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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.
Sung Goo Chi, Nam Zin Cho
Nuclear Technology | Volume 137 | Number 2 | February 2002 | Pages 127-138
Technical Paper | Nuclear Plany Operations and Control | doi.org/10.13182/NT00-31
Articles are hosted by Taylor and Francis Online.
A robust controller is designed by applying the H optimal control theory to the xenon control for the load-following operation of a nuclear reactor. The set of reactor model equations for controller design is a stiff system. This singularly perturbed system arises from the interaction of slow dynamics modes (iodine and xenon concentrations) and fast dynamics modes (neutron density, fuel and coolant temperatures). The singular perturbation technique is used to overcome this stiffness problem. The design specifications are incorporated by the frequency weights using the mixed-sensitivity problem approach. The robustness of H control is demonstrated by comparing it with linear quadratic Gaussian (LQG) control in the case of a measurement delay of the power measurement system.Since the gains and phase margins of H control are larger than those of LQG control, the H control is expected to provide excellent stability robustness and performance robustness against external disturbances and noises, model parameter variations, and modeling errors as well as hardware failures. It may also provide a practical design method because the design specifications can be easily implemented by the frequency weights.