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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.
Hyung Jin Shim, Chang Hyo Kim
Nuclear Science and Engineering | Volume 162 | Number 1 | May 2009 | Pages 98-108
Technical Paper | doi.org/10.13182/NSE09-2
Articles are hosted by Taylor and Francis Online.
The sample variance of a tally in Monte Carlo eigenvalue calculations is biased because of an intercycle correlation between the fission source distributions (FSDs). How to estimate the variance bias or equivalently how to calculate the real variance has been an interesting subject of study. This paper proposes a new method to estimate the real variance based on an intercycle covariance of the FSDs that can be derived from the cycle-by-cycle stochastic error propagation model. The proposed method enables one to calculate every intercycle covariance of a tally accurately, regardless of the number of active cycles. Therefore, the method can be applied satisfactorily even to problems with the dominance ratio (DR) close to 1. The accuracy of the new method is examined for small- and medium-sized pressurized water reactor core problems and a fuel storage facility problem exhibiting a slow source convergence. It is shown that the new method is capable of predicting the variance bias strikingly better than the existing methods, especially for problems with high DRs.