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Optimization of Nuclear Reactors Loading Patterns with Computational Intelligence Methods

  • Anderson Alvarenga de Moura MenesesEmail author
  • Lenilson Moreira Araujo
  • Fernando Nogueira Nast
  • Patrick Vasconcelos da Silva
  • Roberto Schirru
Chapter

Abstract

The goal of the Loading Pattern (LP) optimization problem is to determine an optimal (or near-optimal) distribution of Fuel Assemblies of a Nuclear Reactor for producing full power within adequate safety margins. Also known as In-Core Fuel Management Optimization, the LP optimization is a prominent real-world problem in Nuclear Engineering with high complexity due to its combinatorial formulation with a large number of feasible solutions, a large number of sub-optimal solutions, disconnected feasible regions, high dimensionality, complex and time-consuming evaluation functions with Reactor Physics calculations. In the present chapter, we discuss LP optimization problem and four computational intelligence optimization methods, also known as optimization metaheuristics or generic heuristic methods, namely the Cross-Entropy algorithm, the Particle Swarm Optimization, Artificial Bee Colonies, and Population-Based Incremental Learning. Results using actual models are described and also discussed.

Notes

Acknowledgements

A.A.M.M. acknowledges CNPq (Brazilian National Research Council—Project no. 472912/2013-5) and Federal University of Western Pará for financial support. P.V.S. acknowledges Federal University of Western Pará for supporting the research. F.N.N. acknowledges CNPq for supporting the research. R.S. acknowledges CNPq and FAPERJ for supporting the research. The authors acknowledge CNEN for the agreement of mutual cooperation with the Federal University of Western Pará (part of CAMP agreement). Portions of the present chapter were published in the journals Annals of Nuclear Energy and Progress in Nuclear Energy, as well as in the conference National Meeting in Computational Modeling in Brazil. The authors would like to thank the reviewers for their valuable comments and suggestions.

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Anderson Alvarenga de Moura Meneses
    • 1
    Email author
  • Lenilson Moreira Araujo
    • 1
  • Fernando Nogueira Nast
    • 1
  • Patrick Vasconcelos da Silva
    • 1
  • Roberto Schirru
    • 2
  1. 1.Institute of Engineering and GeosciencesFederal University of Western ParáSantarémBrazil
  2. 2.Program of Nuclear EngineeringFederal University of Rio de JaneiroRio de JaneiroBrazil

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