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Fusion energy: Progress, partnerships, and the path to deployment
Over the past decade, fusion energy has moved decisively from scientific aspiration toward a credible pathway to a new energy technology. Thanks to long-term federal support, we have significantly advanced our fundamental understanding of plasma physics—the behavior of the superheated gases at the heart of fusion devices. This knowledge will enable the creation and control of fusion fuel under conditions required for future power plants. Our progress is exemplified by breakthroughs at the National Ignition Facility and the Joint European Torus.
Cihang Lu, Zeyun Wu
Nuclear Technology | Volume 208 | Number 10 | October 2022 | Pages 1577-1590
Technical Paper | doi.org/10.1080/00295450.2022.2049966
Articles are hosted by Taylor and Francis Online.
Equilibrium state generation for the pebble bed reactor (PBR) is challenging due to the need to simultaneously account for both pebble movement and changes in fuel compositions. Multigroup diffusion codes have been historically employed to generate the equilibrium state and perform conventional neutronics calculations for PBRs, while neutron cross-section generation has been challenging due to the double heterogeneity of PBRs. Thanks to the capability to treat the double heterogeneity naturally, continuous-energy Monte Carlo (MC) methods are more suitable for detailed PBR analysis, but at the cost of significantly higher computing power.
This paper presents a new Methodology to Efficiently Estimate the Equilibrium State of a PBR (MEEES-PBR) to generate equilibrium-state MC models for PBRs at lower computational expense. The MEEES-PBR is expected to contribute to the future development of PBR designs by accelerating the efforts in core designs and parametric studies. The theory of the MEEES-PBR is introduced in detail in this paper, and the procedure is demonstrated via an example application to the 165-MW(thermal) Xe-100 design. The computational cost and the accuracy of the MEEES-PBR are discussed to prove its viability.