21 Jan (NucNet): UK-based engineering consultancy Lloyds Register Consulting (LRC) has teamed up with the Swedish Radiation Safety Authority (SSM) to improve prediction software used to support the decision-making process for emergency response in the event of a nuclear incident with radiological consequences, a statement said.
Rastep (‘Rapid Source Term Prediction’) is a diagnosis tool designed to aid decision-making during accidents at nuclear power stations, LRC said. It helps operators understand potential probabilities of issues happening given a set of conditions. This output is essential for an operator’s offsite emergency response planning process.
LRC and SSM are now developing an advanced interface for Rastep. The interface will “optimise the decision-making process” for emergency planning and response personnel in the nuclear industry.
The aim of the project is to improve the application of ‘Level 5’ of the international defence-in-depth guidelines. Level 5 provides guidelines for mitigating the consequences of radioactive material release through appropriate offsite emergency response. According to the International Atomic Energy Agency, implementation of defence-in-depth concept contains several levels of protection, including successive barriers preventing the release of radioactive material to the environment.
The Rastep project began earlier this month and is scheduled for completion by August 2015, LRC said. “Rastep will help nuclear operators understand potential probabilities of issues happening given a set of conditions and plan the offsite emergency response accordingly.”
By improving the way the questions are organised and the detail in the graphical illustrations, the interface will provide better clarity on decision making for nuclear operators, and help to inform on predicted or likely consequences. This will provide improved offsite emergency response initiatives for industry and better protect the public.
According to Nordik Nuclear Safety Research (NKS), Rastep models radioactive dispersion by using Bayesian belief networks – essentially probabilistic graphical models – and pre-calculated source terms, such as amount, timing, and pathway of released radionuclides.
Development of such tools is increasingly desired by offsite emergency planning and response personnel to help prepare accident response options and to quickly predict likely offsite consequences and result in “a more appropriate offsite response”, NKS said.
The need for improving accident diagnosis tools and forecasting likely scenarios was demonstrated by the March 2011 accident at Japan’s Fukushima-Daiichi nuclear station, LRC said. The accident has led to “a greater focus on strengthening the nuclear industry’s defence-in-depth safety philosophy”.
