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Conference Spotlight
Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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Ho Nieh nominated to the NRC
Nieh
President Trump recently nominated Ho Nieh for the role of commissioner in the Nuclear Regulatory Commission through the remainder of a term that will expire June 30, 2029.
Nieh has been the vice president of regulatory affairs at Southern Nuclear since 2021, though he is currently working as a loaned executive at the Institute of Nuclear Power Operations, where he has been for more than a year.
Nieh’s experience: Nieh started his career at the Knolls Atomic Power Laboratory, where he worked primarily as a nuclear plant engineer and contributed as a civilian instructor in the U.S. Navy’s Nuclear Power Program.
From there, he joined the NRC in 1997 as a project engineer. In more than 19 years of service at the organization, he served in a variety of key leadership roles, including division director of Reactor Projects, division director of Inspection and Regional Support, and director of the Office of Nuclear Reactor Regulation.
Zhanpeng Huang, Yunki Jo, Qingming He, Liangzhi Cao, Hongchun Wu, Deokjung Lee
Nuclear Science and Engineering | Volume 199 | Number 9 | September 2025 | Pages 1391-1405
Research Article | doi.org/10.1080/00295639.2024.2438570
Articles are hosted by Taylor and Francis Online.
This study introduces a novel DXTRAN-based weight window generator (DWWG) for variance reduction in Monte Carlo (MC) radiation shielding calculations implemented in the MCS code developed at Ulsan National Institute of Science and Technology. DWWG eliminates the iterative generation required by traditional weight window generators (WWGs) while ensuring target region scoring through the embedded DXTRAN. By incorporating virtual tracks of DXTRAN into the importance estimation, DWWG produces high-quality weight windows. Performance verification of two radiation shielding problems demonstrated that weight windows generated by DWWG, when combined with direct simulation or DXTRAN, yielded increased figures of merit and reduced relative standard deviations. These results establish DWWG as an effective WWG for variance reduction in MC simulations.