Evaluating the neutronic state (neutron flux, power…) of the whole nuclear core is a very important topic that has strong implication for nuclear core management and for security monitoring. The core state is evaluated using measurements and calculations. Usually, parts of the measurements are used, and only one kind of instrument is taken into account. However, the core state evaluation should be more accurate when more measurements are collected in the core. But using information from heterogeneous sources is at glance a difficult task. This difficulty can be overcome by Data Assimilation techniques. Such a method allows to combine in a coherent framework the information coming from numerical model and the one coming from various types of observations. Beyond the inner advantage to use heterogeneous instruments, this leads to obtaining a significant increase of the quality of neutronic global state reconstruction with respect to individual use of measures. In order to describe this approach, we introduce here the basic principles of data assimilation (focusing on BLUE, Best Unbiased Linear Estimation). Then we present the configuration of the method within the nuclear core problematic. Finally, we present the results obtained on nuclear measurements coming from various instruments.

Data assimilation method consists in combining all available pieces of information about a system to obtain optimal estimates of initial states. The different sources of information are weighted according to their accuracy by the means of error covariance matrices. Our purpose here is to evaluate the efficiency of variational data assimilation for the xenon induced oscillations forecasts in nuclear cores. In this paper we focus on the comparison between 3DVAR schemes with optimised background error covariance matrix B and a 4DVAR scheme. Tests were made in twin experiments using a simulation code which implements a mono-dimensional coupled model of xenon dynamics, thermal, and thermal–hydraulic processes. We enlighten the very good efficiency of the 4DVAR scheme as well as good results with the 3DVAR one using a careful multivariate modelling of B.

Data assimilation method consists in combining all available pieces of information about a system to obtain optimal estimates of initial states. The different sources of information are weighted according to their accuracy by the means of error covariance matrices. Our purpose here is to evaluate the efficiency of variational data assimilation for the xenon induced oscillations forecasts in nuclear cores. In this paper we focus on the comparison between 3DVAR schemes with optimised background error covariance matrix B and a 4DVAR scheme. Tests were made in twin experiments using a simulation code which implements a mono-dimensional coupled model of xenon dynamics, thermal, and thermal-hydraulic processes. We enlighten the very good efficiency of the 4DVAR scheme as well as good results with the 3DVAR one using a careful multivariate modelling of B. (C) 2013 Elsevier Ltd. All rights reserved.

The purpose of this paper is the use data assimilation techniques, inspired from meteorological applications, to perform an optimal reconstruction of the neutronic field in a nuclear core. Both measurements, and information coming from a numerical model, are used in this purpose. We first study the robustness of the method when the amount of measured information varies. We then study the influence of the nature of the instruments and their spatial repartition on the efficiency of the field reconstruction. Such study allows, also enlightening the instruments providing the most information within a data assimilation procedure. The study of various network configurations of instruments in the nuclear core establishes that influence of the instruments depends both on the individual instrumentation location as well as on the chosen network.

We apply a data assimilation technique, inspired from meteorological applications, to perform an optimal reconstruction of the neutronic activity field in a nuclear core. Both measurements and information coming from a numerical model are used. We first study the robustness of the method when the amount of measured information decreases. We then study the influence of the nature of the instruments and their spatial repartition on the efficiency of the field reconstruction.

The global neutronic activity fields of a nuclear core can be reconstructed using data assimilation. Indeed, data assimilation allows to combine both measurements from instruments and information from a model, to evaluate the best possible neutronic activity within the core. We present and apply a specific procedure which evaluates the influence of measures by adding or removing instruments in a given measurement network (possibly empty). The study of various network configurations for the instruments in the nuclear core establishes that the influence of the instruments depends both on the independent instrumentation location and on the chosen network.

In this paper, data assimilation (DA) techniques that have proven to be efficient in the fields of meteorological forecast and oceanography are applied to neutronics problems. Two applications, the techniques of which can be used to enhance optimization, are presented: three-dimensional (3-D) neutronic field interpolation in online core monitoring, and parameter estimation in code qualification procedures. First, the main bases of DA theory are shortly presented. Calibration and estimation procedures that are in use today at Electriciti de France (EDF) are then briefly introduced. We also analyze the main limitations of these procedures and the potential improvements that the use of various DA techniques can provide. We present the MANARA mock-up, which computes a 3-D field interpolation and has been implemented in PALM, a DA-dedicated coupling platform developed at the Centre Europeen de Recerche et de Formation Avancee en Calcul Scientifique (CERFACS). Result validation and comparison with former interpolation procedure CAMARET are also presented. Next, the principles of the KAFEINE mock-up, based on an extended Kalman filter approach, are displayed. This application covers the field of optimal parameter calibration using the in-core measures. In conclusion, these first two applications to neutronics, carried out in partnership between CERFACS and EDF Research and Development, seem very promising, especially considering the new generation of neutronic solvers that are being developed in the frame of the DESCARTES project.