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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
Cihang Lu, Zeyun Wu
Nuclear Technology | Volume 208 | Number 1 | January 2022 | Pages 37-48
Technical Paper | doi.org/10.1080/00295450.2021.1874779
Articles are hosted by Taylor and Francis Online.
A one-dimensional (1-D) thermal stratification (TS) model was recently developed in our research group to predict the TS phenomenon in pool-type sodium-cooled fast reactors. This paper performs uncertainty quantification (UQ) of the 1-D TS model to evaluate its performance by considering the aleatoric uncertainties that existed in the model parameters and to identify the plausible sources of the epistemic uncertainties. The Latin hypercube sampling–Monte Carlo method (LHS-MC), which is elaborated with an example in this paper to facilitate its understanding and implementation, is used for the UQ process. The advantages of LHS-MC, including both better stability and better accuracy than the conventional random sampling–Monte Carlo method with fewer realizations, are demonstrated in this paper.
In total, 648 temperature measurements acquired from nine experimental transients performed in a university-scale Thermal Stratification Experimental Facility are used to evaluate the performance of the computational 1-D TS model. The UQ result shows that 77.5% of the experimental data can be predicted by the 1-D TS model within uncertainty ranges, which indicates the good performance of the computational model when the aleatoric uncertainties are correctly captured. The rest 22.5% of the experimental data are found located outside of the uncertainty ranges, which reveals the existence of the epistemic uncertainties caused by the lack of understanding of the TS phenomenon and defects in the 1-D model. The simple jet model currently employed by the 1-D TS model is thought to be one of the attributors to these defects.