ANS is committed to advancing, fostering, and promoting the development and application of nuclear sciences and technologies to benefit society.
Explore the many uses for nuclear science and its impact on energy, the environment, healthcare, food, and more.
Explore membership for yourself or for your organization.
Conference Spotlight
2026 Nuclear Energy Conference & Expo (NECX)
August 24–27, 2026
Dallas, TX|Hilton Anatole
Latest Magazine Issues
Jul 2026
Jan 2026
2026
Latest Journal Issues
Nuclear Science and Engineering
September 2026
Nuclear Technology
August 2026
Fusion Science and Technology
Latest News
Texas A&M welcomes uranium conversion research
The Texas A&M Engineering Experiment Station (TEES) has signed a research agreement with Quantum Leap Energy (QLE) “to advance and de-risk the commercial production of high-purity uranium hexafluoride (UF6).”
QLE is an Austin, Texas–based subsidiary of ASP Isotopes (ASPI), which is developing an isotope enrichment platform for applications in nuclear energy, nuclear medicine, and semiconductors. QLE specializes in the uranium conversion step of the nuclear fuel cycle—the conversion of yellowcake uranium concentrate (U3O8) into UF6 prior to enrichment.
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.