Subcriticality determination by neural-based inversion of space-energy neutron kinetic equations

P. Picca, R. Furfaro, B. D. Ganapol, S. Dulla, P. Ravetto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

The determination of the reactivity level in ADS represents nowadays an important open issue for the development of subcritical systems. Several approaches were developed in the past based on simplified models, such as point kinetics, for which an analytical inversion is feasible. On the other hand, numerical simulations shows that the evolution of the power and detector signals are not point-like. In the present work, we propose a neural-based inversion that can invert more realistic physical models. As a result, the spatial and spectral effects connected to the position and the energy selectivity of detectors are directly accounted for. An outline of the theory is presented in the following and some preliminary results show the performance of the technique proposed in the interpretation of pulsed experiments.

Original languageEnglish (US)
Title of host publicationAmerican Nuclear Society - International Conference on Mathematics, Computational Methods and Reactor Physics 2009, M and C 2009
Pages3049-3058
Number of pages10
StatePublished - 2009
EventInternational Conference on Mathematics, Computational Methods and Reactor Physics 2009, M and C 2009 - Saratoga Springs, NY, United States
Duration: May 3 2009May 7 2009

Publication series

NameAmerican Nuclear Society - International Conference on Mathematics, Computational Methods and Reactor Physics 2009, M and C 2009
Volume5

Other

OtherInternational Conference on Mathematics, Computational Methods and Reactor Physics 2009, M and C 2009
Country/TerritoryUnited States
CitySaratoga Springs, NY
Period5/3/095/7/09

Keywords

  • Accelerator-driven systems
  • Inverse techniques
  • Neural networks
  • Reactivity measurement

ASJC Scopus subject areas

  • Nuclear Energy and Engineering
  • Computational Mathematics
  • Nuclear and High Energy Physics

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