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The human factor in licensing and operating the next generation of nuclear plants
As human factors specialists working at the intersection of human performance and nuclear operations, we are witnessing one of the nuclear sector’s most significant transitions in decades. The emergence of small modular reactors, microreactors, and other advanced designs is reshaping the industry’s landscape. Digital instrumentation and controls, passive safety systems, and increased automation are creating opportunities for greater safety margins and more flexible operation. These same features also fundamentally redefine what it means to “operate” a nuclear plant. Interactions among human roles, automation, and passive systems shape how people maintain awareness, exercise judgment, and intervene when necessary. These developments affect both operational realities and the regulatory foundations on which nuclear safety is built.
John Pevey, Ondřej Chvála, Sarah Davis, Vladimir Sobes, J. Wes Hines
Nuclear Technology | Volume 206 | Number 4 | April 2020 | Pages 609-619
Technical Paper | doi.org/10.1080/00295450.2019.1664198
Articles are hosted by Taylor and Francis Online.
This paper discusses the design of a fast spectrum subcritical assembly utilizing a genetic algorithm. The facility proposed in this paper would be a flexible platform for expanding the knowledge of fast spectrum neutron cross sections needed for next-generation fast reactor designs. The Fast Neutron Source (FNS) would be composed of both a fast and a thermal region to minimize the amount of uranium fuel and reduce overall material costs while maintaining flexibility for many potential fast neutron cross-section experiments. The FNS would be customizable and interchangeable down to 1 × 1 × 10-in.-volume sections. An optimal core design requires the adjustment of many factors to both reduce the cost and accurately reproduce the spectra of interest during an experiment. A genetic algorithm was developed to optimize this complex design problem while reducing design time and expert judgment. The genetic algorithm was able to vary multiple design factors in an unattended fashion from a random initial population of designs and arrived at a design comparable to an expertly designed assembly.