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Materials in Nuclear Energy Systems (MiNES 2023)
December 10–14, 2023
New Orleans, LA|New Orleans Marriott
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Fusion Science and Technology
Framatome and USNC team up to produce TRISO fuel at Framatome facility
Framatome Inc. and Ultra Safe Nuclear Corp. (USNC) signed an agreement on November 28 at the World Nuclear Exhibition in Paris, France, establishing a joint venture to manufacture nuclear fuel for USNC’s gas-cooled microreactor and other advanced reactor designs. Working together, the companies plan to produce commercial quantities of TRISO fuel particles and USNC’s proprietary Fully Ceramic Microencapsulated (FCM) fuel, which contains TRISO fuel particles within a ceramic fuel pellet.
P. Rodriguez-Fernandez, A. E. White, A. J. Creely, M. J. Greenwald, N. T. Howard, F. Sciortino, J. C. Wright
Fusion Science and Technology | Volume 74 | Number 1 | July-August 2018 | Pages 65-76
Technical Paper | doi.org/10.1080/15361055.2017.1396166
Articles are hosted by Taylor and Francis Online.
Understanding transport in magnetically confined plasmas is critical for developing predictive models for future devices such as ITER. Thanks to recent progress in simulation and theory, along with enhanced computational power and better diagnostic systems, direct and quantitative comparisons between experimental results and models is possible. However, validating transport models using additional constraints and accounting for experimental uncertainties still remains a formidable task. In this work, a new optimization framework is developed to address the issue of constrained validation of transport models. The Validation via Iterative Training of Active Learning Surrogates (VITALS) framework exploits surrogate-based strategies using Gaussian processes and sequential parameter updates to achieve the combination of plasma parameters that matches experimental transport measurements within diagnostic error bars. VITALS is successfully implemented to study L-mode plasmas in the Alcator C-Mod tokamak, and for the first time, additional measurable quantities, such as incremental diffusivity and fluctuation levels, are used during the validation process of the quasi-linear transport models TGLF-SAT1 and TGLF-SAT0. First results indicate that these machine-learning algorithms are very suitable and adaptable as a self-consistent, fast, and comprehensive validation methodology for plasma transport codes.