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Latest News
General Atomics announces breeding blanket test facility
General Atomics announced it is developing design concepts in collaboration with the Department of Energy for the Fusion Blanket Component Test Facility (BCTF), which will test full-scale breeding blankets.
“No one has tested a fusion blanket at this scale. While there are more research and development challenges ahead, a BCTF brings us closer to turning fusion from proven science into practical, sustainable power,” said Anantha Krishnan, senior vice president of the General Atomics Energy Group.
Justin Weinmeister, Casey J. Jesse, Prashant Jain, Brian J. Ade, Danny Schappel
Nuclear Science and Engineering | Volume 196 | Number 12 | December 2022 | Pages 1496-1516
Technical Paper | doi.org/10.1080/00295639.2022.2096999
Articles are hosted by Taylor and Francis Online.
Additive manufacturing (AM) methods are currently being explored for applications in nuclear reactors to make advanced reactors more efficient, safe, and reliable. The Transformational Challenge Reactor (TCR) program has explored AM for nuclear by designing a high-temperature gas reactor (HTGR) using an AM silicon carbide fuel form with uranium nitride–tristructural isotropic fuel. This work details the design process for the TCR fuel form’s coolant channels using computational fluid dynamics models with conjugate heat transfer. Additionally, this work discusses how these models were interfaced with other design teams, project milestones, and the agile design method used to mature the reactor design. The methodology deployed was able to create a channel design with lower maximum fuel temperatures and thermal stresses in the fuel form over traditional channel designs that can be manufactured subtractively. These results were achieved with only small manufacturing penalties. Results are discussed and presented on lessons learned for designing AM components for nuclear reactors. Finally, areas of opportunity are discussed for advanced design tools to further automate design activities and optimize reactors with fewer built-in assumptions.