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Front-end nuclear fuel supply cooperation: Turning allied interdependence into strategic advantage
The global nuclear revival, which is fueled by unprecedented demand for firm, affordable, dispatchable power for artificial intelligence and data center build-out, energy security imperatives, and climate commitments, has exposed a structural reality of the Western fuel cycle: No single allied nation currently possesses the full suite of front-end capabilities. From mining through conversion, enrichment, fabrication, and the emerging deconversion and metallization steps required for reactor fuels, capability is distributed across Canada, France, Japan, the United Kingdom, and the United States (collectively, the “Sapporo Five”), as well as a small group of close partners.
Troy L. Becker, Allan B. Wollaber, Edward W. Larsen
Nuclear Science and Engineering | Volume 155 | Number 2 | February 2007 | Pages 155-167
Technical Paper | Mathematics and Computation, Supercomputing, Reactor Physics and Nuclear and Biological Applications | doi.org/10.13182/NSE07-A2653
Articles are hosted by Taylor and Francis Online.
A new hybrid Monte Carlo-Deterministic technique is presented for simulating global particle transport problems, in which flux estimates are desired at all physical locations in the system. This technique has two steps: First, an inexpensive deterministic global estimate of the forward flux is obtained; then Monte Carlo is used to estimate the multiplicative correction to the deterministic flux estimate. We call the multiplicative correction to the deterministic flux the correcton flux, and the Monte Carlo particles that estimate this flux correctons. For deep-penetration problems, the correcton flux has significantly less spatial variation than the physical flux. Therefore, the Monte Carlo process automatically distributes correctons much more uniformly across the system than it distributes Monte Carlo particles for the original angular flux. In the "deep" parts of the problem, at locations far from the source, this results in a greatly reduced variance and a greatly increased figure of merit.