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
Nuclear Nonproliferation Policy
The mission of the Nuclear Nonproliferation Policy Division (NNPD) is to promote the peaceful use of nuclear technology while simultaneously preventing the diversion and misuse of nuclear material and technology through appropriate safeguards and security, and promotion of nuclear nonproliferation policies. To achieve this mission, the objectives of the NNPD are to: Promote policy that discourages the proliferation of nuclear technology and material to inappropriate entities. Provide information to ANS members, the technical community at large, opinion leaders, and decision makers to improve their understanding of nuclear nonproliferation issues. Become a recognized technical resource on nuclear nonproliferation, safeguards, and security issues. Serve as the integration and coordination body for nuclear nonproliferation activities for the ANS. Work cooperatively with other ANS divisions to achieve these objective nonproliferation policies.
Meeting Spotlight
2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
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
Mar 2024
Jan 2024
Latest Journal Issues
Nuclear Science and Engineering
April 2024
Nuclear Technology
Fusion Science and Technology
February 2024
Latest News
Can hydrogen be the transportation fuel in an otherwise nuclear economy?
Let’s face it: The global economy should be powered primarily by nuclear power. And it probably will by the end of this century, with a still-significant assist from renewables and hydro. Once nuclear systems are dominant, the costs come down to where gas is now; and when carbon emissions are reduced to a small portion of their present state, it will become obvious that most other sources are only good in niche settings. I mean, why use small modular reactors to load-follow when they can just produce that power instead of buffering it?
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.