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May 31–June 3, 2026
Denver, CO|Sheraton Denver
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DOE awards ANS-backed workforce consortium $19.2M
The Department of Energy’s Office of Nuclear Energy recently awarded about $49.7 million to 10 university-led projects aiming to develop nuclear workforce training programs around the country.
DOE-NE issued its largest award, $19.2 million, to the newly formed Great Lakes Partnership to Enhance the Nuclear Workforce (GLP). This regional consortium, which is led by the University of Toledo and includes the American Nuclear Society, will use the funds to fill a variety of existing gaps in the nuclear workforce pipeline.
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.