An open time-series simulated dataset covering various accidents for nuclear power plants

Abstract

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.

Resilient design in nuclear energy: Critical lessons from a cross-disciplinary analysis of the Fukushima Dai-ichi nuclear accident

“…Molten core material breached the RPV, which depressurized from 6.9 MPa to less than 1 MPa between the evening of March 11, and 02:45 on March 12 ( Figure 2 A). 45 The pressure in Unit-1 RPV remained low throughout the event, confirming the RPV breach and suggesting that some molten core material relocated to the PCV. 47 Concurrently, the Unit-1 PCV pressure readings started showing beyond-design values reaching 0.85 MPa at 02:45, as a result of hydrogen accumulation and gas generation from the molten core-concrete interaction.…”

“…41 Furthermore, we assign timings to key physical events—such as core damage, breach of the RPV, core material relocation, and hydrogen explosion—by incorporating recent findings from severe accident simulations. 42 , 43 , 44 , 45 Finally we use the latest source term time-series estimates provided by Terada et al. 18 to establish the connection between the onsite events and their offsite consequences.…”

Enhancing LOCA Breach Size Diagnosis with Fundamental Deep Learning Models and Optimized Dataset Construction

“…The reactor type selected for this research was the PWR, which is the most popular in the world. The dataset used in this study was obtained from our previous work [31] and was generated from the PCTRAN v1.0 software. PCTRAN, a reactor transient and accident simulation software, can be operated on a personal computer.…”

“…Leveraging a diverse array of mathematical models, PCTRAN facilitates the simulation of nuclear reactor cores and the reactor coolant system during transients and accidents. Its effectiveness has been validated through Fukushima nuclear accident simulations and two operating conditions in our previous work [31], which presents a first-of-its-kind open dataset created using PCTRAN.…”

“…PCTRAN is capable of generating an NPP accident dataset based on the type of accident, the severity of the accident, and the simulation time [31]. The generated data are informative, containing 97 parameters such as time stamp, temperature, flow speed, and power.…”