Abstract

In the [eV;MeV] energy range, modelling of the neutron induced reactions are based on nuclear reaction models having parameters. Estimation of co-variances on cross sections or on nuclear reaction model parameters is a recurrent puzzle in nuclear data evaluation. Major breakthroughs were asked by nuclear reactor physicists to assess proper uncertainties to be used in applications. In this paper, mathematical methods developped in the CONRAD code[2] will be presented to explain the treatment of all type of uncertainties, including experimental ones (statistical and systematic) and propagate them to nuclear reaction model parameters or cross sections. Marginalization procedure will thus be exposed using analytical or Monte-Carlo solutions. Furthermore, one major drawback found by reactor physicist is the fact that integral or analytical experiments (reactor mock-up or simple integral experiment, e.g. ICSBEP, …) were not taken into account sufficiently soon in the evaluation process to remove discrepancies. In this paper, we will describe a mathematical framework to take into account properly this kind of information.

Highlights

  • As one major drawback found by reactor physicist is the fact that most of the time the integral experiments were not taken into account sufficiently soon in the evaluation process to remove discrepancies, we will present the methodology used in the CONRAD code [2] for a direct use of these integral experiment in the nuclear data models

  • In the CONRAD code, nuclear reaction model parameters are the vector of uncertainties

  • This paper describes a mathematical framework to add integral experiments information during the evaluation process with CONRAD

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Summary

Introduction

Evaluating uncertainties and correlations in the nuclear data field is of great interest for reactors physicists as it is a major contribution in their own uncertainty propagation evaluation [1]. Mathematical methods will be presented to explain how to treat all type of uncertainties (including experimental ones, statistical and systematic ones) and propagate them to nuclear reaction model parameters or cross sections. As one major drawback found by reactor physicist is the fact that most of the time the integral (or analytical) experiments were not taken into account sufficiently soon in the evaluation process to remove discrepancies, we will present the methodology used in the CONRAD code [2] for a direct use of these integral experiment in the nuclear data models

Marginalization
Marginalization techniques
Integral experiments
Traditional Multigroup cross section adjustment
Mathematicals solutions with Integral measurements
Best practice to take into account integral experiments
The reference method
Example
Findings
Conclusions
Full Text
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