The Composite Analysis (CA) performed for the Savannah River Site (SRS) in 2009 (SRS CA 2009) included a simplified uncertainty analysis. The uncertainty analysis in the CA (Smith et al. 2009b) was limited to considering at most five sources in a separate uncertainty calculation performed for each POA. To perform the uncertainty calculations in a reasonable amount of time, the analysis was limited to using 400 realizations, 2,000 years of simulated transport time, and the time steps used for the uncertainty analysis were increased from what was used in the CA base case analysis. As part of the CA maintenance plan, the Savannah River National Laboratory (SRNL) committed to improving the CA uncertainty/sensitivity analysis. The previous uncertainty analysis was constrained by the standard GoldSim licensing which limits the user to running at most four Monte Carlo uncertainty calculations (also called realizations) simultaneously. Some of the limitations on the number of realizations that could be practically run and the simulation time steps were removed by building a cluster of three HP Proliant windows servers with a total of 36 64-bit processors and by licensing the GoldSim DP-Plus distributed processing software. This allowed running as many as 35 realizations simultaneously (one processor is reserved as a master process that controls running the realizations). These enhancements to SRNL computing capabilities made uncertainty analysis: using 1000 realizations, using the time steps employed in the base case CA calculations, with more sources, and simulating radionuclide transport for 10,000 years feasible. In addition, an importance screening analysis was performed to identify the class of stochastic variables that have the most significant impact on model uncertainty. This analysis ran the uncertainty model separately testing the response to variations in the following five sets of model parameters: (a) K{sub d} values (72 parameters for the 36 CA elements in sand and clay), (b) Dose Parameters (34 parameters), (c) Material Properties (20 parameters), (d) Surface Water Flows (6 parameters), and (e) Vadose and Aquifer Flow (4 parameters). Results provided an assessment of which group of parameters is most significant in the dose uncertainty. It was found that K{sub d} and the vadose/aquifer flow parameters, both of which impact transport timing, had the greatest impact on dose uncertainty. Dose parameters had an intermediate level of impact while material properties and surface water flows had little impact on dose uncertainty. Results of the importance analysis are discussed further in Section 7 of this report. The objectives of this work were to address comments received during the CA review on the uncertainty analysis and to demonstrate an improved methodology for CA uncertainty calculations as part of CA maintenance. This report partially addresses the LFRG Review Team issue of producing an enhanced CA sensitivity and uncertainty analysis. This is described in Table 1-1 which provides specific responses to pertinent CA maintenance items extracted from Section 11 of the SRS CA (2009). As noted above, the original uncertainty analysis looked at each POA separately and only included the effects from at most five sources giving the highest peak doses at each POA. Only 17 of the 152 CA sources were used in the original uncertainty analysis and the simulation time was reduced from 10,000 to 2,000 years. A major constraint on the original uncertainty analysis was the limitation of only being able to use at most four distributed processes. This work expanded the analysis to 10,000 years using 39 of the CA sources, included cumulative dose effects at downstream POAs, with more realizations (1,000) and finer time steps. This was accomplished by using the GoldSim DP-Plus module and the 36 processors available on a new windows cluster. The last part of the work looked at the contribution to overall uncertainty from the main categories of uncertainty variables: K{sub d}s, dose parameters, flow parameters, and material properties. This was not intended to be a detailed sensitivity analysis but only to see in very general terms what broad category of parameters contributes most significantly to overall uncertainty in the CA dose. This analysis was not intended to be a final CA uncertainty calculation and there was no intent to revise the stochastic distributions as part of this work.
