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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.
Moosung Jae, Antony D. Milici, William E. Kastenberg, George E. Apostolakis
Nuclear Technology | Volume 104 | Number 1 | October 1993 | Pages 13-36
Technical Paper | Nuclear Reactor Safety | doi.org/10.13182/NT93-A34867
Articles are hosted by Taylor and Francis Online.
A framework for assessing severe accident management strategies is presented using a new analytical tool, namely, influence diagrams. This framework includes multiple and sequential decisions, sensitivity analysis, and uncertainty propagation, and is applied to a proposed set of strategies for a pressurized water reactor station blackout sequence. The influence diagram associated with these strategies is constructed and evaluated. Each decision variable, represented by a node in the influence diagram, has an uncertainty distribution associated with it. Using the mean value of these distributions, a best estimate assessment is performed, and each strategy is ranked with respect to the conditional frequency of early containment failure (ECF). For the preferred alternative, the sensitivity of the results to values of the input variables is investigated. The sensitivity of the ranking itself is then considered. The distributions of the uncertain variables are also propagated through the influence diagram to rank the alternatives with respect to the uncertainty associated with the calculated conditional frequency of ECF. Finally, the sensitivity of the variance of the output distribution, given the preferred decision alternative, to the uncertainty of the input variables is investigated.