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Case Study of Data Assimilation Methods with the LWR-Proteus Phase II Experimental Campaign

Type of publication Peer-reviewed
Publikationsform Proceedings (peer-reviewed)
Author Siefman Daniel, Hursin Mathieu, Grimm Peter, Pautz Andreas,
Project Development of a Methodology for Nuclear Data Assimilation in Reactor Physics employing the PROTEUS Experimental Data Base
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Proceedings (peer-reviewed)

Title of proceedings M&C 2017 - International Conference on Mathematics & Computational Methods Applied to Nuclear Scienc
Place Jeju, Korea

Open Access

Abstract

This paper describes the application of data assimilation methods to CASMO-5 simulations of a Proteus research reactor experiment. Its focus is a comparison and evaluation of three prominent data assimilation methods: generalized linear least squares, MOCABA, and Bayesian Monte Carlo. These methods have not yet been extensively compared to date. The experiment is an interesting case study for this comparison because the measured reactivity worth response can be non-linear. This study investigates the eects that non-linearity has upon the agreement between the methods. The adjusted calculated values, calculation uncertainty, and nuclear data are all investigated to compare and evaluate the methods. The presented results provide evidence supporting the hypothesis that for linear responses, all of the data assimilation methods agree well. But when the responses become more non-linear, significant disagreements occur between generalized linear least squares results and those of MOCABA and Bayesian Monte Carlo.
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