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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.
Oleg Roderick, Mihai Anitescu, Paul Fischer
Nuclear Science and Engineering | Volume 164 | Number 2 | February 2010 | Pages 122-139
Technical Paper | dx.doi.org/10.13182/NSE08-79
Articles are hosted by Taylor and Francis Online.
In this work we describe a polynomial regression approach that uses derivative information for analyzing the performance of a complex system that is described by a mathematical model depending on several stochastic parameters.We construct a surrogate model as a goal-oriented projection onto an incomplete space of polynomials; find coordinates of the projection by regression; and use derivative information to significantly reduce the number of the sample points required to obtain a good model. The simplified model can be used as a control variate to significantly reduce the sample variance of the estimate of the goal.For our test model, we take a steady-state description of heat distribution in the core of the nuclear reactor core, and as our goal we take the maximum centerline temperature in a fuel pin. For this case, the resulting surrogate model is substantially more computationally efficient than random sampling or approaches that do not use derivative information, and it has greater precision than linear models.