Abstract
Artificial Intelligence (AI) algorithms have shown their capability to complement human analysis in the understanding of complex phenomena. Their advantages essentially rely on the flexibility of their construction that allows capturing complex relationships between inputs and outputs of a problem and also on their ability to adapt themselves via a learning phase to the available information. As a result, AI is widely used in many scientific fields and especially those related to the nuclear industry. This work deals an application of AI based on several classical machine learning approaches for the study of Reactivity Initiated Accidents (RIA) in the CABRI experimental pulse reactor located at the Cadarache research center, southern France. We focus on the interpretation of the fuel rod behaviour during the power pulse using the online fuel motion monitoring system called the hodoscope. The objective of this paper is to investigate how AI algorithms can be used for the automatic detection of a generic fuel delocalization from the signals recorded by the hodoscope.
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