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Decommissioning & Environmental Sciences
The mission of the Decommissioning and Environmental Sciences (DES) Division is to promote the development and use of those skills and technologies associated with the use of nuclear energy and the optimal management and stewardship of the environment, sustainable development, decommissioning, remediation, reutilization, and long-term surveillance and maintenance of nuclear-related installations, and sites. The target audience for this effort is the membership of the Division, the Society, and the public at large.
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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
Vogtle-3 shuts down for valve issue
One of the new Vogtle units in Georgia was shut down unexpectedly on Monday last week for a valve issue that has been investigated and repaired. According to multiple local news outlets, Georgia Power reported on July 17 that unit 3 was back in service.
Southern Company spokesperson Jacob Hawkins confirmed that Vogtle-3 went off line at 9:25 p.m. on July 8 “due to lowering water levels in the steam generators caused by a valve issue on one of the three main feedwater pumps.”
Miltiadis Alamaniotis, Andreas Ikonomopoulos, Tatjana Jevremovic, Lefteri H. Tsoukalas
Nuclear Technology | Volume 175 | Number 2 | August 2011 | Pages 480-497
Technical Paper | Radiation Measurements and General Instrumentation | doi.org/10.13182/NT11-A12319
Articles are hosted by Taylor and Francis Online.
Nuclear resonance fluorescence (NRF) has been considered as a promising method for cargo inspection. Almost all isotopes existing in nature yield a unique NRF spectral signature. NRF signals obtained during cargo inspection are aggregates of various signatures from materials hidden inside. The challenge is to identify individual signatures embedded in this signature aggregation. Background noise and spectra overlap to further complicate the NRF signal analysis. This paper addresses these concerns through an intelligent methodology recognizing signature spectra and, subsequently, identifying cargo materials. The methodology relies on fuzzy logic for pattern identification and evaluation of the weighted options involved in decision making. The intelligent methodology is presented using different simulated NRF signal scenarios. The results obtained demonstrate that the algorithm is highly accurate in most spectra carrying a signal-to-noise ratio (SNR) >20 db. Misses and false alarms were observed for isotopes with only one NRF peak (lead) with SNR <35 db. Extensive parameter testing under different scenarios indicated the existence of parameter couples that maximize the accuracy even for SNR values <20 db. In all cases the algorithm execution time was <0.1 s and was significantly faster than that of the maximum likelihood algorithm.