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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.
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April 8–10, 2021
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Nuclear Science and Engineering
Fusion Science and Technology
Understanding the ITER Project in the context of global Progress on Fusion
(photo: ITER Project gangway assembly)
The promise of hydrogen fusion as a safe, environmentally friendly, and virtually unlimited source of energy has motivated scientists and engineers for decades. For the general public, the pace of fusion research and development may at times appear to be slow. But for those on the inside, who understand both the technological challenges involved and the transformative impact that fusion can bring to human society in terms of the security of the long-term world energy supply, the extended investment is well worth it.
Failure is not an option.
Haoyu Wang, Andrew Longman, J. Thomas Gruenwald, James Tusar, Richard Vilim
Nuclear Technology | Volume 205 | Number 8 | August 2019 | Pages 1003-1020
Technical Paper – Special section on Big Data for Nuclear Power Plants | dx.doi.org/10.1080/00295450.2019.1583957
Articles are hosted by Taylor and Francis Online.
Moisture carryover (MCO) is modeled in the General Electric Type-4 boiling water reactor (BWR) using machine-learning methods and data from operating plants. Understanding MCO and the conditions that give rise to an elevated value is important since excessive MCO can damage critical turbine components, can result in elevated dose levels to on-site personnel, and can interfere with late-cycle power management. The analysis of MCO takes into account simplifying reactor symmetries and important geometric dependencies. The plant data are taken from several reactors and were collected over multiple years and multiple fuel cycles. A brief description of the origin of MCO in U.S. BWR plants is given. A machine-learning model is constructed from the data using applicable algorithms and data-reduction techniques. Matching model complexity with available data is one of the more challenging machine-learning tasks. Too many features and too little data will lead to overfitting. The data for each fuel cycle included over 6876 original features, 9 for each fuel bundle. Two approaches are used to reduce the data set into a manageable number of features. The first was an engineering analysis that resulted in the selection of steam quality Q and steam liquid phase velocity VL as the main features driving MCO. Using a Q and a VL for each fuel bundle gives 1528 Q and a VL feature describing the reactor behavior. An analysis of different functional forms of these two variables led to the actual inputs to the neural network model. The second approach involved the use of statistical techniques such as Pearson’s correlation and k-means analysis. The identified groupings of bundles behaved similarly. Treating each grouping as a single feature further reduced the input variable set to a manageable number. A model selection criterion is proposed, and results are presented along with a discussion of related issues.