Abstract

This work presents the description of the first part of a methodology applied to perform In-Core Fuel Management (ICFM) in Pressurized Water Reactor (PWR). The ICFM of a PWR reactor consists on defining the best charging or recharging pattern of fuel assemblies inside a reactor for an operational cycle. This means, finding a suitable arrangement of fuel assemblies that optimizes the performance of the reactor, which complies with all safety criteria. Genetic algorithms (GAs) are used to select the arrangements that interact with the reactor physics simulation code, holding the neutron characteristics of each fuel assembly. Therefore, a reliable and fast code was developed accordingly. The consolidated technique of coarse mesh node code that numerically solves the multigroup diffusion equation for two groups of energy, fast and thermal neutrons, in two dimensions was selected. In this type of code, it is essential that each fuel assembly is homogenized and characterized by its macroscopic cross sections, for each reactor’s burnup condition. The cross sections are generated with the support of SCALE 6.0, computational platform developed by the Reactor and Nuclear Systems Division (RNSD), from the Oak Ridge National Laboratory (ORNL). The completeness of the qualification and validation of the results obtained from the homogenization of the fuel assembly by the SCALE was performed comparing the results with actual data of a benchmark reactor. The fully documented Almaraz Nuclear Power Plant provided by the International Atomic Energy Agency (IAEA)-TECDOC-815, has been used as benchmark with successful results.

Highlights

  • Nuclear fuel management relates to the decisions pursuing the optimal strategy of fuel assembly (FA), replacement and reposition after each cycle of operation

  • The cross sections of the FA’s Almaraz II were generated using the SCALE 6.0 developed by the Reactor and Nuclear Systems Division (RNSD) from the Oak Ridge National Laboratory (ORNL) [5]

  • The International Atomic Energy Agency (IAEA) benchmark column shows the average calculations from IAEA benchmark with their respective standard deviation (Std.)

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Summary

Introduction

Nuclear fuel management relates to the decisions pursuing the optimal strategy of fuel assembly (FA), replacement and reposition after each cycle of operation It is comprising three decisions: the choice of FA that are exhausted and will be withdrawn after an operational cycle; the position or reposition of the partially burnt fuel assemblies and the type and position of new FA to be inserted in the reactor. This operation is aimed to restore reactivity for a new operational cycle, optimizing the performance of the reactor and complying with all safety criteria. With the introduction of artificial intelligence techniques, such as Genetic Algorithms (GAs), these techniques began to be applied in nuclear fuel management and they are currently becoming one of the main tools for ICFM [2]

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