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Mathematics & Computation
Division members promote the advancement of mathematical and computational methods for solving problems arising in all disciplines encompassed by the Society. They place particular emphasis on numerical techniques for efficient computer applications to aid in the dissemination, integration, and proper use of computer codes, including preparation of computational benchmark and development of standards for computing practices, and to encourage the development on new computer codes and broaden their use.
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Utility Working Conference and Vendor Technology Expo (UWC 2024)
August 4–7, 2024
Marco Island, FL|JW Marriott Marco Island
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
Oklo completes end-to-end demonstration of advanced fuel recycling
Oklo Inc. has announced that it has completed the first end-to-end demonstration of its advanced fuel recycling process as part of an ongoing $5 million project in collaboration with Argonne and Idaho National Laboratories. Oklo’s goal: scaling up its fuel recycling capabilities to deploy a commercial-scale recycling facility that would increase advanced reactor fuel supplies and enhance fuel cost effectiveness for its planned sodium fast reactors.
Yair bartal, Jie Lin, Robert E. Uhrig
Nuclear Technology | Volume 110 | Number 3 | June 1995 | Pages 436-449
Technical Paper | Actinide Burning and Transmutation Special / Reactor Control | doi.org/10.13182/NT95-A35112
Articles are hosted by Taylor and Francis Online.
A nuclear power plant’s (NPP’s) status is usually monitored by a human operator. Any classifier system used to enhance the operators capability to diagnose a safety-critical system like an NPP should classify a novel transient as “don’t-know” if it is not contained within its accumulated knowledge base. In particular, the classifier needs some kind of proximity measure between the new data and its training set. Artificial neural networks have been proposed as NPP classifiers, the most popular ones being the multilayered perceptron (MLP) type. However, MLPs do not have a proximity measure, while learning vector quantization, probabilistic neural networks (PNNs), and some others do. This proximity measure may also serve as an explanation to the classifier’s decision in the way that case-based-reasoning expert systems do. The capability of a PNN network as a classifier is demonstrated using simulator data for the three-loop 436-MW(electric) Westinghouse San Onofre unit I pressurized water reactor. A transient’s classification history is used in an “evidence accumulation” technique to enhance a classifier’s accuracy as well as its consistency.