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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.
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
ENEC inks deal with Kazatomprom, MOUs with TerraPower, GEH
On the margins of the COP28 climate conference in Dubai, UAE, this week, Barakah nuclear plant owner Emirates Nuclear Energy Corporation (ENEC) signed its first commercial uranium fuel supply contract with Kazatomprom, in addition to memorandums of understanding with two U.S.-based advanced reactor developers—TerraPower and GE Hitachi Nuclear Energy (GEH).
Cristina Rea, Robert S. Granetz
Fusion Science and Technology | Volume 74 | Number 1 | July-August 2018 | Pages 89-100
Technical Paper | doi.org/10.1080/15361055.2017.1407206
Articles are hosted by Taylor and Francis Online.
Using data-driven methodology, we exploit the time series of relevant plasma parameters for a large set of disrupted and non-disrupted discharges from the DIII-D tokamak with the objective of developing a disruption classification algorithm. We focus on a subset of disruption predictors, most of which are dimensionless and/or machine-independent parameters such as the plasma internal inductance and the Greenwald density fraction , coming from both plasma diagnostics and equilibrium reconstructions. The utilization of dimensionless indicators will facilitate a more direct comparison between different tokamak devices.
In order to eventually develop a robust disruption warning algorithm, we leverage Machine Learning techniques, and in particular, we choose the Random Forests algorithm to explore the DIII-D database. We show the results coming from both binary (disrupted/non-disrupted) and multiclass classification problems. In the latter, the time dependency is introduced through the definition of class labels on the basis of the elapsed time before the disruption (i.e., ‘far from a disruption’, ‘within 350 ms of disruption’, etc.). Depending on the formulation of the problem, overall disruption prediction accuracy up to 90% is demonstrated, approaching 97% when identifying a stable and a disruptive phase for disrupted discharges. The performances of the different Random Forest classifiers are discussed in terms of accuracy, by showing the percentages of successfully detected samples, together with the false positive and false negative rates.