Publications Details

A review of techniques for propagating data and parameter uncertainties in high-level radioactive waste repository performance assessment models

Zimmerman, D.A.; Wahl, K.K.; Gutjahr, A.L.; Davis, P.A.

Techniques for propagating data and parameter uncertainties in high-level waste (HLW) repository performance assessment models are discussed. Uncertainty analysis techniques techniques ascribe quantitative measures of reliability to model predictions. Both 10 CFR 60 and 40 CFR 191 require consideration of uncertainties, including uncertainties in data and parameters, in the performance assessment of an HLW repository system. Four categories of uncertainty analysis methods are discussed: Monte Carlo simulation, replacement models (response surface techniques), differential techniques (direct, adjoint, and Green's function technique), and geostatistical techniques (stochastic modeling using Monte Carlo simulation and spectral analysis). Advantages, disadvantages and applications of each technique are presented. Propagation of uncertainties through multiple, linked models is also discussed. Application of these techniques to sensitivity analysis is also presented. Sensitivity analyses can be useful to uncertainty studies because the number of parameters included in the uncertainty analysis can be reduced by eliminating those parameters for which the uncertainty has a minimal effect on the performance variable(s).