Abstract

Most fuel cycle simulation tools are based either on fixed recipes or assembly calculations for reactor modeling. Due to the high number of calculations and extensive computational power requirements, full-core computations are often seen as not viable for this purpose. However, this leads to additional hypotheses and modeling biases, thus limiting the realism of the resulting fuel cycle. For several applications, the current modeling method is sufficient, but precise calculations of discharged fuel composition may require further refinements. CLASS (Core Library for Advanced Simulation Scenarios) is a dynamic fuel cycle simulation code developed since 2012 with reactor models based on neural networks to produce nuclear data and physical quantities. Past work has shown a first coupling between CLASS and DONJON5 to quantify neural networks approach biases. This work assesses the applicability of 3D full-core diffusion calculations using the DONJON5 code coupled with nuclear scenario simulations involving a realistic PWR core at equilibrium cycle conditions. DONJON5 interpolates burnup dependent diffusion coefficients and cross sections generated beforehand by DRAGON5, a deterministic lattice calculation tool. Whereas previous studies considered only homogeneous reactors (i.e. homogeneous assembly in terms of composition and enrichment as well as homogeneous core), the present contribution focuses on the integration of full-core calculations in CLASS for fuel cycles involving a MOX/UO2 PWR core (i.e. 1/3 MOx–2/3 UOx). The DONJON5 model considered in this work describes a core with critical boron concentration at each time step partially loaded with MOx heterogeneous assemblies composed of three enrichments. In fuel cycle calculations, the main issue is to adapt, in the fabrication stage, the fresh fuel composition for the reactor with regards to the isotopic composition of the available stocks. This work presents a fuel loading model based on power peaking factors minimization that respects irradiation cycle length, 235U enrichment as well as Pu concentration and fissile quality, hence, ensuring a more uniform power distribution in the core.

Highlights

  • In fuel cycle simulators, reactor models are usually based on infinite assembly calculations both for the depletion simulation and for the fuel loading model that calculate each fresh fuel composition in fuel cycle simulations [1]

  • In order to increase the reliability of fuel cycle simulations, reference [2] proposed a first coupling between DONJON5 [3] and CLASS [4] allowing full-core calculations for PWRs (Pressurized Water Reactors) in a fuel cycle simulator

  • These are used to build the reactor database used by DONJON5 for full-core simulations and to build artificial neural networks for models based on assembly considerations

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Summary

Introduction

Reactor models are usually based on infinite assembly calculations both for the depletion simulation and for the fuel loading model that calculate each fresh fuel composition in fuel cycle simulations [1]. In the CLASS package, reactor models rely mostly on ANN (Artificial Neural Networks) to predict infinite neutron multiplication coefficients and average cross-sections ((n, f ), (n, γ) and (n, 2n)) to solve Bateman equations Those neural networks are trained on a dedicated 1-group cross-sections database built with any fuel depletion software. This work offers a coupling between CLASS and DONJON5 for heterogeneous reactors: PWR loaded with All lattice calculations have been performed with DRAGON5 [5] These are used to build the reactor database used by DONJON5 for full-core simulations and to build artificial neural networks for models based on assembly considerations. The fuel loading model ( referred to as the equivalence model) that estimates the Pu content as a function of the uranium enrichment in the UOx fuels and the plutonium isotopic quality are presented in Section 5 followed respectively, in Sections 6 and 7, by CLASS analyses of an elementary scenario involving a single MOx/UO2 fuelled PWR and complex scenario that involves both UOx and MOx/UO2 heterogeneously fuelled PWRs

Assembly calculations
Fuel assembly
Reflector
Database characteristics
Diffusion database
Neural network database
Geometry description
Heterogeneous modeling for CLASS
Critical 235U enrichment predictions
Reloading burnup predictions
Critical boron calculations
Resulting accelerated calculations
Power equivalence for fresh fuel determination
ANN calculations for peaking factor estimation
Elementary scenarios
Reactor modeling with artificial neural networks
Scenario analysis
Complex scenarios involving UOx and heterogeneous reactors
Scenario characteristics
Results and physical analysis of scenario A
Findings
Impact of reactor parameters
Full Text
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