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High-temperature plumbing and advanced reactors
The use of nuclear fission power and its role in impacting climate change is hotly debated. Fission advocates argue that short-term solutions would involve the rapid deployment of Gen III+ nuclear reactors, like Vogtle-3 and -4, while long-term climate change impact would rely on the creation and implementation of Gen IV reactors, “inherently safe” reactors that use passive laws of physics and chemistry rather than active controls such as valves and pumps to operate safely. While Gen IV reactors vary in many ways, one thing unites nearly all of them: the use of exotic, high-temperature coolants. These fluids, like molten salts and liquid metals, can enable reactor engineers to design much safer nuclear reactors—ultimately because the boiling point of each fluid is extremely high. Fluids that remain liquid over large temperature ranges can provide good heat transfer through many demanding conditions, all with minimal pressurization. Although the most apparent use for these fluids is advanced fission power, they have the potential to be applied to other power generation sources such as fusion, thermal storage, solar, or high-temperature process heat.1–3
Tomasz Skorek
Nuclear Technology | Volume 205 | Number 12 | December 2019 | Pages 1540-1553
Technical Paper | doi.org/10.1080/00295450.2019.1580532
Articles are hosted by Taylor and Francis Online.
The input uncertainties propagation methods are the most frequently applied statistical methods in uncertainty analyses. Among them, particularly popular are the methods based on Wilks’ formula. Numerous studies on uncertainty analyses show that the identification and quantification of input uncertainties is a major problem with uncertainty analyses. Among input uncertainties evaluation, the identification and quantification of physical model uncertainties in thermal-hydraulic codes appear to be particularly difficult.
This paper deals with this problem by proposing inherent model uncertainties quantification by code developers in the frame of code development and validation. The introduction of the extended code validation would not only contribute to potential uncertainty analyses, solving to a large degree the problem of model uncertainties quantification, but also contribute to code validation, and as a consequence, improve the safety issues. A not-negligible factor is also better management of the resources. Instead of uncertainty quantification repeatedly performed by each user, the quantification could be performed once and, in addition, by experts having the required know-how.
Introducing this new standard in code validation would require additional effort from the code developers but integral quantification of the model uncertainties would be profitable also for code development. In fact, by code development, in particular if the model is own development of the team, such an accuracy (or uncertainty) evaluation is usually performed. The additional effort, in this case, would be to describe the present information in the form of probability distribution functions or at least in the form of ranges.