Differential parameters uncertainty estimation via a PCA-based monte carlo sampling approach: IRT-4M fuel type as a case study

In this work, a hybrid Monte Carlo/sensitivity-based uncertainty contribution estimation approach is derived and tested. The approach is based on using the statistical samples used in the Monte Carlo uncertainty quantification combined with the subspace analysis to define and solve a set of linear equations to estimate the first-order sensitivities whenever the adjoint and forward sensitivity analyses are not available. The proposed approach empowers the Monte Carlo uncertainty quantification method with a pathway to evaluate and rank the uncertainty contributors efficiently without prior access to the sensitivity coefficients. The proposed approach has been illustrated and verified via an IRT-4 M 8 tubes fuel assembly uncertainty quantification problem where the proposed approach is compared to the sensitivity-based adjoint method. Results indicate that both methods concluded the same key differential contributors to the integral uncertainty with good agreement in the values of the sensitivity coefficients.