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Development of a Reduced Order Model for Fuel Burnup Analysis

Author

Listed:
  • Christian Castagna

    (Politecnico di Milano, Department of Energy, CeSNEF (Enrico Fermi Center for Nuclear Studies), via La Masa 34, 20156 Milano, Italy
    Istituto Nazionale di Fisica Nucleare, Sezione di Milano-Bicocca, piazza della Scienza 3, 20126 Milano, Italy)

  • Manuele Aufiero

    (Milano Multiphysics, PoliHub Startup Incubator, via Durando 39, 20158 Milano, Italy)

  • Stefano Lorenzi

    (Politecnico di Milano, Department of Energy, CeSNEF (Enrico Fermi Center for Nuclear Studies), via La Masa 34, 20156 Milano, Italy)

  • Guglielmo Lomonaco

    (GeNERG-DIME/TEC, University of Genova, via all’opera Pia 15/A, 16145 Genova, Italy
    Istituto Nazionale di Fisica Nucleare, Sezione di Genova, via Dodecaneso 33, 16146 Genova, Italy)

  • Antonio Cammi

    (Politecnico di Milano, Department of Energy, CeSNEF (Enrico Fermi Center for Nuclear Studies), via La Masa 34, 20156 Milano, Italy
    Istituto Nazionale di Fisica Nucleare, Sezione di Milano-Bicocca, piazza della Scienza 3, 20126 Milano, Italy)

Abstract

Fuel burnup analysis requires a high computational cost for full core calculations, due to the amount of the information processed for the total reaction rates in many burnup regions. Indeed, they reach the order of millions or more by a subdivision into radial and axial regions in a pin-by-pin description. In addition, if multi-physics approaches are adopted to consider the effects of temperature and density fields on fuel consumption, the computational load grows further. In this way, the need to find a compromise between computational cost and solution accuracy is a crucial issue in burnup analysis. To overcome this problem, the present work aims to develop a methodological approach to implement a Reduced Order Model (ROM), based on Proper Orthogonal Decomposition (POD), in fuel burnup analysis. We verify the approach on 4 years of burnup of the TMI-1 unit cell benchmark, by reconstructing fuel materials and burnup matrices over time with different levels of approximation. The results show that the modeling approach is able to reproduce reactivity and nuclide densities over time, where the accuracy increases with the number of basis functions employed.

Suggested Citation

  • Christian Castagna & Manuele Aufiero & Stefano Lorenzi & Guglielmo Lomonaco & Antonio Cammi, 2020. "Development of a Reduced Order Model for Fuel Burnup Analysis," Energies, MDPI, vol. 13(4), pages 1-26, February.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:4:p:890-:d:321682
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    Citations

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    Cited by:

    1. Claire E. Heaney & Andrew G. Buchan & Christopher C. Pain & Simon Jewer, 2021. "Reduced-Order Modelling Applied to the Multigroup Neutron Diffusion Equation Using a Nonlinear Interpolation Method for Control-Rod Movement," Energies, MDPI, vol. 14(5), pages 1-27, March.
    2. Jerzy Cetnar & Przemysław Stanisz & Mikołaj Oettingen, 2021. "Linear Chain Method for Numerical Modelling of Burnup Systems," Energies, MDPI, vol. 14(6), pages 1-19, March.
    3. Dan Gabriel Cacuci, 2022. "Sensitivity Analysis, Uncertainty Quantification and Predictive Modeling of Nuclear Energy Systems," Energies, MDPI, vol. 15(17), pages 1-7, September.
    4. Toby R. F. Phillips & Claire E. Heaney & Brendan S. Tollit & Paul N. Smith & Christopher C. Pain, 2021. "Reduced-Order Modelling with Domain Decomposition Applied to Multi-Group Neutron Transport," Energies, MDPI, vol. 14(5), pages 1-25, March.

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