USE OF A STOCHASTIC MODEL TO EVALUATE UNCERTAINTY IN A PERFORMANCE ASSESSMENT AT THE SAVANNAH RIVER SITE - 8120

Conference · Mon Jan 21 00:00:00 EST 2008

OSTI ID:922656

Hiergesell, R; Glenn Taylor, G

A significant effort has recently been initiated to address probabilistic issues within radiological Performance Assessments (PA's) conducted at the Savannah River Site (SRS). This effort is considered to be part of a continual process, as is the program of PA analysis and maintenance across the Department of Energy (DOE) complex. At SRS, findings in the initial probabilistic analysis of the Slit Trenches in the E-Area PA were built upon and improved in the later development of the probabilistic model for the F-Area Tank Farm. Within the PA studies conducted at SRS, the initial effort of the uncertainty analyses was focused on the Slit Trenches as part of the E-Area PA. Specifically, a probabilistic model was developed for Slit Trench 5 within the E-Area. This model was utilized in deterministic mode to compare its results against the 2- and 3-D model results of the deterministic models. Then, utilizing the PDFs, the model was used to perform multiple realizations and produce probabilistic results. Later, a second probabilistic sensitivity and uncertainty analysis was undertaken for the F-Area Tank Farm PA. This effort is currently underway. Many improvements were made in how the flow and transport processes were incorporated within this model.

• Research Organization: Savannah River Site (SRS), Aiken, SC (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: DE-AC09-96SR18500
• OSTI ID: 922656
• Report Number(s): WSRC-STI-2008-00033; TRN: US0801726
• Resource Relation: Conference: WASTE MANAGEMENT 08
• Country of Publication: United States
• Language: English

Related Subjects

12 MANAGEMENT OF RADIOACTIVE WASTES, AND NON-RADIOACTIVE WASTES FROM NUCLEAR FACILITIES
99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
RADIOACTIVE WASTE FACILITIES
PERFORMANCE
SAVANNAH RIVER PLANT
STORAGE FACILITIES
PROBABILISTIC ESTIMATION
DATA COVARIANCES
MATHEMATICAL MODELS