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Conference Spotlight
Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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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Powering the future: How the DOE is fueling nuclear fuel cycle research and development
As global interest in nuclear energy surges, the United States must remain at the forefront of research and development to ensure national energy security, advance nuclear technologies, and promote international cooperation on safety and nonproliferation. A crucial step in achieving this is analyzing how funding and resources are allocated to better understand how to direct future research and development. The Department of Energy has spearheaded this effort by funding hundreds of research projects across the country through the Nuclear Energy University Program (NEUP). This initiative has empowered dozens of universities to collaborate toward a nuclear-friendly future.
Keith Humenik, Kenny C. Gross
Nuclear Science and Engineering | Volume 112 | Number 2 | October 1992 | Pages 127-135
Technical Paper | doi.org/10.13182/NSE92-A28409
Articles are hosted by Taylor and Francis Online.
Sequential probability ratio tests (SPRTs) are applied to the monitoring of nuclear power reactor signals. The theory of SPRTs applied to correlated data that have an unknown distribution is very incomplete. Unfortunately, a common problem regrading the application of sequential methods to reactor variables is that the variables are often contaminated with noise that is either non-Gaussian or serially correlated (or both). A Fourier series approximation can be used to remove much of the correlation in the data. This method is relatively simple to implement but has the desirable property of reducing correlation, thereby allowing the assumption of Gaussian, independent data to hold more readily. Delayed neutron signal data and reactor coolant pump data are analyzed. The theory has been validated by extensive testing with data from the Experimental Breeder Reactor II. The use of SPRT techniques as decision aids in two artificial intelligence-based expert systems for surveillance and diagnosis applications in nuclear reactors is also discussed.