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
Aug 2026
Jan 2026
2026
Latest Journal Issues
Nuclear Science and Engineering
October 2026
Nuclear Technology
September 2026
Fusion Science and Technology
August 2026
Latest News
Center for Used Fuel Research: Building confidence in storage and transport
Used nuclear fuel storage and transportation have reached a critical juncture.
Dozens of utilities need reliable data on how used nuclear fuel performs in dry storage casks and canisters to extend regulatory licenses at sites across the United States. Likewise, the Department of Energy expects to take ownership of the used nuclear fuel—termed “spent nuclear fuel” in the laws and regulations governing its stewardship—and transfer it to one or more federal staging facilities for management and disposition.
Meanwhile, dozens of reactor companies are testing prototypes of advanced reactors and advanced reactor fuels. Eventually, regulators and industry must also verify the safety and security of storage methods for these advanced fuel types.
To help address these challenges, the DOE established the Center for Used Fuel Research (CUFR) in January 2026 for work related to the long-term storage and transport of used nuclear fuel.
Navid Delgarm, Mahmoud Rostami Varnousfaaderani, Hamid Farrokhfal, Sajad Ardeshiri
Nuclear Technology | Volume 212 | Number 9 | September 2026 | Pages 2237-2274
Research Article | doi.org/10.1080/00295450.2025.2507976
Articles are hosted by Taylor and Francis Online.
This study presents a sophisticated thermoeconomic model, meticulously integrated with Pareto optimization for the marine nuclear propulsion system, with the Nuclear Ship Savannah serving as a reference benchmark. Given the constraints in the available design data for the Savannah marine nuclear propulsion (SMNP) system, innovative ideas are devised to precisely characterize its thermodynamic properties, ensuring a rigorous and reliable analysis. Following model validation, multi-objective particle swarm optimization is applied to the SMNP to optimize performance criteria, such as energy efficiency and total product exergy cost rate , to identify Pareto-optimal solutions and evaluate performance trade-offs. The optimized Savannah marine nuclear propulsion (OSMNP) is determined through the analytic network process within a multi-criteria decision-making framework. A thorough comparative analysis of the SMNP and OSMNP is then performed from the energy, exergy, and exergoeconomic perspectives, incorporating key system indicators, including energy efficiency and total product exergy cost rate , exergy efficiency , total propulsion power , and total capital cost rate . The results reveal that SMNP achieves and of 26.18% and 51.8%, respectively, with corresponding and of $249.07/h and $4616.8/h. In addition, reaches 15.09 MW. In contrast, OSMNP exhibits a remarkable improvement in , , and but a marginal rise in and . The and increase to 27.82% and 54.51%, representing enhancements of 1.64% and 2.71%, respectively. The also experiences a significant boost, reaching 15.93 MW—an increase of 0.84 MW. These performance gains are achieved with only a small rise in to $253.8/h (a minor increment of $4.73/h) and to $4990/h (an increase of $373.2/h). Moreover, the OSMNP demonstrates a significant reduction in the relative exergy cost difference of components, indicating a notable decrease in exergy destruction and inefficiencies compared to the SMNP. Finally, to assess the effectiveness of particle swarm optimization (PSO) in solving the SMNP problem, a comparative analysis is conducted against the firefly algorithm and genetic algorithm using the Wilcoxon rank-sum test. The comparative analysis demonstrates that PSO outperforms the other optimization algorithms. These findings support the development of sustainable, environmentally friendly marine propulsion technologies.
