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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.
J. M. O. Pinto, P. F. Frutuoso E Melo, P. L. C. Saldanha
Nuclear Technology | Volume 188 | Number 1 | October 2014 | Pages 20-33
Technical Paper | Fission Reactors | doi.org/10.13182/NT13-48
Articles are hosted by Taylor and Francis Online.
A methodology comprising Dynamic Flowgraph Methodology (DFM) and A Technique for Human Error Analysis (ATHEANA) is applied to a digital control system proposed for the pressurizer of current pressurized water reactor plants. The methodology consists of modeling this control system and its interactions with the controlled process and operator through an integrated DFM/ATHEANA approach. The results were complemented by the opinions of experts in conjunction with fuzzy theory. In terms of human reliability, DFM, along with ATHEANA, can model equipment failure modes, operator errors (omission/commission), and human factors that, combined with plant conditions, influence human performance. The results show that the methodology provides an efficient fault analysis of digital systems identifying all possible interactions among components. Through prime implicants, the methodology shows the event combinations that lead to system failure. Quantitative results obtained are in agreement with literature data, with a few percentage value discrepancies.