ANS is committed to advancing, fostering, and promoting the development and application of nuclear sciences and technologies to benefit society.
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Division Spotlight
Operations & Power
Members focus on the dissemination of knowledge and information in the area of power reactors with particular application to the production of electric power and process heat. The division sponsors meetings on the coverage of applied nuclear science and engineering as related to power plants, non-power reactors, and other nuclear facilities. It encourages and assists with the dissemination of knowledge pertinent to the safe and efficient operation of nuclear facilities through professional staff development, information exchange, and supporting the generation of viable solutions to current issues.
Meeting Spotlight
2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
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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Latest News
Commercial nuclear innovation "new space" age
In early 2006, a start-up company launched a small rocket from a tiny island in the Pacific. It exploded, showering the island with debris. A year later, a second launch attempt sent a rocket to space but failed to make orbit, burning up in the atmosphere. Another year brought a third attempt—and a third failure. The following month, in September 2008, the company used the last of its funds to launch a fourth rocket. It reached orbit, making history as the first privately funded liquid-fueled rocket to do so.
Brandon Rasmussen, J. Wesley Hines, Robert E. Uhrig
Nuclear Technology | Volume 143 | Number 2 | August 2003 | Pages 217-226
Technical Paper | Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies | doi.org/10.13182/NT03-A3411
Articles are hosted by Taylor and Francis Online.
This work presents an empirical modeling approach combining a bilinear modeling technique, partial least squares, with the universal function approximation abilities of single hidden layer nonlinear artificial neural networks. This approach, referred to as neural network partial least squares (NNPLS), is compared to the common autoassociative artificial neural network. The NNPLS model is embedded into a graphical user interface and implemented at the Electrical Power Research Institute's Instrumentation and Control Center located at Tennessee Valley Authority's Kingston fossil power plant. Results are presented for 51 process signals with an average absolute estimation error of ~1.7% of the mean value, and sample drift detection performances are shown.