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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.
Joonhong Ahn, Myeongguk Cheon
Nuclear Technology | Volume 156 | Number 3 | December 2006 | Pages 303-319
Technical Paper | Radioactive Waste Management and Disposal | doi.org/10.13182/NT06-A3793
Articles are hosted by Taylor and Francis Online.
A linear programming approach has been developed to determine maximum mass loading of radionuclides in vitrified high-level waste (HLW). Linear approximation for the centerline temperature of vertically stacked cylindrical HLW canisters has been developed by assuming constant heat flux from a canister, steady-state heat transfer, natural convection, and by neglecting radiation effects. With the linear formula for the centerline temperature, it has been demonstrated that maximum radionuclide mass loading can be determined by the linear programming model conservatively. A numerical result for vitrification of HLW from PUREX reprocessing of pressurized water reactor spent fuel indicates that the maximum temperature constraint is one of the essential constraints in determining the feasible solution space for optimization if the heat emission from the waste is in a certain range (between 11.2 and 24.5 W/kg in this example).The linear programming model can be utilized to link various fuel cycle models and repository performance models in a consistent and quantitative manner.