainties and covariances for experimental data in a consistent and efficient manner. Currently, it is designed to aid in the uncertainty quantification of prompt fission neutron spectra, and was employed to estimate experimental covariances for CIELO and ENDF/B-VIII.0 evaluations. It provides a streamlined way to estimate detailed covariances by (1) implementing uncertainty quantification algorithms specific to the observables, (2) defining input quantities for typically encountered uncertainty sources and correlation shapes, and (3) automatically generating plots of data, uncertainties and correlations, GND formatted XML and plain text output files. Covariances of the same and between different datasets can be estimated, and tools are provided to assemble a database of experimental data and covariances for an evaluation based on ARIADNE outputs. The underlying IPython notebook files can be easily stored, including all assumptions on uncertainties, leading to more reproducible inputs for nuclear data evaluations. Here, the key inputs and outputs are shown along with a representative example for the current version of ARIADNE to illustrate its usability and to open a discussion on how it could address further needs of the nuclear data evaluation community
Potential sources of uncertainties in nuclear reaction modeling
No full textNowadays, reliance on nuclear models to interpolate or extrapolate between experimental data points is very common, for nuclear data evaluation. It is also well known that the knowledge of nuclear reaction mechanisms is at best approximate, and that their modeling relies on many parameters which do not have a precise physical meaning outside of their specific implementations in nuclear model codes: they carry both specific physical information, and effective information that is related to the deficiencies of the model itself. Therefore, to improve the uncertainties associated with evaluated nuclear data, the models themselves must be refined so that their parameters can be rigorously derived from theory. Examples of such a process will be given for a wide sample of models like: detailed theory of compound nucleus decay through multiple nucleon or gamma emission, or refinements to the width fluctuation factor of the Hauser-Feshbach model. All these examples will illustrate the reduction in the effective components of nuclear model parameters, through the reduced dynamics of parameter adjustment needed to account for experimental data. The significant progress, recently achieved for the non-fission channels, also highlights the difficult path ahead to improve our quantitative understanding of fission in a similar way: by relying on microscopic theory