RESUMEN

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.

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RESUMEN

Prompt Gamma Neutron Activation Analysis is a nuclear-based technique that can be used in explosives detection. It relies on bombarding unknown samples with neutrons emitted from a neutron source. These neutrons interact with the sample nuclei emitting the gamma spectrum with peaks at specific energies, which are considered a fingerprint for the sample composition. Analyzing these peaks heights will give information about the unknown sample material composition. Shielding the sample from gamma rays or neutrons will affect the gamma spectrum obtained to be analyzed, providing a false indication about the sample constituents, especially when the shield is unknown. Here we show how using deep neural networks can solve the shielding drawback associated with the prompt gamma neutron activation analysis technique in explosives detection. We found that the introduced end-to-end framework was capable of differentiating between explosive and non-explosive hydrocarbons with accuracy of 95% for the previously included explosives in the model development data set. It was also, capable of generalizing with accuracy 80% over the explosives which were not included in the model development data set. Our results show that coupling prompt gamma neutron activation analysis with deep neural networks has a good potential for high accuracy explosives detection regardless of the shield presence.