A publication of the Office of Advanced Simulation & Computing, NA-121.2, NNSA Defense Programs
March 2008
NA-ASC-500-08—Issue 6
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QASPR’s Successful “Blind” Best Estimate Plus Uncertainty Predictions vs. Experimental Data for Radiation Effects Study
The Sandia Qualification Alternative to the Sandia Pulsed Reactor (QASPR) project team has successfully made “blind” predictions, with associated uncertainty quantification, of short-pulse neutron effects test data that were taken at the Sandia Pulsed Reactor (SPR) before shutdown of that facility in FY06. This short-pulse neutron environment is a key component of the hostile radiation environment suite to which nuclear weapon reentry systems must be qualified. The QASPR project, which is developing a combined test- and simulation-based alternative to the previous test-only weapon qualification process, has relied heavily on ASC-funded software tools and computational infrastructure. Specifically, this project has employed Sandia’s ASC-funded RAMSES code for simulating the effects of short-pulse neutrons on electronic devices. RAMSES is built upon Sandia’s ASC-funded Trilinos mathematical software library, and has employed Sandia’s ASC-funded DAKOTA optimization/uncertainty quantification software toolkit. Sandia’s Verification and Validation program provided the methodology to develop confidence in the predictive capability of RAMSES via software verification testing, along with validation studies involving comparisons to experimental test data. In addition, many of the RAMSES calculations were performed on the ASC Purple computer, with RAMSES accounting for 99% of the ASC tri-laboratory C1 class (“full machine”) resources during the most recent Purple campaign. Current QASPR activities are focusing on additional “blind” comparisons of simulation data versus test data for more complex tests that also were conducted in the SPR facility before its shutdown at the end of FY06.
Electronic device micrograph (top),
computational model of the device (center),
and a detailed view of computational mesh used to model the device (bottom).
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