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Conference Spotlight
Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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Hash Hashemian: Visionary leadership
As Dr. Hashem M. “Hash” Hashemian prepares to step into his term as President of the American Nuclear Society, he is clear that he wants to make the most of this unique moment.
A groundswell in public approval of nuclear is finding a home in growing governmental support that is backed by a tailwind of technological innovation. “Now is a good time to be in nuclear,” Hashemian said, as he explained the criticality of this moment and what he hoped to accomplish as president.
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.