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 Annual Conference
May 31–June 3, 2026
Denver, CO|Sheraton Denver
Latest Magazine Issues
May 2026
Jan 2026
Apr 2026
Latest Journal Issues
Nuclear Science and Engineering
June 2026
Nuclear Technology
April 2026
Fusion Science and Technology
Latest News
PJM queues a fusion project among 810 others
The breakdown by number of projects, share of megawatts, and generation types in PJM’s new interconnection cycle. (Source: PJM Interconnection)
On April 27, PJM Interconnection closed its first full interconnection cycle since 2022. Under a reformed application process, 811 developers submitted generation projects capable of generating 220 gigawatts of electricity. About 400 megawatts of that total share comes from Commonwealth Fusion Systems, which submitted an application for its ARC fusion power plant. This is a notable milestone for the industry: it is the first time a developer has requested to connect a commercial fusion power plant to a major grid.
Paul Cosgrove, John R. Tramm
Nuclear Science and Engineering | Volume 198 | Number 9 | September 2024 | Pages 1739-1758
Research Article | doi.org/10.1080/00295639.2023.2270618
Articles are hosted by Taylor and Francis Online.
The Random Ray Method (TRRM) is a recently developed approach to solving neutral particle transport problems based on the Method of Characteristics. While the method previously has been implemented only in closed-source or limited-functionality codes, this work describes its implementation in two open-source Monte Carlo codes: OpenMC and SCONE. The random ray implementations required small modifications to the existing Multigroup Monte Carlo (MGMC) solvers, offering a rare venue for redundant, fine-grained, “apples-to-apples” speed and accuracy comparisons between transport methods. To this end, TRRM and MGMC solvers are evaluated against each other using each code’s native capabilities on reactor eigenvalue problems with different degrees of energy discretization. On the C5G7 benchmark (featuring only seven energy groups), TRRM achieves a maximum pin power error comparable to or lower than that of MGMC for a given run time. On a problem with 69 energy groups, MGMC is found to scale more efficiently, obtaining a lower pin power error for a given run time. However, the defining difference between the two transport methods is found to be their vastly different uncertainty distributions. Specifically, TRRM is found to maintain similar levels of accuracy and uncertainty throughout the simulation domain whereas MGMC can exhibit orders-of-magnitude greater errors in areas of the problem that feature low neutron flux. For instance, TRRM provided an up to 373 times speed advantage compared with MGMC for computing the flux in low-flux regions in the moderator surrounding the C5G7 core.