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Division members promote the advancement of mathematical and computational methods for solving problems arising in all disciplines encompassed by the Society. They place particular emphasis on numerical techniques for efficient computer applications to aid in the dissemination, integration, and proper use of computer codes, including preparation of computational benchmark and development of standards for computing practices, and to encourage the development on new computer codes and broaden their use.
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2024 ANS Winter Conference and Expo
November 17–21, 2024
Orlando, FL|Renaissance Orlando at SeaWorld
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Nuclear new build procurement considerations
It may seem counterintuitive, but the best time to enhance the ability to support operations and maintenance for a new plant is before construction starts. This is one of many lessons learned by the currently operating nuclear fleet. As construction and startup of many nuclear facilities was completed, it quickly became evident that the ability to efficiently support operations and maintenance was limited. Most of the information necessary to establish and manage procurement of spare and replacement items, maintenance, and configuration of the facilities was unavailable and had to be gathered on a case-by-case, “on-demand” basis. Absence of necessary information and the associated challenges resulted in the need for staff augmentation and multiyear-long projects to develop equipment bills of material and maintenance programs and to perform technical evaluations for the huge quantities of spare and replacement items being requested.
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.