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Conference Spotlight
2025 ANS Winter Conference & Expo
November 9–12, 2025
Washington, DC|Washington Hilton
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October 2025
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DOE’s latest fusion energy road map aims to bridge known gaps
The Department of Energy introduced a Fusion Science & Technology (S&T) Roadmap on October 16 as a national “Build–Innovate–Grow” strategy to develop and commercialize fusion energy by the mid-2030s by aligning public investment and private innovation. Hailed by Darío Gil, the DOE’s new undersecretary for science, as bringing “unprecedented coordination across America's fusion enterprise” and advancing President Trump’s January 2025 executive order, on “Unleashing American Energy,” the road map echoes plans issued by the DOE’s Office of Fusion Energy Sciences (FES) in 2023 and 2024, with a new emphasis on the convergence of AI and fusion.
The road map release coincided with other fusion energy events held this week in Washington, D.C., and beyond.
Herschel P. Smith, John C. Wagner
Nuclear Science and Engineering | Volume 149 | Number 1 | January 2005 | Pages 23-37
Technical Paper | doi.org/10.13182/NSE05-A2474
Articles are hosted by Taylor and Francis Online.
Certain reactor transients cause a reduction in moderator temperature and, hence, increased attenuation of neutrons and decreased response of excore detectors. This decreased detector response is of concern because of the credit assumed for detector-initiated reactor trip to terminate the transient. Explicit modeling of this phenomenon presents the analyst with a difficult problem because of the dense and optically thick neutron absorption media, given the constraint that precise response characteristics must be known in order to account for this phenomenon. The solution in this study was judged to be the use of Monte Carlo techniques coupled with robust variance reduction to accelerate problem convergence. A fresh discussion on the motivation for variance reduction is included, followed by separate accounts of manual and automated applications of variance reduction techniques. Finally, the results of both manual and automated variance reduction techniques are presented and compared.