RESUMO
Nuclear energy plays an important role in global energy supply, especially as a key low-carbon source of power. However, safe operation is very critical in nuclear power plants (NPPs). Given the significant impact of human-caused errors on three serious nuclear accidents in history, artificial intelligence (AI) has increasingly been used in assisting operators with regard to making various decisions. In particular, data-driven AI algorithms have been used to identify the presence of accidents and their root causes. However, there is a lack of an open NPP accident dataset for measuring the performance of various algorithms, which is very challenging. This paper presents a first-of-its-kind open dataset created using PCTRAN, a pre-developed and widely used simulator for NPPs. The dataset, namely nuclear power plant accident data (NPPAD), basically covers the common types of accidents in typical pressurised water reactor NPPs, and it contains time-series data on the status or actions of various subsystems, accident types, and severity information. Moreover, the dataset incorporates other simulation data (e.g., radionuclide data) for conducting research beyond accident diagnosis.
RESUMO
A conclusion from the lessons learned after the March 2011 Fukushima Daiichi accident was that Korea needs a tool to estimate consequences from a major accident that could occur at a nuclear power plant located in a neighboring country. This paper describes a suite of computer-based codes to be used by Korea's nuclear emergency response staff for training and potentially operational support in Korea's national emergency preparedness and response program. The systems of codes, Northeast Asia Nuclear Accident Simulator (NANAS), consist of three modules: source-term estimation, atmospheric dispersion prediction and dose assessment. To quickly assess potential doses to the public in Korea, NANAS includes specific reactor data from the nuclear power plants in China, Japan and Taiwan. The completed simulator is demonstrated using data for a hypothetical release.