Investigation of uncertainty quantification methods for constitutive models and the application to LOFT LBLOCA

Abstract Best estimate (BE) codes are developed to carry out realistic safety analysis of the nuclear reactor, and generally hundreds of constitutive models are comprised in a BE code. Nevertheless, uncertainties of these constitutive models are often not properly handled in the best estimate plus uncertainty (BEPU) analysis. It is not sufficient to fully evaluate the uncertainties of different sorts of models with only one method. Thereby a structural method for uncertainty quantification (UQ) of constitutive models is proposed and relevant description is presented. Based on the method, constitutive models will be classified into two categories according to the characteristics, namely the independent model and the dependent model, and different methods will be adopted for different sorts of model. Several statistical methods for UQ of constitutive models such as the non-parametric curve estimation method, the Bayesian calibration method and the coverage calibration method are evaluated and the characteristics of these methods are discussed. In addition, several methods for the construction of surrogate model are utilized to reduce the computational cost, and a model selection technique is adopted to opt the optimal model among all alternative models. The large break loss of coolant accident (LBLOCA) experiment LOFT LP-02-6 is utilized to verify the proposed structural method, and the BEPU analysis of the LP-02-6 experiment is carried out. The results show that uncertainty intervals of the identified models obtained through the structural method are reasonable, uncertainties of the peak cladding temperature (PCT) as well as the accumulator injection time (AIT) are quantified, and the sensitivity analysis is carried out to evaluate the influence of different input parameters on the 1st PCT, 2nd PCT and the AIT.