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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.
Ahmad Al Rashdan, Vivek Agarwal
Nuclear Technology | Volume 205 | Number 8 | August 2019 | Pages 1053-1061
Technical Paper – Special section on Big Data for Nuclear Power Plants | doi.org/10.1080/00295450.2019.1601469
Articles are hosted by Taylor and Francis Online.
The migration of paper-based work packages to an electronic version for the nuclear power industry results in opportunities for work optimization through data analytics and integration. This can only be achieved if the work package is broken into its data elements and stored in a structured data form. The contribution of this paper is the development of a set of guidelines that enables creating a data model from breaking the work package into its data elements. The data model can be used to create a common information model for work packages at nuclear power plants. The results presented and discussed in this paper highlight distinctive data model characteristics regarding the work element properties and associations; work package topology; properties cascade; elements and properties function; templates and instances; and steps flow. In total, 13 guidelines were identified as part of this work. The resulting benefits from the extracted data model are enabling step-level review of the work, reducing planning effort, and automating work package creation and formatting. In addition, coupling work process data with other data sources at the plant improves overall maintenance activity efficiency by enabling capabilities such as real-time schedule update and automatic allocation and release of work resources.