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Materials Science & Technology
The objectives of MSTD are: promote the advancement of materials science in Nuclear Science Technology; support the multidisciplines which constitute it; encourage research by providing a forum for the presentation, exchange, and documentation of relevant information; promote the interaction and communication among its members; and recognize and reward its members for significant contributions to the field of materials science in nuclear technology.
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2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
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Nicholas Tsoulfanidis—ANS member since 1969
We welcome ANS members who have careered in the community to submit their own Nuclear Legacy stories, so that the personal history of nuclear power can be captured. For information on submitting your stories, contact nucnews@ans.org.
As an undergraduate I studied physics at the University of Athens. I entered the university in 1955 after successfully passing a national exam (came up fourth in a field of about 700 candidates). Upon graduation and finishing my mandatory two-year military service, the plan was to teach physics either in a public high school or as a tutor for a private for-profit institution, preparing high school students for the national exam.
Inhyung Kim, HyeonTae Kim, Yonghee Kim
Nuclear Science and Engineering | Volume 194 | Number 1 | January 2020 | Pages 14-31
Technical Paper | doi.org/10.1080/00295639.2019.1654815
Articles are hosted by Taylor and Francis Online.
This paper presents a systematic way to truncate the high-fidelity Monte Carlo (MC) solution to reduce the computational cost without compromising the essential reliability of the solution. Based on the fine-mesh finite difference (FMFD) acceleration for the MC analysis, the deterministic truncation of the Monte Carlo (DTMC) solution method is developed and investigated for a systematic approximation to the MC solution of the reactor eigenvalue problem. This deterministic solution is used for the acceleration of the MC simulation as well as the solution prediction itself. The concept, motivations, and challenges of the DTMC method are described in detail, and theoretical backgrounds of the FMFD method are discussed. In addition, an unbiased ratio estimator for more accurate FMFD parameter generation and a modified particle ramp-up method for the determination of optimal generation size in the MC simulation are also introduced and explained. Both the C5G7 benchmark and a small modular reactor (SMR) core are analyzed to characterize the numerical performance of the DTMC method in this work. Convergence behavior of the fission source distribution is examined, and reactor parameters such as the multiplication factor and three-dimensional pin power distribution are estimated and compared to the reference solution. The stochastic features of the DTMC solutions are also discussed in terms of the apparent and real standard deviations. For the pin power distribution, the root-mean-square error and relative error for the reactor core are also evaluated and compared. The computing time and figure of merit are compared for each method.