This report represents completion of milestone deliverable M2SF-19SNO10309013 "Online Waste Library (OWL) and Waste Forms Characteristics Annual Report" that reports annual status on fiscal year (FY) 2019 activities for the work package SF-19SN01030901 and is due on August 2, 2019. The online waste library (OWL) has been designed to contain information regarding United States (U.S.) Department of Energy (DOE)-managed (as) high-level waste (DHLW), spent nuclear fuel (SNF), and other wastes that are likely candidates for deep geologic disposal, with links to the current supporting documents for the data (when possible; note that no classified or official-use-only (OUO) data are planned to be included in OWL). There may be up to several hundred different DOE-managed wastes that are likely to require deep geologic disposal. This annual report on FY2019 activities includes evaluations of waste form characteristics and waste form performance models, updates to the OWL development, and descriptions of the management processes for the OWL. Updates to the OWL include an updated user's guide, additions to the OWL database content for wastes and waste forms, results of the beta testing and changes implemented from it. Also added are descriptions of the management/control processes for the OWL development, version control, and archiving. These processes have been implemented as part of the full production release of OWL (i.e., OWL Version 1.0), which has been developed on, and will be hosted and managed on, Sandia National Laboratories (SNL) systems. The version control/update processes will be implemented for updates to the OWL in the future. Additionally, another process covering methods for interfacing with the DOE SNF Database (DOE 2007) at Idaho National Laboratory on the numerous entries for DOE-managed SNF (DSNF) has been pushed forward by defining data exchanges and is planned to be implemented sometime in FY2020. The INL database is also sometimes referred to as the Spent Fuel Database or the SFDB, which is the acronym that will be used in this report. Once fully implemented, this integration effort will serve as a template for interfacing with additional databases throughout the DOE complex.
The third Sandia Fracture Challenge highlighted the geometric and material uncertainties introduced by modern additive manufacturing techniques. Tasked with the challenge of predicting failure of a complex additively-manufactured geometry made of 316L stainless steel, we combined a rigorous material calibration scheme with a number of statistical assessments of problem uncertainties. Specifically, we used optimization techniques to calibrate a rate-dependent and anisotropic Hill plasticity model to represent material deformation coupled with a damage model driven by void growth and nucleation. Through targeted simulation studies we assessed the influence of internal voids and surface flaws on the specimens of interest in the challenge which guided our material modeling choices. Employing the Kolmogorov–Smirnov test statistic, we developed a representative suite of simulations to account for the geometric variability of test specimens and the variability introduced by material parameter uncertainty. This approach allowed the team to successfully predict the failure mode of the experimental test population as well as the global response with a high degree of accuracy.
This study examines the single-event upset and single-event latch-up susceptibility of the Xilinx 16nm FinFET Zynq UltraScale+ RFSoC FPGA in proton irradiation. Results for SEU in configuration memory, BlockRAM memory, and device SEL are given.
This study examines the single-event upset and single-event latch-up susceptibility of the Xilinx 16nm FinFET Zynq UltraScale+ RFSoC FPGA in proton irradiation. Results for SEU in configuration memory, BlockRAM memory, and device SEL are given.
