Abstract
Evaluated nuclear data are essential for nuclear reactor studies. In order to significantly improve the precision of nuclear data, more and more fundamental fission models are used in the evaluation processing. Therefore, tests of fission models become a central issue. In this framework, FIFRELIN (FIssion Fragments Evaporation Leading to an Investigation of Nuclear data) is a Monte Carlo code developed in order to modelize fission fragments de-excitation through the emission of neutrons, γ and conversion e-. To be performed, a FIFRELIN calculation relies on several models such as gamma strength function and nuclear level density and of more empirical hypothesis such as total excitation energy repartition or angular momentum given by the fission reaction. Moreover, pre-emission mass yield and kinetic energy distribution per mass are necessary to process the simulation. A set of five free parameters are chosen to reproduce a target observable. Often this observable corresponds to the mean neutron multiplicity for heavy and light fragment. In this work, the impact of the set of parameters on different output observables (neutron emission probability, neutron multiplicity as function of the fission fragment mass) is investigated.
Highlights
Nuclear reactor simulations rely more and more on complex multi-physics modelling
The second one comes from the theoretical side. New models such as FIFRELIN aims to improve the predictibility of models by looking simultaneously at multiple observables
After being created at the scission point and accelerated, fission fragments are characterized by their kinetic energy KE, excitation energy E∗, total angular momentum and parity Jπ, mass A and nuclear charge Z
Summary
Nuclear reactor simulations rely more and more on complex multi-physics modelling. In order to reach target precision, it is necessary to improve the accuracy of fission data in the actinide region. The first one comes from the experimental side. New instruments and analysis are developed with new measured observables. The second one comes from the theoretical side. New models such as FIFRELIN aims to improve the predictibility of models by looking simultaneously at multiple observables
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