The project will use artificial intelligence and signal processing tools to monitor nuclear power plants and to predict the dispersion of radioactivity in time and space following an accident.Visit the project website
Dr Edoardo Patelli
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The mitigation of cooling system breaks in pressurized heavy-water reactor requires fast and effective decision making that, in turn, relies on the prompt detection and accurate diagnostic of the fault. On-line monitoring techniques, based on the continuous acquisition of data during the reactor operation, provide appropriate solutions for the early detection of faults but, on the other hand, raise reasonable doubts on the accuracy and robustness of the response, due to the unavoidable uncertainty associated with the measured data and the low-computationally demanding models adopted.
The SMART project addresses these challenges, focusing on the implementation and validation of on-line monitoring tools for nuclear power plants. The resulting computational tools, developed in the OpenCossan environment, integrate cutting-edge machine learning techniques, able to ensure real-time fault detection, with Bayesian statistics, in order to enhance the robustness of the model response in presence of uncertainty. Application of the tool to a real-world system is currently under development.