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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
Emily H. Vu, Aaron J. Olson
Nuclear Science and Engineering | Volume 197 | Number 2 | February 2023 | Pages 212-232
Technical Paper | doi.org/10.1080/00295639.2022.2116378
Articles are hosted by Taylor and Francis Online.
Conditional Point Sampling (CoPS) is our newly proposed Monte Carlo method for transport in stochastic media that has been demonstrated to achieve highly accurate mean response results and to compute variance of the mean caused by random spatial mixing. The ability of CoPS to efficiently characterize the effects of random spatial mixing beyond the mean is hindered by the algorithm’s potentially unbounded computer memory footprint. Thus, in previous work, we established two limited-memory techniques for CoPS to improve required computer memory, i.e., recent memory (RM) CoPS and amnesia radius (AR) CoPS, the latter of which enables CoPS to tractably compute probability density functions (PDFs) of response. In this work, we create a limited-memory framework that allows CoPS to combine the advantages of limited-memory techniques and populate the framework with the two inaugural techniques of RM and AR. The proposed framework enables the user to control the computational performance of CoPS by making problem-specific trade-offs between accuracy, computer memory footprint, and characterization of response distributions based on input parameters. We present mean leakage results, material-dependent scalar flux, leakage PDFs, and computer memory footprint computed using this new framework. By selecting different input parameters in our proposed limited-memory framework, CoPS is demonstrated to roughly match the accuracy and computer memory footprint of the established approximate method Chord Length Sampling or to provide response distribution information comparable to the brute-force benchmark approach while improving the computer memory footprint compared to the original CoPS algorithm.