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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.
Michel Bloch, Daniel Dussarté, Jean-Louis Pierrey
Nuclear Technology | Volume 84 | Number 3 | March 1989 | Pages 282-284
Technical Paper | Probabilistic Safety Assessment and Risk Management / Nuclear Safety | doi.org/10.13182/NT89-A34211
Articles are hosted by Taylor and Francis Online.
Premature aging of the heat exchange tubes in steam generators due to stress corrosion may be a common cause of nonsimultaneous multiple ruptures, enhancing the risk associated with that accidental situation. Classical methods for probabilistic evaluation are not easily applicable to this type of problem. The component lifetime can be used directly as a primary random variable with a distribution width (mean value irrelevant) deduced from operational data or engineering judgment. The conditional probability to get one or more ruptures before a critical time following the occurrence of the first rupture can be obtained from the probability laws for the time intervals between the first and successive ruptures and can be used in accident sequence analyses. As an example, the conditional probability of the second rupture is approximately proportional to the critical time and is ∼10−4 for a lifetime distribution standard deviation of 15 000 h and a critical time of 1 h.