Uncertainty Quantification Spectral Technique for the Stochastic Point Reactor with Random Parameters

Abstract

The stochastic point reactor with random parameters is considered in this work. The hybrid uncertain variations—noise and random parameters—are analyzed with the spectral techniques for the efficiency and high rates of convergence. The proposed hybrid technique enables one to derive an equivalent deterministic system that can be solved to get the mean solution and deviations due to each uncertainty. The contributions of different sources uncertainties can be decomposed and quantified. The deviations in the thermal hydraulics are also computed in the current work. Two model reactors are tested with the proposed technique and the comparisons show the advantages and efficiency compared with the other techniques.

A New Model for the Stochastic Point Reactor: Development and Comparison with Available Models

“…This second model drives simplified stochastic point kinetic equations (SSPK) by ignoring the covariances between different components. The SSPK model was further considered in [5] by using the Wiener-Hermite expansion (WHE) spectral technique to avoid sampling of the stochastic terms and to allow for random variation of the model parameters [6].…”