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.
Yuqing Dai, Ming Lin, Maosong Cheng, Xiangzhou Cai
Nuclear Science and Engineering | Volume 200 | Number 8 | August 2026 | Pages 1876-1897
Research Article | doi.org/10.1080/00295639.2025.2552057
Articles are hosted by Taylor and Francis Online.
High-fidelity computational fluid dynamics simulations can effectively capture transient three-dimensional thermal-fluid phenomena in molten salt reactors (MSRs), but they are computationally expensive and time consuming. The dynamic mode decomposition (DMD) method is used to improve simulation efficiency. The research findings indicate that the DMD method encounters processing difficulties and numerical instability issues when handling a large-scale transient data set. To address these issues, three domain decomposition strategies are proposed: geometry-based, velocity-based, and temperature-based clustering. All three methods effectively improve numerical stability and modeling efficiency, with the velocity-based decomposition showing the best performance.
Based on this method, the number of modes in each subdomain is optimized to construct an efficient and accurate domain-decomposed DMD model. The optimized model can quickly and effectively predict transient three-dimensional temperature and velocity fields in MSRs, with the maximum temperature error under 0.22 K and the relative velocity error within 5%. This result demonstrates that the proposed domain-decomposed DMD method significantly enhances the efficiency and stability of transient prediction, providing an effective method for the fast simulation of transient three-dimensional thermal-fluid behavior in MSRs.