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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.
E. Dumonteil, T. Courau
Nuclear Technology | Volume 172 | Number 2 | November 2010 | Pages 120-131
Technical Paper | Reactor Safety | doi.org/10.13182/NT10-A10899
Articles are hosted by Taylor and Francis Online.
Typical dimensions of large neutronic systems are often two orders of magnitude greater than the mean free path of the neutrons. Such high dominance ratio systems represent a particularly challenging issue when performing Monte Carlo criticality simulations. As a matter of fact, these simulations are contaminated by a cycle-to-cycle correlation that strongly slows down the flux convergence. In this paper, we will first discuss the link between the dominance ratio and the cycle-to-cycle correlations that are responsible for the poor flux convergence. Then, we will present a new and original technique to assess the dominance ratio of a given Monte Carlo simulation. It consists of fitting the relaxation process of the neutron field after an initial excitation from a fission source with a Dirac delta function shape. Having showed that these flux convergence issues are dominance ratio driven, we will then propose the use of an "independent replicas" approach to deal with the underprediction bias in statistics. The different theoretical points presented in this paper will be verified on a pin cell test case simulated with the Monte Carlo code TRIPOLI4. Additional results based on a three-dimensional pressurized water reactor core calculation are provided to confirm the reliability of the fitting technique described.