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The busyness of the nuclear fuel supply chain
Ken Petersenpresident@ans.org
With all that is happening in the industry these days, the nuclear fuel supply chain is still a hot topic. The Russian assault in Ukraine continues to upend the “where” and “how” of attaining nuclear fuel—and it has also motivated U.S. legislators to act.
Two years into the Russian war with Ukraine, things are different. The Inflation Reduction Act was passed in 2022, authorizing $700 million in funding to support production of high-assay low-enriched uranium in the United States. Meanwhile, the Department of Energy this January issued a $500 million request for proposals to stimulate new HALEU production. The Emergency National Security Supplemental Appropriations Act of 2024 includes $2.7 billion in funding for new uranium enrichment production. This funding was diverted from the Civil Nuclear Credits program and will only be released if there is a ban on importing Russian uranium into the United States—which could happen by the time this column is published, as legislation that bans Russian uranium has passed the House as of this writing and is headed for the Senate. Also being considered is legislation that would sanction Russian uranium. Alternatively, the Biden-Harris administration may choose to ban Russian uranium without legislation in order to obtain access to the $2.7 billion in funding.
John C. Wagner, Alireza Haghighat
Nuclear Science and Engineering | Volume 128 | Number 2 | February 1998 | Pages 186-208
Technical Paper | doi.org/10.13182/NSE98-2
Articles are hosted by Taylor and Francis Online.
Although the Monte Carlo method is considered to be the most accurate method available for solving radiation transport problems, its applicability is limited by its computational expense. Thus, biasing techniques, which require intuition, guesswork, and iterations involving manual adjustments, are employed to make reactor shielding calculations feasible. To overcome this difficulty, we have developed a method for using the SN adjoint function for automated variance reduction of Monte Carlo calculations through source biasing and consistent transport biasing with the weight window technique. We describe the implementation of this method into the standard production Monte Carlo code MCNP and its application to a realistic calculation, namely, the reactor cavity dosimetry calculation. The computational effectiveness of the method, as demonstrated through the increase in calculational efficiency, is demonstrated and quantified. Important issues associated with this method and its efficient use are addressed and analyzed. Additional benefits in terms of the reduction in time and effort required of the user are difficult to quantify but are possibly as important as the computational efficiency. In general, the automated variance reduction method presented is capable of increases in computational performance on the order of thousands, while at the same time significantly reducing the current requirements for user experience, time, and effort. Therefore, this method can substantially increase the applicability and reliability of Monte Carlo for large, real-world shielding applications.