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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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The Sodium Reactor Experiment
In February 1957, construction was completed on the Sodium Reactor Experiment (SRE), a sodium-cooled, graphite-moderated reactor with an output of 20 MWt. The design of theSRE had begun three years earlier in 1954, and construction started in April 1955. On April 25, 1957, the reactor reached criticality, and the SRE operated until February 1964.
Jung Hwan Kim, Chul Min Kim, Yong Hee Lee, Man-Sung Yim
Nuclear Technology | Volume 207 | Number 11 | November 2021 | Pages 1753-1767
Regular Technical Paper | doi.org/10.1080/00295450.2020.1837583
Articles are hosted by Taylor and Francis Online.
The safe operation of a nuclear power plant (NPP) can be guaranteed through the team effort of operators in the main control room (MCR). Among the various features, peer checks, concurrent verification, independent verification, and communication reconfirmation are major contributors to effective operations in the MCR. In the digital MCR environment of advanced NPPs, there are potential emerging issues of concern related to these contributors resulting from the use of PC-soft controls for reactor operations. The objective of this study is to investigate the development of quantitative indicators for estimating the implicit intentions of reactor operators as a way to mitigate such concerns. The proposed quantitative indicators support peer checks and concurrent/independent verifications for diagnosing and preventing human errors through communication enhancement in a digital technology-based MCR. A machine learning–based algorithm was used to classify two implicit intentions of agreement and disagreement. The classification was based on electroencephalography data measured from human subjects while they performed mock operational tasks using soft controls. The mock operational tasks were based on using a Windows-based nuclear plant performance analyzer (Win-NPA). Statistical analysis was performed on the measured data to identify significant differences between the agreement and disagreement judgments by the operators. An average classification accuracy of 72% was achieved by using a support vector machine classifier for the Win-NPA task with a low number of features across the various Brodmann areas. The methodology proposed in this study may also serve to enhance communications in conventional MCRs for human error minimization.