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Division Spotlight
Materials Science & Technology
The objectives of MSTD are: promote the advancement of materials science in Nuclear Science Technology; support the multidisciplines which constitute it; encourage research by providing a forum for the presentation, exchange, and documentation of relevant information; promote the interaction and communication among its members; and recognize and reward its members for significant contributions to the field of materials science in nuclear technology.
Meeting Spotlight
International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering (M&C 2025)
April 27–30, 2025
Denver, CO|The Westin Denver Downtown
Standards Program
The Standards Committee is responsible for the development and maintenance of voluntary consensus standards that address the design, analysis, and operation of components, systems, and facilities related to the application of nuclear science and technology. Find out What’s New, check out the Standards Store, or Get Involved today!
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Nuclear Science and Engineering
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Nuclear Technology
Fusion Science and Technology
May 2025
Latest News
Argonne’s METL gears up to test more sodium fast reactor components
Argonne National Laboratory has successfully swapped out an aging cold trap in the sodium test loop called METL (Mechanisms Engineering Test Loop), the Department of Energy announced April 23. The upgrade is the first of its kind in the United States in more than 30 years, according to the DOE, and will help test components and operations for the sodium-cooled fast reactors being developed now.
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.