scholarly journals EVACUATING ORANGE COUNTY, CALIFORNIA, IN ABOUT ELEVEN (11) SECONDS

Author(s):  
J. Riechel

Abstract. Orange County, California, residents must evacuate when there is a crisis at the San Onofre nuclear power plant in San Clemente, California. They must travel roughly north and east over safe roads. Depending on their location in Orange County (OC), residents will travel to the closest of four (4) waypoints located on the border between OC and neighboring counties. Once a waypoint is reached, evacuees can travel in any direction except back toward OC. The approximate driving distance algorithm is used to suggest a possible waypoint for each address—business or residential. The approximate driving distance algorithm makes this evacuation planning possible, as it takes only around eleven (11) seconds on a state-of-the-art laptop to route 1.1 to 1.2 million addresses to waypoints. Using actual driving distances would take too long and be too expensive, taking approximately fifty-three (53) days on the same platform. The waypoint suggestions are just that: suggestions. In some cases, the approximate driving distance algorithm might not choose the closest waypoint.

2021 ◽  
Vol 9 ◽  
Author(s):  
Guang Hu ◽  
Taotao Zhou ◽  
Qianfeng Liu

Data-driven machine learning (DDML) methods for the fault diagnosis and detection (FDD) in the nuclear power plant (NPP) are of emerging interest in the recent years. However, there still lacks research on comprehensive reviewing the state-of-the-art progress on the DDML for the FDD in the NPP. In this review, the classifications, principles, and characteristics of the DDML are firstly introduced, which include the supervised learning type, unsupervised learning type, and so on. Then, the latest applications of the DDML for the FDD, which consist of the reactor system, reactor component, and reactor condition monitoring are illustrated, which can better predict the NPP behaviors. Lastly, the future development of the DDML for the FDD in the NPP is concluded.


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