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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.
Zhichao Guo, Robert E. Uhrig
Nuclear Technology | Volume 99 | Number 1 | July 1992 | Pages 36-42
Technical Paper | Nuclear Reactor Safety | doi.org/10.13182/NT92-A34701
Articles are hosted by Taylor and Francis Online.
A hybrid artificial neural network is used to model the thermodynamic behavior of the Tennessee Valley Authority’s Sequoyah nuclear power plant using data for heat rate measurements acquired over a 1-yr period. The modeling process involves the use of a selforganizing network to rearrange the original data into several classes by clustering. Then, the centroids of these clusters are used as the training patterns for an artificial neural network that utilizes backpropagation training to adjust the weights on the connections between artificial neurons. This procedure greatly reduces the training time and reduces the system error. Comparison of the calculated heat rates with those predicted by the artificial neural network gives an error of <0.1%. A sensitivity analysis is then performed by taking the partial derivative of the heat rate with respect to each individual input to secure a sensitivity coefficient. These coefficients identified the input variables that were most important to improving the heat rate and efficiency. The methodology reported is an alternative to the conventional modeling procedures used in other heat rate monitoring systems. It has the advantage that the artificial neural network model is based on actual plant data that cover the dynamic range normally occurring over an annual cycle of operation, and it is not subject to linearization or empirical approximations. This process could be utilized by existing heat rate monitoring systems.