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Isotopes & Radiation
Members are devoted to applying nuclear science and engineering technologies involving isotopes, radiation applications, and associated equipment in scientific research, development, and industrial processes. Their interests lie primarily in education, industrial uses, biology, medicine, and health physics. Division committees include Analytical Applications of Isotopes and Radiation, Biology and Medicine, Radiation Applications, Radiation Sources and Detection, and Thermal Power Sources.
2021 ANS Winter Meeting and Technology Expo
November 30–December 3, 2021
Washington, DC|Washington Hilton
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Nuclear Science and Engineering
Fusion Science and Technology
Neutron noise monitoring during plant operation expedites flexure replacement at Salem-1
The nuclear industry has historically relied on intermittent ultrasonic test and visual inspections of pressurized water reactor components to identify and manage degradation. While this reactive approach has proven to be effective, imagine a scenario in which the degradation could propagate throughout the reactor internals, making a more proactive measure necessary to avoid a major enterprise risk to the plant. Could a utility identify the onset of degradation within the reactor internals during plant operation? If so, could a repair be developed prior to the next refueling outage to prevent additional, cascading degradation? That is exactly the situation that Public Service Enterprise Group (PSEG) and Westinghouse engineers were able to navigate over the course of the 2019–2020 operating cycle at Salem Unit 1, resulting in a tremendous success for the plant and a historic landmark in the nuclear industry, while earning the team a 2021 Nuclear Energy Institute Top Innovative Practice (TIP) award.
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 | dx.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.