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NKS Programme Area:
Research Area:NKS R and B
Report Number:NKS-267
Report Title:Using Bayesian Belief Network (BBN) Modelling for Rapid Source Term Prediction - RASTEP Phase 1
Activity Acronym:RASTEP
Authors:Michael Knochenhauer, Vidar Hedtjärn Swaling, Per Alfheim,
Abstract:The project is connected to the development of RASTEP, a computerized source term prediction tool aimed at providing a basis for improving off-site emergency management. RASTEP uses Bayesian belief networks (BBN) to model severe accident progression in a nuclear power plant in combination with pre-calculated source terms (i.e., amount, timing, and pathway of released radio-nuclides). The output is a set of possible source terms with associated probabilities. In the NKS project, a number of complex issues associated with the integration of probabilistic and deterministic analyses are addressed. This includes issues related to the method for estimating source terms, signal validation, and sensitivity analysis. One major task within Phase 1 of the project addressed the problem of how to make the source term module flexible enough to give reliable and valid output throughout the accident scenario. Of the alternatives evaluated, it is recommended that RASTEP is connected to a fast running source term prediction code, e.g., MARS, with a possibility of updating source terms based on real-time observations.
Keywords:BBN, Bayesian Belief Network, Severe Accidents, Source Terms, Level 2 PSA, Signal Validation
Publication date:24 Sept 2012
ISBN:ISBN 978-87-7893-340-9
Number of downloads:1896
Download:pdf NKS-267.pdf
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