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Fuel Cycle & Waste Management
Devoted to all aspects of the nuclear fuel cycle including waste management, worldwide. Division specific areas of interest and involvement include uranium conversion and enrichment; fuel fabrication, management (in-core and ex-core) and recycle; transportation; safeguards; high-level, low-level and mixed waste management and disposal; public policy and program management; decontamination and decommissioning environmental restoration; and excess weapons materials disposition.
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2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
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Latest News
College students help develop waste-measuring device at Hanford
A partnership between Washington River Protection Solutions (WRPS) and Washington State University has resulted in the development of a device to measure radioactive and chemical tank waste at the Hanford Site. WRPS is the contractor at Hanford for the Department of Energy’s Office of Environmental Management.
Taro Ueki
Nuclear Science and Engineering | Volume 180 | Number 1 | May 2015 | Pages 58-68
Technical Paper | doi.org/10.13182/NSE14-54
Articles are hosted by Taylor and Francis Online.
The overlapping batch means method (OBM) has been investigated for robust statistical error estimation of local power tallies in Monte Carlo (MC) reactor core calculation. Originally, a nonoverlapping version was introduced in MC criticality calculation by Gelbard and Prael. However, the issue of batch size optimization was thought of as a lack of robustness. In this work, OBM with asymptotic bias correction was implemented with the batch size of the square root of the number of generations and compared with the orthonormally weighted standardized time series method (OWSTS). Numerical tests were conducted for various positions of the core of a pressurized water reactor. Results obtained indicate that neither OBM nor OWSTS consistently outperforms the other in terms of an overall performance measure incorporating bias and stability. Therefore, OBM with asymptotic bias correction can be an option to statistical error estimation in production MC criticality codes since OWSTS lacks an automated process to determine the number of weighting functions and can output the estimate only at the final generation. It is also shown that OBM with asymptotic bias correction performs equally regardless of the batch size.