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2025 ANS Winter Conference & Expo
November 9–12, 2025
Washington, DC|Washington Hilton
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Senate EPW Committee to hold Nieh nomination hearing
Nieh
The Senate Environment and Public Works Committee will hold a nomination hearing Wednesday for Ho Nieh, President Donald Trump’s nominee to serve as commission at the Nuclear Regulatory Commission.
Trump nominated Nieh on July 30 to serve as NRC commissioner the remainder of a term that will expire June 30, 2029, as Nuclear NewsWire previously reported.
Nieh has been vice president of regulatory affairs at Southern Nuclear since 2021, though since June 2024 he has been at the Institute of Nuclear Power Operations as a loaned executive.
A return to the NRC: If confirmed by the Senate, Nieh would be returning to the NRC after three previous stints totaling nearly 20 years.
Christopher M. Perfetti, Bradley T. Rearden, William J. Marshall
Nuclear Science and Engineering | Volume 185 | Number 1 | January 2017 | Pages 139-158
Technical Paper | doi.org/10.13182/NSE16-54
Articles are hosted by Taylor and Francis Online.
This study focuses on understanding the phenomena in Monte Carlo simulations known as undersampling, in which Monte Carlo tally estimates may not encounter a sufficient number of particles during each generation to obtain unbiased tally estimates. Steady-state Monte Carlo simulations were performed using the KENO Monte Carlo tools within the SCALE code system for models of several burnup credit applications with varying degrees of spatial and isotopic complexities, and the incidence and impact of undersampling on eigenvalue and flux estimates were examined. Using an inadequate number of particle histories in each generation was found to produce a maximum bias of ~100 pcm in eigenvalue estimates and biases that exceeded 10% in fuel pin flux tally estimates. Having quantified the potential magnitude of undersampling biases in eigenvalue and flux tally estimates in these systems, this study then investigated whether Markov Chain Monte Carlo convergence metrics could be integrated into Monte Carlo simulations to predict the onset and magnitude of undersampling biases. Five potential metrics for identifying undersampling biases were implemented in the SCALE code system and evaluated for their ability to predict undersampling biases by comparing the test metric scores with the observed undersampling biases. Of the five convergence metrics that were investigated, three (the Heidelberger-Welch relative half-width, the Gelman-Rubin Řc diagnostic, and tally entropy) showed the potential to accurately predict the behavior of undersampling biases in the responses examined.