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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.
Helin Gong, Sibo Cheng, Zhang Chen, Qing Li
Nuclear Science and Engineering | Volume 196 | Number 6 | June 2022 | Pages 668-693
Technical Paper | doi.org/10.1080/00295639.2021.2014752
Articles are hosted by Taylor and Francis Online.
This paper proposes an approach that combines reduced-order models with machine learning in order to create physics-informed digital twins to predict high-dimensional output quantities of interest, such as neutron flux and power distributions in nuclear reactor cores. The digital twin is designed to solve forward problems given input parameters, as well as to solve inverse problems given some extra measurements. Offline, we use reduced-order modeling, namely, the proper orthogonal decomposition, to assemble physics-based computational models that are accurate enough for the fast predictive digital twin. The machine learning techniques, namely, k-nearest-neighbors and decision trees, are used to formulate the input-parameter-dependent coefficients of the reduced basis, after which the high-fidelity fields are able to be reconstructed. Online, we use the real-time input parameters to rapidly reconstruct the neutron field in the core based on the adapted physics-based digital twin. The effectiveness of the framework is illustrated through a real engineering problem in nuclear reactor physics—reactor core simulation in the life cycle of the HPR1000 governed by the two-group neutron diffusion equations affected by input parameters, i.e., burnup, control rod inserting step, power level, and temperature of the coolant—which shows potential applications for online monitoring purposes.