This is NKS NKS-R NKS-B News Seminars NKS Reports Phantom Library

You are here: Homepage NKS Reports View document

List all reports List all NKS-R reports List all NKS-B reports Search Reports  
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:3216
Download:pdf NKS-267.pdf
Contact NKS   NKS Sekretariatet
Boks 49
DK-4000 Roskilde
  Telephone +45 46 77 40 41
E-mail: nks@nks.org 
 

Address for visitors
Directions and map

Privacy policy

Cookie policy

 

Website last modified: 03 December 2024