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Division Spotlight
Fuel Cycle & Waste Management
Devoted to all aspects of the nuclear fuel cycle including waste management, worldwide. Division specific areas of interest and involvement include uranium conversion and enrichment; fuel fabrication, management (in-core and ex-core) and recycle; transportation; safeguards; high-level, low-level and mixed waste management and disposal; public policy and program management; decontamination and decommissioning environmental restoration; and excess weapons materials disposition.
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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.
Miltiadis Alamaniotis (Univ of Texas at San Antonio), Asok Ray (Penn State)
Proceedings | Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technolgies (NPIC&HMIT 2019) | Orlando, FL, February 9-14, 2019 | Pages 431-439
Monitoring of Boiling Water Rectors (BWRs) is a complex process that requires the use of a numerous sensors and systems. Acquisition of data and the subsequent processing of it accommodate inference making with regard to the state of the reactor system. System identification promotes decision making with regard to operation action taking. In this paper, we present a new method for serially integrating two machine learning tools and more specifically a neural network and a set of algorithms for learning Gaussian processes. Both sets of tools exhibit learning capabilities, and their integration in the current work offers a two-stage learning schema applied to identification of transient states in BWR. In particular, the proposed methodology utilizes the synergism of a set of Gaussian processes with a feedforward neural network for recognizing the type of loss of coolant accident (LOCA) that occurs in the reactor. The methodology is tested on a set of real-world datasets taken from the FIX-II facility. Results demonstrate efficacy of the method to accurately identify the occurring LOCA among three possible states.