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Savannah River marks the closure of another legacy waste tank
The Department of Energy’s Office of Environmental Management has received concurrence from regulators that Tank 14 at the Savannah River Site has reached preliminary cease waste removal (PCWR) status after radioactive liquid waste was successfully removed from the tank. PCWR is a regulatory milestone in the closure of SRS’s old-style waste tanks, which were built in the 1950s to store waste generated by the chemical separations of plutonium and uranium.
Emerald D. Ryan, Chad L. Pope
Nuclear Technology | Volume 206 | Number 10 | October 2020 | Pages 1506-1516
Technical Paper | doi.org/10.1080/00295450.2019.1704576
Articles are hosted by Taylor and Francis Online.
Flooding is a hazard for nuclear power plants (NPPs) and has caused extensive damage and economic impact. Improved NPP flooding risk characterization starts with improving scenario realism by using physics-based flooding simulations. Smoothed particle hydrodynamics (SPH) is one method for modeling fluid flow and is being investigated for NPP flooding simulation. While still in its infancy as a fluid simulation tool, SPH offers enticing features especially in three-dimensional modeling. However, when conducting SPH simulations, users must establish, inter alia, the appropriate particle spacing, which can be a tedious and time-consuming process. This paper describes the coupling of the SPH code Neutrino and the Idaho National Laboratory developed Risk Analysis Virtual Environment (RAVEN). By coupling Neutrino and RAVEN, the RAVEN optimization capabilities can now be applied to the particle spacing selection problem. A brief description of SPH, the overall capabilities of RAVEN, and the protocol used to couple the codes are provided. Additionally, the paper details a hypothetical problem and demonstrates the ability of automating the particle spacing selection and performing an example particle spacing optimization using RAVEN. With the Neutrino/RAVEN coupling established, a wide range of capabilities can now be utilized including optimization, reduced order model training and analysis, uncertainty quantification, sensitivity analysis, etc. Previously, these capabilities would require extensive work and time from the Neutrino user. Now, these capabilities are readily available and require only the creation of a RAVEN input file.