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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.
M. J. Fleming, L. W. G. Morgan, E. Shwageraus
Nuclear Science and Engineering | Volume 183 | Number 2 | June 2016 | Pages 173-184
Technical Paper | doi.org/10.13182/NSE15-55
Articles are hosted by Taylor and Francis Online.
Modeling of nuclide densities as a function of time within magnetic confinement fusion devices such as the JET, ITER, and proposed DEMO tokamaks is performed using Monte Carlo transport codes coupled with a Bateman equation solver. The generation of reaction rates occurs through either pointwise interpolation of energy-dependent tracked particle data with nuclear data or multigroup (MG) convolution of binned fluxes with binned cross sections. The MG approach benefits from decreased computational expense and data portability, but introduces errors through effects such as self-shielding. Depending on the MG structure and nuclear data used, this method can introduce unacceptable errors without warning. We present a MG optimization method that utilizes a modified particle swarm algorithm to generate seed solutions for a nonstochastic string-tightening algorithm. This procedure has been used with a semihomogenized one-dimensional DEMO-like reactor design to produce an optimized energy group structure for tritium breeding. In this example, the errors introduced by the Vitamin-J 175 MG are reduced by two orders of magnitude in the optimized group structure.