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2026 ANS Annual Conference
May 31–June 3, 2026
Denver, CO|Sheraton Denver
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Latest News
DOE awards $2.7B for HALEU and LEU enrichment
Yesterday, the Department of Energy announced that three enrichment services companies have been awarded task orders worth $900 million each. Those task orders were given to American Centrifuge Operating (a Centrus Energy subsidiary) and General Matter, both of which will develop domestic HALEU enrichment capacity, along with Orano Federal Services, which will build domestic LEU enrichment capacity.
The DOE also announced that it has awarded Global Laser Enrichment an additional $28 million to continue advancing next generation enrichment technology.
Priscila Palma Sanchez, Adimir dos Santos
Nuclear Science and Engineering | Volume 195 | Number 5 | May 2021 | Pages 555-562
Technical Note | doi.org/10.1080/00295639.2020.1854541
Articles are hosted by Taylor and Francis Online.
In order to ensure safety in a nuclear power plant, operation and protection systems must take into account safety parameters, whether to guide operators or to trip the reactor in emergency cases. Especially in a boron-free small modular reactor (SMR) where reactivity and power are controlled exclusively by rod banks, the power distribution is mostly influenced by its movements affecting the power peaking factor (PPF), which is an important parameter to be considered. The PPF relates the maximum local linear power density to the average power density in a fuel rod indicating a high neutron flux that can cause fuel rod damage. In this technical note, 2117 samples from simulations of an idealized boron-free SMR controlled exclusively by rod banks were used to generate a Support Vector Machine (SVM) model capable of estimating the PPF as a function of control rod bank positions. Such model could be used to predict the maximum PPF in the reactor core by carrying out simple calculation. Residing in a SVM parameter grid search and a 10-cross-validation process in the training set to reach an optimized and robust model, the results have shown a root-mean-squared error of about 0.1% consistent for both training and testing sets.