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60 Years of U: Perspectives on resources, demand, and the evolving role of nuclear energy
Recent years have seen growing global interest in nuclear energy and rising confidence in the sector. For the first time since the early 2000s, there is renewed optimism about the industry’s future. This change is driven by several major factors: geopolitical developments that highlight the need for secure energy supplies, a stronger focus on resilient energy systems, national commitments to decarbonization, and rising demand for clean and reliable electricity.
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.