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NC State celebrates 70 years of nuclear engineering education
An early picture of the research reactor building on the North Carolina State University campus. The Department of Nuclear Engineering is celebrating the 70th anniversary of its nuclear engineering curriculum in 2020–2021. Photo: North Carolina State University
The Department of Nuclear Engineering at North Carolina State University has spent the 2020–2021 academic year celebrating the 70th anniversary of its becoming the first U.S. university to establish a nuclear engineering curriculum. It started in 1950, when Clifford Beck, then of Oak Ridge, Tenn., obtained support from NC State’s dean of engineering, Harold Lampe, to build the nation’s first university nuclear reactor and, in conjunction, establish an educational curriculum dedicated to nuclear engineering.
The department, host to the 2021 ANS Virtual Student Conference, scheduled for April 8–10, now features 23 tenure/tenure-track faculty and three research faculty members. “What a journey for the first nuclear engineering curriculum in the nation,” said Kostadin Ivanov, professor and department head.
Hyung Jin Shim, Chang Hyo Kim
Nuclear Science and Engineering | Volume 162 | Number 1 | May 2009 | Pages 98-108
Technical Paper | dx.doi.org/10.13182/NSE09-2
Articles are hosted by Taylor and Francis Online.
The sample variance of a tally in Monte Carlo eigenvalue calculations is biased because of an intercycle correlation between the fission source distributions (FSDs). How to estimate the variance bias or equivalently how to calculate the real variance has been an interesting subject of study. This paper proposes a new method to estimate the real variance based on an intercycle covariance of the FSDs that can be derived from the cycle-by-cycle stochastic error propagation model. The proposed method enables one to calculate every intercycle covariance of a tally accurately, regardless of the number of active cycles. Therefore, the method can be applied satisfactorily even to problems with the dominance ratio (DR) close to 1. The accuracy of the new method is examined for small- and medium-sized pressurized water reactor core problems and a fuel storage facility problem exhibiting a slow source convergence. It is shown that the new method is capable of predicting the variance bias strikingly better than the existing methods, especially for problems with high DRs.