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Chicago, IL|Chicago Marriott Downtown
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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
Zeyun Wu, Jingang Liang, Xingjie Peng, Hany S. Abdel-Khalik
Nuclear Technology | Volume 205 | Number 7 | July 2019 | Pages 912-927
Regular Technical Paper | doi.org/10.1080/00295450.2018.1556062
Articles are hosted by Taylor and Francis Online.
This paper extends the applicability of the generalized perturbation theory (GPT)–free methodology, earlier developed for deterministic models, to Monte Carlo stochastic models. The objective of the GPT-free method is to calculate nuclear data sensitivity coefficients for generalized responses without solving the GPT response-specific inhomogeneous adjoint eigenvalue problem. The GPT-free methodology requires the capability to generate the eigenvalue sensitivity coefficients. This capability is readily available in several of the state-of-the-art Monte Carlo codes. The eigenvalue sensitivity coefficients are sampled using a statistical approach to construct a subspace of small dimension that is subsequently sampled for sensitivity information using a forward sensitivity analysis. A boiling water reactor assembly model is developed using the Oak Ridge National Laboratory Monte Carlo code KENO to demonstrate the application of the GPT-free methodology in Monte Carlo models. The response variations estimated by the GPT-free agree with the exact variations calculated by direct forward perturbations. The GPT-free method is also implemented in OpenMC and tested with the Godiva model to show its capability and feasibility in the estimation of the energy-dependent sensitivity coefficients for generalized responses in Monte Carlo models. The sensitivity results are compared against the ones acquired by the standard GPT-based methodologies. A higher order of accuracy in the sensitivity estimation is observed in the GPT-free method.