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Aerospace Nuclear Science & Technology
Organized to promote the advancement of knowledge in the use of nuclear science and technologies in the aerospace application. Specialized nuclear-based technologies and applications are needed to advance the state-of-the-art in aerospace design, engineering and operations to explore planetary bodies in our solar system and beyond, plus enhance the safety of air travel, especially high speed air travel. Areas of interest will include but are not limited to the creation of nuclear-based power and propulsion systems, multifunctional materials to protect humans and electronic components from atmospheric, space, and nuclear power system radiation, human factor strategies for the safety and reliable operation of nuclear power and propulsion plants by non-specialized personnel and more.
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ANS Student Conference 2025
April 3–5, 2025
Albuquerque, NM|The University of New Mexico
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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
Norway’s Halden reactor takes first step toward decommissioning
The government of Norway has granted the transfer of the Halden research reactor from the Institute for Energy Technology (IFE) to the state agency Norwegian Nuclear Decommissioning (NND). The 25-MWt Halden boiling water reactor operated from 1958 to 2018 and was used in the research of nuclear fuel, reactor internals, plant procedures and monitoring, and human factors.
Humberto E. Garcia, Richard B. Vilim
Nuclear Technology | Volume 141 | Number 1 | January 2003 | Pages 69-77
Technical Paper | Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies | doi.org/10.13182/NT03-A3351
Articles are hosted by Taylor and Francis Online.
Two basic approaches can be mentioned to model physical systems. One approach derives a model structure from the known physical laws. However, obtaining a model with the required fidelity may be difficult if the system is not well understood. A second approach is to employ a black-box structure to learn the implicit input-output relationships from measurements in which no particular attention is paid to modeling the underlying processes. A method that draws on the respective strengths of each of these two approaches is described. The technique integrates known first-principles knowledge derived from physical modeling with measured input-output mappings derived from neural processing to produce a computer model of a dynamical process. The technique is used to detect operational changes of mechanical equipment by statistically comparing, using a likelihood test, the predicted model output for the given measured input with the actual process output. Experimental results with a peristaltic pump are presented.