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.
Massimo A. Larsen, Simon Bolding, Todd Palmer, Jim Morel
Nuclear Science and Engineering | Volume 200 | Number 3 | March 2026 | Pages 525-538
Research Article | doi.org/10.1080/00295639.2025.2495608
Articles are hosted by Taylor and Francis Online.
Residual Monte Carlo (RMC) methods have been previously used in neutron transport for monoenergetic and multigroup problems. In this paper, we implement an RMC algorithm for solving continuous energy problems with elastic scattering functions. We use a piecewise constant finite-element trial space to approximate the transport solution and build the residual representation. Because of the complexity of the scattering term, an analytic distribution cannot be computed for the residual; instead, we sample source particles directly from the scattering integrand using unnormalized importance sampling. We achieve exponentially convergent Monte Carlo (MC) with the use of an additional weight cancellation technique to reduce the magnitude of particle weights. We then demonstrate the algorithm on continuous energy problems and compare the results with standard MC simulations to demonstrate the increased efficiency of the RMC method.