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2021 Student Conference
April 8–10, 2021
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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.
A. Hoefer, G. Dirksen, J. Eyink, E.-M. Pauli
Nuclear Science and Engineering | Volume 166 | Number 3 | November 2010 | Pages 202-217
Technical Paper | dx.doi.org/10.13182/NSE10-09
Articles are hosted by Taylor and Francis Online.
In a level-2 probabilistic safety analysis (PSA), two types of uncertainty have to be taken into account: the uncertainty related to random variation (variability) and the uncertainty related to limited knowledge (ignorance). We present a consistent treatment of these two types of uncertainty within a Bayesian framework. This framework allows us to translate both types of uncertainty in the basic parameters into branch probability distributions of the PSA accident progression event tree (APET). This, in turn, results in probability distributions for the different release categories. A generic Monte Carlo algorithm for drawing random samples from branch probability distributions is presented, offering the possibility to directly include information in terms of empirical data. To provide an illustrative example, the developed methods are applied to a specific APET question, related to the temperature-induced rupture of the reactor coolant system in case of a high pressure accident scenario. Although this paper addresses level-2 PSA, the proposed framework is presented in a general form to be applicable to other PSA problems.