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Division Spotlight
Accelerator Applications
The division was organized to promote the advancement of knowledge of the use of particle accelerator technologies for nuclear and other applications. It focuses on production of neutrons and other particles, utilization of these particles for scientific or industrial purposes, such as the production or destruction of radionuclides significant to energy, medicine, defense or other endeavors, as well as imaging and diagnostics.
Meeting Spotlight
2021 Student Conference
April 8–10, 2021
Virtual Meeting
Standards Program
The Standards Committee is responsible for the development and maintenance of voluntary consensus standards that address the design, analysis, and operation of components, systems, and facilities related to the application of nuclear science and technology. Find out What’s New, check out the Standards Store, or Get Involved today!
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Nuclear Science and Engineering
March 2021
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February 2021
Fusion Science and Technology
January 2021
Latest News
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.
Andreas Ikonomopoulos, Miltiadis Alamaniotis, Stylianos Chatzidakis, Lefteri H. Tsoukalas
Nuclear Technology | Volume 182 | Number 1 | April 2013 | Pages 1-12
Technical Paper | Fission Reactors | dx.doi.org/10.13182/NT13-A15821
Articles are hosted by Taylor and Francis Online.
A novel machine learning approach for nuclear power plant modeling and state identification is presented together with its test results using data from the Loss-of-Fluid Test experimental facility. The approach exploits Gaussian processes whose principal function is to tackle the temporal problem of forecasting the actual system state in the varying environment of a nuclear reactor facility that undergoes successive overcooling transients. The approach fuses independent Gaussian process expert predictions to provide a single recommendation to the plant operators in a form that is suitable to appear on a decision support system screen. A variety of test cases are developed to explore the validity and relevance of Gaussian processes. The proposed implementation is examined with various predictor variables under different conditions, and the results obtained are in accordance with model expectations.