TY - JOUR

T1 - Distinctive physical insights driven from machine learning modelling of nuclear power plant severe accident scenario propagation

AU - Hossny, Karim

AU - Villanueva, Walter

AU - Wang, Hongdi

N1 - © 2023. The Author(s).

PY - 2023/1/17

Y1 - 2023/1/17

N2 - The severe accident scenario propagation studies of nuclear power plants (NPPs) have been one of the most critical factors in deploying nuclear power for decades. During an NPP accident, the accident scenario can change during its propagation from the initiating event to a series of accident sub-scenarios. Hence, having time-wise updated information about the current type of accident sub-scenario can help plant operators mitigate the accident propagation and underlying consequences. In this work, we demonstrate the capability of machine learning (Decision Tree) to help researchers and design engineers in finding distinctive physical insights between four different types of accident scenarios based on the pressure vessel's maximum external surface temperature at a particular time. Although the four accidents we included in this study are considered some of the most extensively studied NPPs accident scenarios for decades, our findings shows that decision tree classification could define remarkable distinct differences between them with reliable statistical confidence.

AB - The severe accident scenario propagation studies of nuclear power plants (NPPs) have been one of the most critical factors in deploying nuclear power for decades. During an NPP accident, the accident scenario can change during its propagation from the initiating event to a series of accident sub-scenarios. Hence, having time-wise updated information about the current type of accident sub-scenario can help plant operators mitigate the accident propagation and underlying consequences. In this work, we demonstrate the capability of machine learning (Decision Tree) to help researchers and design engineers in finding distinctive physical insights between four different types of accident scenarios based on the pressure vessel's maximum external surface temperature at a particular time. Although the four accidents we included in this study are considered some of the most extensively studied NPPs accident scenarios for decades, our findings shows that decision tree classification could define remarkable distinct differences between them with reliable statistical confidence.

KW - Engineering

KW - Fukushima Nuclear Accident

KW - Nuclear Power Plants

U2 - 10.1038/s41598-023-28205-y

DO - 10.1038/s41598-023-28205-y

M3 - Article

C2 - 36650268

VL - 13

SP - 930

JO - Scientific Reports

JF - Scientific Reports

SN - 2045-2322

IS - 1

M1 - 930

ER -