Addressing geometrical perturbations by applying generalized polynomial chaos to virtual density in continuous energy Monte-Carlo power iteration

TL;DR

This paper introduces a generalized polynomial chaos approach to efficiently estimate the impact of geometrical perturbations on reactor reactivity within Monte Carlo simulations, enabling rapid and accurate predictions for various deformations.

Contribution

It presents a novel algorithm applying intrusive polynomial chaos to model geometrical perturbations in Monte Carlo power iteration, avoiding the need for adjoint flux calculations.

Findings

Accurately estimates reactivity changes due to uniform geometrical deformations.

Converges rapidly with polynomial order, reducing computational cost.

Works for arbitrary geometries and a wide range of deformations within a single simulation.

Abstract

In this work, we revisit the use of the virtual density method to model uniform geometrical perturbations. We propose a general algorithm in order to estimate explicitly the effect of geometrical perturbations in continuous-energy Monte Carlo power iteration simulations. We apply the intrusive generalized polynomial chaos method in order to estimate the coefficients of a reduced model giving the multiplication factor as a function of the amplitude of the geometrical perturbation. Our method accurately estimates the reactivity change induced by uniform expansion or swelling deformations of arbitrary geometries, for a large range of deformations within a single Monte Carlo simulation. The reduced model converges rapidly in polynomial order, does not require knowledge of the adjoint flux, and is free from indirect effects.