Tool And Algorithms for Rapid Source Term Prediction (RASTEP) Based on Bayesian Belief Networks

Detta är en Master-uppsats från Uppsala universitet/Institutionen för informationsteknologi

Författare: Prerna Agrawal; [2015]

Nyckelord: ;


In case of an accident in a nuclear power plant (NPP), the fast and cor-rect identification of the NPP state that would give a prediction of a possible radioactive release presents a major challenge to both nuclear power plants and regulators. Such prediction is important so that correct and timely decisions and measures are taken to mitigate accident consequences, such as evacuation of people from areas around the power plant. Recent research work [2][3] proposes analyzing the NPP using the Bayesian Belief Network models as a solution to this problem. A BBN is a graphical model that represents any entity with a set of connected nodes. These nodes represent the random variables and the connections between the nodes represent the conditional dependencies between them [7]. However, the BBN models alone are not suitable for use in off-site locations under high stress conditions by people who are not experts. Hence there arises a need for an interface that would –– - Be easy to operate by non-experts under high stress situations with incomplete knowledge of the plant state. - Provide the more detailed information about the network that is not easy for users to read out from the BBN itself. - Provide good graphical displays of the radioactive release predictions and other statistics of the network. One such tool is developed as a part of this master thesis project. The contribution is twofold –– - Analyzing the user requirements, designing the architecture and development of the tool. - Design and implementation of the algorithms for extracting additional information from the network which is not easy to read out while working directly with the BBN. This kind of information helps the user to take some decisions with entering the observations when the user is not a BBN expert. For instance, it helps the user to know which nodes are important to answer and which nodes can be left out. This also helps the user to interpret the intermediate state of the BBN model of the plant. The tool and the algorithms were evaluated by an expert user in order to assess them based on ease of use, value of the analysis output and the processing time. This project work was carried forward in collaboration with Swedish Radiation Safety Authority (SSM) [8]. SSM is already assessing the tool with the goal to obtain fast and independent predictions of radioactive releases based on plant observations.

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