Programming language

Reliability engineering

Loss-of-coolant accident

Nuclear power plant

Accident management

Nuclear reactor

Coolant

Instrumentation (computer programming)

Engineering

Artificial intelligence

Physics

Nuclear reactor core

Modular design

Nuclear engineering

SIGNAL (programming language)

Boiler (water heating)

Operating system

Computer science

Nuclear power

Nuclear physics

Artificial neural network

Waste management

Mechanical engineering

Nuclear Engineering and Technology

Acquiring instrumentation signals generated from nuclear power plants (NPPs) is essential to maintain nuclear reactor integrity or to mitigate an abnormal state under normal operating conditions or severe accident circumstances. However, various safety-critical instrumentation signals from NPPs cannot be accurately measured on account of instrument degradation or failure under severe accident circumstances. Reactor vessel (RV) water level, which is an accident monitoring variable directly related to reactor cooling and prevention of core exposure, was predicted by applying a few signals to deep neural networks (DNNs) during severe accidents in NPPs. Signal data were obtained by simulating the postulated loss-of-coolant accidents at hot- and cold-legs, and steam generator tube rupture using modular accident analysis program code as actual NPP accidents rarely happen. To optimize the DNN model for RV water level prediction, a genetic algorithm was used to select the numbers of hidden layers and nodes. The proposed DNN model had a small root mean square error for RV water level prediction, and performed better than the cascaded fuzzy neural network model of the previous study. Consequently, the DNN model is considered to perform well enough to provide supporting information on the RV water level to operators.

Nuclear reactor vessel water level prediction during severe accidents using deep neural networks