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LLNL, Ampera partner to develop thorium-based TRISO fuel
Lawrence Livermore National Laboratory has formed a strategic partnership with Ampera to develop the company’s nuclear fuel concept through a project named THUNDER, for Thorium Unimodal Droplet Ejection for Reactors.
The focus of THUNDER is fabricating TRISO made with kernels of thorium rather than the usual uranium. LLNL and Ampera will evaluate and optimize liquid metal–jetting technology to produce highly uniform, spherical kernels of thorium-232 for later processing into TRISO fuel.
Nicolas Shugart, Jeffrey King, Jake Jacobson
Nuclear Technology | Volume 204 | Number 2 | November 2018 | Pages 147-161
Technical Paper | doi.org/10.1080/00295450.2018.1469350
Articles are hosted by Taylor and Francis Online.
SafeGuards Analysis (SGA) is a toolbox developed to allow engineers and scientists to create detailed simulations of safeguards material control and accountability simulations. SGA accepts material flow data from an external material flow model and can be used with any existing fuel cycle or material control code. This paper examines some new developments to the SGA code that allow the code to consider material losses over long time frames. The first scenario described in this paper examined an enrichment facility consisting of two material balance areas (MBAs). Cumulative sum and basic control chart tests were evaluated for a case involving a loss of material from both MBAs simultaneously and a case in which material is removed from the facility over a timescale of double the one that the tests were calibrated to detect. A second scenario represents an entire fuel cycle consisting of four MBAs and two materials of interest (low-enriched uranium and plutonium). This scenario evaluated the calibrated safeguards system with three blind unidentified stream cases, with the goal of determining the calibrated system’s ability to detect where the material loss occurred in each case. SGA was able to produce the expected results for all of the examples examined in this paper, demonstrating that modules produced using the toolbox are capable of examining larger systems in realistic multi-MBA scenarios.