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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.
Cihang Lu, Zeyun Wu
Nuclear Technology | Volume 208 | Number 1 | January 2022 | Pages 37-48
Technical Paper | doi.org/10.1080/00295450.2021.1874779
Articles are hosted by Taylor and Francis Online.
A one-dimensional (1-D) thermal stratification (TS) model was recently developed in our research group to predict the TS phenomenon in pool-type sodium-cooled fast reactors. This paper performs uncertainty quantification (UQ) of the 1-D TS model to evaluate its performance by considering the aleatoric uncertainties that existed in the model parameters and to identify the plausible sources of the epistemic uncertainties. The Latin hypercube sampling–Monte Carlo method (LHS-MC), which is elaborated with an example in this paper to facilitate its understanding and implementation, is used for the UQ process. The advantages of LHS-MC, including both better stability and better accuracy than the conventional random sampling–Monte Carlo method with fewer realizations, are demonstrated in this paper.
In total, 648 temperature measurements acquired from nine experimental transients performed in a university-scale Thermal Stratification Experimental Facility are used to evaluate the performance of the computational 1-D TS model. The UQ result shows that 77.5% of the experimental data can be predicted by the 1-D TS model within uncertainty ranges, which indicates the good performance of the computational model when the aleatoric uncertainties are correctly captured. The rest 22.5% of the experimental data are found located outside of the uncertainty ranges, which reveals the existence of the epistemic uncertainties caused by the lack of understanding of the TS phenomenon and defects in the 1-D model. The simple jet model currently employed by the 1-D TS model is thought to be one of the attributors to these defects.