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DOE awards ANS-backed workforce consortium $19.2M
The Department of Energy’s Office of Nuclear Energy recently awarded about $49.7 million to 10 university-led projects aiming to develop nuclear workforce training programs around the country.
DOE-NE issued its largest award, $19.2 million, to the newly formed Great Lakes Partnership to Enhance the Nuclear Workforce (GLP). This regional consortium, which is led by the University of Toledo and includes the American Nuclear Society, will use the funds to fill a variety of existing gaps in the nuclear workforce pipeline.
Sarah R. Suffield, William A. Perkins, Ben J. Jensen, Brady D. Hanson, Steven B. Ross
Nuclear Technology | Volume 211 | Number 2 | February 2025 | Pages 241-257
Research Article | doi.org/10.1080/00295450.2024.2324213
Articles are hosted by Taylor and Francis Online.
Deposition models are being developed with the commercial computational fluid dynamics software STAR-CCM+ to evaluate particulate deposition on spent nuclear fuel (SNF) canisters. The primary particulate of concern is chloride salts, which are dispersed in the atmosphere and then deposited onto the canisters. During dry storage, the primary degradation process is likely to be chloride-induced stress corrosion cracking (CISCC) at the heat-affected zones of the canister welds. It is known that stainless steel canisters are susceptible to CISCC; however, the rate of chloride deposition onto the canisters is poorly known, based on sparse field data from a small number of sites.
This paper describes work looking at various approaches to modeling turbulence, such as Reynolds-averaged Navier Stokes (RANS) and large eddy simulation (LES), and its impact on particle flow and deposition within a ventilated SNF storage system. The deposition rate is determined for a vertical canister system and a horizontal canister system. LES has the potential to simulate turbulent flows more accurately versus RANS, but is much more computationally expensive. A k-omega version of the RANS turbulence model was used for this study.
The computational efficient RANS steady-state model predicted a similar canister deposition result as the LES simulation for a vertical canister storage system. For a horizontal storage system, the RANS steady-state model predicted more particles depositing on the canister than the LES simulation, providing a conservative estimation for particle deposition. A wall correction factor was added to the RANS model to dampen the turbulence fluctuation normal to a surface that left undamped, leads to the RANS model overpredicting deposition along a surface for smaller particles.
This work was done to further development of deposition models that could be used to plan and inform SNF canister aging management programs with predictive models for the timing and occurrence of canister CISCC. This work is part of a larger effort tasked with understanding the likelihood of canister degradation due to CISCC. These models are still under development, and testing is needed for validation. However, these models are being presented now to demonstrate to canister vendors, utilities, regulators, and stakeholders the value of this type of modeling.