Journal
ANNALS OF NUCLEAR ENERGY
Volume 146, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.anucene.2020.107626
Keywords
DPSA; DPRA; Machine learning; Convolutional neural network
Categories
Funding
- DOE Office of Nuclear Energy's Nuclear Energy University Programs [17-12723]
Ask authors/readers for more resources
Operating staffs of a nuclear power plant (NPP) are responsible for returning the NPP to a stable state and alert authorities if there is the potential for offsite radiological consequences following an accident. An operator support tool (OST) using deep learning techniques and trained by data from dynamic probabilistic safety/risk assessment (DPSA/DPRA) is proposed to assist the NPP personnel in decision-making. The DPSA/DPRA methodology employs time-dependent branching conditions based on the evolving state of the NPP and accounts for complex hardware/process/software/human interactions to predict possible outcomes of the initiating event. A large number of scenarios generated from the DPSA/DPRA performed for a pressurized water reactor station blackout as a function of time were used to train the OST to predict possible offsite dose outcomes at 2-mile and 10-mile site boundaries for emergency response planning. The results show that the OST can predict offsite dose levels with greater than 90% accuracy. (C) 2020 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available