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Accelerator Applications
The division was organized to promote the advancement of knowledge of the use of particle accelerator technologies for nuclear and other applications. It focuses on production of neutrons and other particles, utilization of these particles for scientific or industrial purposes, such as the production or destruction of radionuclides significant to energy, medicine, defense or other endeavors, as well as imaging and diagnostics.
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2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
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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
Vogtle-4 enters commercial operation
GUnit 4 at Georgia Power’s Plant Vogtle has entered commercial operation, the company announced today. The new unit can produce enough electricity to power an estimated 500,000 homes and businesses, according to the company.
Christoffer Gottlieb, Vasily Arzhanov, Waclaw Gudowski, Ninos Garis
Nuclear Technology | Volume 155 | Number 1 | July 2006 | Pages 67-77
Technical Paper | Nuclear Plant Operations and Control | doi.org/10.13182/NT06-A3746
Articles are hosted by Taylor and Francis Online.
Support vector machines (SVMs), a relatively new paradigm in statistical learning theory, are studied for their potential to recognize transient behavior of detector signals corresponding to various accident events at nuclear power plants (NPPs). Transient classification is a major task for any computer-aided system for recognition of various malfunctions. The ability to identify the state of operation or events occurring at an NPP is crucial so that personnel can select adequate response actions. The Modular Accident Analysis Program, version 4 (MAAP4) is a program that can be used to model various normal and abnormal events in an NPP. This study uses MAAP signals describing various loss-of-coolant accidents in boiling water reactors. The simulated sensor readings corresponding to these events have been used to train and test SVM classifiers. SVM calculations have demonstrated that they can produce classifiers with good generalization ability for our data. This in turn indicates that SVMs show promise as classifiers for the learning problem of identifying transients.