TREATMENT OF UNCERTAINTY IN PERFORMANCE ASSESSMENTS FOR COMPLEX-SYSTEMS

When viewed at a high level, performance assessments (PAs) for complex systems involve two types of uncertainty: stochastic uncertainty, whi ch arises because the system under study can behave in many different ways, and subjective uncertainty, which arises from a lack of knowledg e about quantities required within the computational implementation of the PA. Stochastic uncertainty is typically incorporated into a PA wi th an experimental design based on importance sampling and leads to th e final results of the PA being expressed as a complementary cumulativ e distribution function (CCDF). Subjective uncertainty is usually trea ted with Monte Carlo techniques and leads to a distribution of CCDFs. This presentation discusses the use of the Kaplan/Garrick ordered trip le representation for risk in maintaining a distinction between stocha stic and subjective uncertainty in PAs for complex systems. The topics discussed include (1) the definition of scenarios and the calculation of scenario probabilities and consequences, (2) the separation of sub jective and stochastic uncertainties, (3) the construction of CCDFs re quired in comparisons with regulatory standards (e.g., 40 CFR Part 191 , Subpart B for the disposal of radioactive waste), and (4) the perfor mance of uncertainty and sensitivity studies. Results obtained in a pr eliminary PA for the Waste Isolation Pilot Plant, an uncertainty and s ensitivity analysis of the MACCS reactor accident consequence analysis model, and the NUREG-1150 probabilistic risk assessments are used for illustration.