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Glass strategy: Hanford’s enhanced waste glass program
The mission of the Department of Energy’s Office of River Protection (ORP) is to complete the safe cleanup of waste resulting from decades of nuclear weapons development. One of the most technologically challenging responsibilities is the safe disposition of approximately 56 million gallons of radioactive waste historically stored in 177 tanks at the Hanford Site in Washington state.
ORP has a clear incentive to reduce the overall mission duration and cost. One pathway is to develop and deploy innovative technical solutions that can advance baseline flow sheets toward higher efficiency operations while reducing identified risks without compromising safety. Vitrification is the baseline process that will convert both high-level and low-level radioactive waste at Hanford into a stable glass waste form for long-term storage and disposal.
Although vitrification is a mature technology, there are key areas where technology can further reduce operational risks, advance baseline processes to maximize waste throughput, and provide the underpinning to enhance operational flexibility; all steps in reducing mission duration and cost.
Roger L. Martz, Kevin M. Marshall
Nuclear Technology | Volume 184 | Number 2 | November 2013 | Pages 239-248
Technical Paper | Radiation Transport and Protection | doi.org/10.13182/NT13-A22319
Articles are hosted by Taylor and Francis Online.
MCNP6 has been extended to include a new capability that permits tracking of neutrons and photons on an unstructured mesh (UM) embedded as a mesh universe within its constructive solid geometry capability. Our mesh geometry was created through Abaqus/CAE using its solid modeling capabilities. Monte Carlo transport results are calculated for mesh elements using a path length estimator while particles track from element face to element face on the mesh. This paper presents some performance comparisons for the initialization and calculation phases of two well-known benchmark problems using both the legacy and the UM tracking capabilities. For detailed geometries, UM initialization is always faster. For very detailed geometries where the models are comparable, the UM capability is faster than the legacy geometry capability.