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 ANS Winter Conference & Expo
November 15–18, 2026
Phoenix, AZ|Arizona Grand Resort & Spa
Latest Magazine Issues
Aug 2026
Jan 2026
2026
Latest Journal Issues
Nuclear Science and Engineering
October 2026
Nuclear Technology
Fusion Science and Technology
August 2026
Latest News
Fuel loading process begins at Palisades
The Palisades nuclear power plant has drawn closer to restart, as plant staff began the process of loading fuel into the reactor vessel on Sunday morning.
The commencement of fuel loading places the Covert, Mich., facility in Mode 6—or the refueling stage—under the plant’s technical specifications, plant owner and operator Holtec International said in a news release. The Palisades reactor core consists of 204 fuel assemblies that include new fuel and partially used fuel from the plant’s most recent operating cycles. According to Holtec, the fuel loading is being conducted in accordance with plant procedures and technical specifications.
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.