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Division Spotlight
Accelerator Applications
The division was organized to promote the advancement of knowledge of the use of particle accelerator technologies for nuclear and other applications. It focuses on production of neutrons and other particles, utilization of these particles for scientific or industrial purposes, such as the production or destruction of radionuclides significant to energy, medicine, defense or other endeavors, as well as imaging and diagnostics.
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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!
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The Nuclear Family: Empowering parents and caregivers
The Diversity and Inclusion in ANS Committee is hosting a webinar today to celebrate the contributions of parents in the nuclear industry while fostering diversity and inclusion within the community.
Register now: The webinar, from 1:00-2:00 pm ET, will highlight how the nuclear industry supports caregivers, new parents, and new mothers, and will focus on life transitions and parental responsibilities.
Jacob A. Farber, Daniel G. Cole, Ahmad Y. Al Rashdan, Vaibhav Yadav
Nuclear Technology | Volume 205 | Number 8 | August 2019 | Pages 1043-1052
Technical Paper – Special section on Big Data for Nuclear Power Plants | doi.org/10.1080/00295450.2018.1534484
Articles are hosted by Taylor and Francis Online.
This paper presents data-driven methods to detect loss-of-coolant accidents (LOCAs) in the primary side of a pressurized water reactor. Process data for a variety of accident scenarios have been generated and collected using a generic pressurized water reactor simulator. The data have been used to train kernel density functions, which estimate nonparametric probability density functions based on training data. These density functions have then been used with Bayesian hypothesis testing and maximum likelihood estimation to detect the onset of the LOCAs and to identify where in the primary side the leaks have occurred. The methods have been able to detect the LOCAs for all scenarios tested with an average detection delay of one-seventh the time for the reactor to trip. Furthermore, the methods have been able to correctly identify the leak locations for 92.3% of the scenarios tested, with higher success rates for larger leaks.