DONJON5/CLASS coupled simulations of MOX/UO2 heterogeneous PWR core

Author:

Paradis Maxime,Doligez Xavier,Marleau Guy,Ernoult Marc,Thiollière Nicolas

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.

Publisher

EDP Sciences

Subject

General Medicine

Reference22 articles.

1. A neural network approach for burn-up calculation and its application to the dynamic fuel cycle code CLASS

2. Coupled CLASS and DONJON5 3D full-core calculations and comparison with the neural network approach for fuel cycles involving MOX fueled PWRs

3. Hébert A., Sekki D., Chambon R., A User Guide for DONJON Version 5, Institut de génie nucléaire, Département de génie mécanique, École Polytechnique de Montréal. Montréal QC, Canada, Tech. Rep. IGE-344, 2019

4. Mouginot B. et al., Core library for advanced scenario simulation, CLASS: principle & application, in International Conference “The Role of Reactor Physics toward a Sustainable Future” (PHYSOR 2014) (2014), pp. 12

5. Marleau G., Hébert A., Roy R., A User Guide for DRAGON Version 5, Institut de génie nucléaire, Département de génie physique, École Polytechnique de Montréal. Montréal QC, Canada, Tech. Rep. IGE-335, 2018

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