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The mission of the Decommissioning and Environmental Sciences (DES) Division is to promote the development and use of those skills and technologies associated with the use of nuclear energy and the optimal management and stewardship of the environment, sustainable development, decommissioning, remediation, reutilization, and long-term surveillance and maintenance of nuclear-related installations, and sites. The target audience for this effort is the membership of the Division, the Society, and the public at large.
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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.
Cyrille De Saint Jean, Gilles Noguere, Benoit Habert, Bertrand Iooss
Nuclear Science and Engineering | Volume 161 | Number 3 | March 2009 | Pages 363-370
Technical Paper | dx.doi.org/10.13182/NSE161-363
Articles are hosted by Taylor and Francis Online.
The evaluation of neutron cross sections in the low energy range (electron volt, mega-electron-volt) is based on formal nuclear models having different types of parameters. Some of them may be fitted to reproduce experimental datasets giving rise to an adjusted covariance matrix. In this paper, a Monte Carlo method is presented to properly consider the influence of the remaining parameters, having a priori uncertainties, on the fitted parameters covariances. This method is based on an exact mathematical description using conditional probabilities. To explain the key points of the methodology, an academic example of average parameters evaluation in the unresolved resonance range is presented using Hauser-Feshbach model calculations.
