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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.
Brian A. Lockwood, Mihai Anitescu
Nuclear Science and Engineering | Volume 170 | Number 2 | February 2012 | Pages 168-195
Technical Paper | dx.doi.org/10.13182/NSE10-86
Articles are hosted by Taylor and Francis Online.
In this work, we investigate the issue of providing a statistical model for the response of a computer model-described nuclear engineering system, for use in uncertainty propagation. The motivation behind our approach is the need for providing an uncertainty assessment even in the circumstances where only a few samples are available. Building on our recent work in using a regression approach with derivative information for approximating the system response, we investigate the ability of a universal gradient-enhanced Kriging model to provide a means for inexpensive uncertainty quantification. The universal Kriging model can be viewed as a hybrid of polynomial regression and Gaussian process regression. For this model, the mean behavior of the surrogate is determined by a polynomial regression, and deviations from this mean are represented as a Gaussian process. Tests with explicit functions and nuclear engineering models show that the universal gradient-enhanced Kriging model provides a more accurate surrogate model than either regression or ordinary Kriging models. In addition, we investigate the ability of the Kriging model to provide error predictions and bounds for regression models.