Publications

5 Results
Skip to search filters

Fire-Induced Pressure Response and Failure Characterization of PCV/SCV/3013 Containers - Phase 3

Mendoza, Hector M.; Baird, Austin R.; Gill, Walt; Figueroa Faria, Victor G.; McClard, James M.; Sprankle, Ray S.; Hensel, Steve H.; Michel, Danielle M.; Adee, Shane M.

Several Department of Energy (DOE) facilities have materials stored in robust, welded, stainless - steel containers with presumed fire - induced pressure response behaviors. Lack of test data related to fire exposure requires conservative safety analysis assumptions for container response at these facilities. This conservatism can in turn result in the implementation of challenging operational restrictions with costly nuclear safety controls. To help address this issue for sites that store DOE 3013 stainless - steel containers, a series of ten tests were undertaken at Sandia National Laboratories. The goal of this test series was to obtain the response behavior for various configurations of DOE 3013 containers with various payload compositions when exposed to one of two ASTM fire conditions. Key parameters measured in the test series included identification of failure - specific characteristics such as pressure, temperature, and whether or not a vessel was breached during a test . Numerous failure - specific characteristics were identified from the ten tests. This report describes the implementation and execution of the test series performed to identify these failure - specific characteristics. Discussions on the test configurations, payload compositions, thermal insults, and experimental setups are presented. Test results in terms of pressurization and vessel breach (or no - breach) are presented along with corresponding discussions for each test.

More Details

Designing and modeling analog neural network training accelerators

2019 International Symposium on VLSI Technology, Systems and Application, VLSI-TSA 2019

Agarwal, Sapan A.; Jacobs-Gedrim, Robin B.; Bennett, Christopher H.; Hsia, Alexander W.; Adee, Shane M.; Hughart, David R.; Fuller, Elliot J.; Li, Yiyang; Talin, A.A.; Marinella, Matthew J.

Analog crossbars have the potential to reduce the energy and latency required to train a neural network by three orders of magnitude when compared to an optimized digital ASIC. The crossbar simulator, CrossSim, can be used to model device nonidealities and determine what device properties are needed to create an accurate neural network accelerator. Experimentally measured device statistics are used to simulate neural network training accuracy and compare different classes of devices including TaOx ReRAM, Lir-Co-Oz devices, and conventional floating gate SONOS memories. A technique called 'Periodic Carry' can overcomes device nonidealities by using a positional number system while maintaining the benefit of parallel analog matrix operations.

More Details
5 Results
5 Results