A way to confine potential consequences of an accident in a nuclear reactor is to limit the number of fuel rods expected to fail in the course of the incident. Particularly, the safety regulations in Finland require that the number of failed fuel rods in the most severe accident scenario would be less than 10% of all the rods. In safety assessments, the estimation of the fraction of failing rods is conventionally based on conservative analyses, but this approach has several downsides. Sometimes it is hard to judge whether the assumptions are conservative because the phenomena in the reactor are highly nonlinear. Additionally, conservative methods often lead to excessive margins and that way to economic losses. As a result, statistical best-estimate methods have acquired an established position during the past two decades. The development started worldwide when the U.S.NRC revised its rules in 1988 to allow realistic best-estimate methods complemented with uncertainty analysis alongside with the old conservative approach. These methods are based on the selection and variation of parameters that are important in accident conditions. The accident scenario is simulated with a designated computer programme several times with different parameter values between simulations, and that way an estimation of the number of failed rods is obtained. In order to the results to be statistically reliable, enormous number of simulation runs is needed. Thus the analysis requires a lot of computer resources, and this has been a limiting factor for the breakthrough of this procedure. Different approaches have been used to reduce the number of fuel performance code calculations. Before the current efforts in this field at VTT, there has not been a statistical or any other systematic tool in Finland for the evaluation of rod failures. Since 2006, a calculation system for statistical fuel failure analysis has been under development. The calculation procedure introduces neural networks as a new way to reduce the number of simulations. Neural networks are familiar from other applications in nuclear plant modelling but the concept is a novelty in this context. A neural network is trained with results from stacked fuel performance code calculations, and then the network is used as a substitute for the analysis code. Further, the developed method utilizes the results of nonparametric statistics, the Wilks' formula in particular. The system has been successfully tested with a small scale analysis and it is now ready for full reactor scale applications.
|Title of host publication||SAFIR2010. The Finnish Research Programme on Safety 2007-2010. Final Report. Puska, Eija Karita & Suolanen, Vesa. VTT Research Notes 2571|
|Place of Publication||Espoo|
|Publisher||VTT Technical Research Centre of Finland|
|Pages||132 - 144|
|ISBN (Print)||978-951-38-7689-0 (soft back ed.), 978-951-38-7690-6 (URL: http://www.vtt.fi/publications/index.jsp|
|Publication status||Published - 2011|
|MoE publication type||D2 Article in professional manuals or guides or professional information systems or text book material|