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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.
R. Accorsi, M. Marseguerra, E. Padovani, E. Zio
Nuclear Science and Engineering | Volume 132 | Number 3 | July 1999 | Pages 326-336
Technical Paper | doi.org/10.13182/NSE99-A2067
Articles are hosted by Taylor and Francis Online.
In real, complex plants, a sensitivity analysis of the effects that variations in the plant inputs and design parameters have on the outputs is of great importance both from the point of view of productivity and of safety. To a first approximation, sensitivity analysis consists of estimating the partial derivatives of the outputs with respect to the varied quantities. These derivatives cannot be obtained on the real plant directly since the effects of all the involved variables are intermixed. Therefore, one has to resort to suitable computational models and algorithms.A new neural network approach that aims at creating a differentiable copy of the plant is proposed. A feature of the method is that the data for network training are collected with the system in nominal operation: This represents, indeed, a fundamental constraint for all risky plants, for which unrestrained playing is definitely not recommended. The sensitivity coefficients (partial derivatives) thereby obtained are applied for the regulation of the reactivity of a simulated pressurized water reactor in response to changes in the electric load at the power grid, so as to maintain the average temperature of the water in the reactor core at a constant value.