Development of a Reduced Order Model for Fuel Burnup Analysis

Christian Castagna, Manuele Aufiero, Stefano Lorenzi, Guglielmo Lomonaco and Antonio Cammi
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Christian Castagna: Politecnico di Milano, Department of Energy, CeSNEF (Enrico Fermi Center for Nuclear Studies), via La Masa 34, 20156 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
Antonio Cammi: Politecnico di Milano, Department of Energy, CeSNEF (Enrico Fermi Center for Nuclear Studies), via La Masa 34, 20156 Milano, Italy

Energies, 2020, vol. 13, issue 4, 1-26

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.

Keywords: burnup; ROM; POD; neutronics; Monte Carlo; multi-physics (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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