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Division Spotlight
Reactor Physics
The division's objectives are to promote the advancement of knowledge and understanding of the fundamental physical phenomena characterizing nuclear reactors and other nuclear systems. The division encourages research and disseminates information through meetings and publications. Areas of technical interest include nuclear data, particle interactions and transport, reactor and nuclear systems analysis, methods, design, validation and operating experience and standards. The Wigner Award heads the awards program.
Meeting Spotlight
2025 ANS Annual Conference
June 15–18, 2025
Chicago, IL|Chicago Marriott Downtown
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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Smarter waste strategies: Helping deliver on the promise of advanced nuclear
At COP28, held in Dubai in 2023, a clear consensus emerged: Nuclear energy must be a cornerstone of the global clean energy transition. With electricity demand projected to soar as we decarbonize not just power but also industry, transport, and heat, the case for new nuclear is compelling. More than 20 countries committed to tripling global nuclear capacity by 2050. In the United States alone, the Department of Energy forecasts that the country’s current nuclear capacity could more than triple, adding 200 GW of new nuclear to the existing 95 GW by mid-century.
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.