A stochastic method to propagate uncertainties along large cores deterministic calculations

Ludovic Volat (1, 2) , Bernard Gastaldi (1, 2) , Alain Santamarina (1, 2)

1 LE2C - Laboratoire d'Etudes des Coeurs et du Cycle
2 SPRC - Service de Physique des Réacteurs et du Cycle

Deterministic uncertainty propagation methods are certainly powerful and time-sparing but their access to uncertainties related to the power map remains difficult due to a lack of numerical convergence. On the contrary,stochastic methods do notface such an issue and they enable a more rigorous access to uncertainty related to the PFNS. Our method combines an innovative transport calculation chain and a stochastic way of propagating uncertainties on nuclear data: first, our calculation scheme consists in the calculation of assembly self-shielded cross sections and a pin-by-pin flux calculation on the whole core. Validation was done and the required CPU time is suitable to allow numerous calculations. Then, we sample nuclear cross sections with consistent probability distribution functions with a correlated optimized Latin Hypercube Sampling. Finally, we deduce the power map uncertainties from the study of the output response functions. We performed our study on the system described in the framework of the OECD/NEA Expert Group in Uncertainty Analysis in Modelling. Results show the $^{238}$U inelastic scattering crosssection,the $^{235}$U PFNS,the elastic scattering crosssection of $^1$H and the $^{56}$Fe crosssections as major contributors to the total uncertainty on the power map: the power tilt between central and peripheral assemblies using COMAC-V2 covariance library amountsto 5.4% (1$\sigma$)(respectively 7.4% (1$\sigma$) using COMAC-V0).

Keywords: GEN III cores; Uncertainty propagation; Power map; Stochastic Propagation

Domains: Physics [physics]

Ludovic Volat, Bernard Gastaldi, Alain Santamarina. A stochastic method to propagate uncertainties along large cores deterministic calculations. EPJ N - Nuclear Sciences & Technologies, In press, ⟨10.1051/epjn/2018015⟩. ⟨cea-01597850v2⟩