ANS is committed to advancing, fostering, and promoting the development and application of nuclear sciences and technologies to benefit society.
Explore the many uses for nuclear science and its impact on energy, the environment, healthcare, food, and more.
Division Spotlight
Human Factors, Instrumentation & Controls
Improving task performance, system reliability, system and personnel safety, efficiency, and effectiveness are the division's main objectives. Its major areas of interest include task design, procedures, training, instrument and control layout and placement, stress control, anthropometrics, psychological input, and motivation.
Meeting Spotlight
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!
Latest Magazine Issues
Jul 2024
Jan 2024
Latest Journal Issues
Nuclear Science and Engineering
August 2024
Nuclear Technology
Fusion Science and Technology
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 since 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. local time 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.”
Junyung Kim, Inseop Jeon, Sanghun Lee, Hyun Gook Kang (RPI)
Proceedings | Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technolgies (NPIC&HMIT 2019) | Orlando, FL, February 9-14, 2019 | Pages 10-23
It has been a challenge in dynamic probabilistic risk assessment (PRA) world that a large number of scenarios from one initiating event with time-related scenario evolutions give complexness on an understanding of the transient/accident scenarios. The understanding of risk which enhances the safety of the entire system requires not only the full understandings of scenario evolutions but also the key characteristics of the events: Both success events and failed events. Since the time evolution is now in consideration of the plant risk assessment, a lot of difficulties such as organizing such large amounts of information and interpreting its physical meaning should be properly resolved. Clustering analysis, one of the unsupervised machine learning (ML) techniques, has been discussed in years to group scenarios with similar characteristics and to identify key patterns of each group so that an analyst can understand entire scenario behaviors by groups. Here we propose a novel methodology of identifying key patterns of scenarios in an accident case of a nuclear power plant system with dynamic reliability analysis. In clustering analysis four items need to be considered: 1Clustering algorithm, 2distance matrix, 3variables in clustering algorithm, and 4cluster validity evaluation. In this paper, partition around medoids (PAM) clustering algorithm with global alignment (GA) kernel distance is utilized. GA kernel, which is considered suitable for clustering time series data, is to assess the similarity between time series data by casting the dynamic time warping (DTW) distances and similarities as positive definite kernels. In order to find variables which will be embedded in the clustering algorithm, multilevel flow model (MFM) methodology is leveraged. For a case study, dynamic PRA tool, MOSAIQUE (Module for SAmpling Input and QUantifying Estimator) coupled with a RELAP-5 generates 2,500 scenarios of SBLOCA. Advanced power reactor 1400 MWe (APR- 1400) is used as a reference plant model. The proposed classification and identification approach has grouped the 8000 scenarios with only 77 clusters and the result can show key patterns shown in core damaged and safe cases which static PRA may not present.