ANS is committed to advancing, fostering, and promoting the development and application of nuclear sciences and technologies to benefit society.
Explore the many uses for nuclear science and its impact on energy, the environment, healthcare, food, and more.
Explore membership for yourself or for your organization.
Conference Spotlight
2026 ANS Annual Conference
May 31–June 3, 2026
Denver, CO|Sheraton Denver
Latest Magazine Issues
Feb 2026
Jul 2025
Latest Journal Issues
Nuclear Science and Engineering
February 2026
Nuclear Technology
January 2026
Fusion Science and Technology
Latest News
INL’s Teton supercomputer open for business
Idaho National Laboratory has brought its newest high‑performance supercomputer, named Teton, online and made it available to users through the Department of Energy’s Nuclear Science User Facilities program. The system, now the flagship machine in the lab’s Collaborative Computing Center, quadruples INL’s total computing capacity and enters service as the 85th fastest supercomputer in the world.
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.