Uncertainty quantification for severe accident scenarios

Zheng, X.

no journal, , 

The severe accident researches, of both experiment and simulation, are active to assess the potential risk of a nuclear reactor. The results of the risk assessment, however, include significant uncertainties as a result of its innate complexity. The author introduces methods of how to quantify the uncertainties during the severe accident simulation. Integral severe accident codes have been developed to simulate accident progression and determine the consequences. Various accident scenarios are modeled within the probabilistic risk assessment (PRA) framework, which specifies the progression and probability of each accident scenario. Numerical models are generally the simplification of severe accidents, which diverges from the reality and causes the uncertainties. Methods of parameter and scenario uncertainty analysis are introduced in detail, including Monte-Carlo-sampling-based method, global sensitivity analysis, dynamic event tree, and surrogate models.