Abstract
A fission gas release (FGR) model was developed by using an artificial neural network method to predict fission gas release in UO 2 fuel under reactivity initiated accident (RIA) conditions. Based on the test data obtained in the CABRI test reactor and nuclear safety research reactor, the model takes into account the effect of the five parameters: pellet average burnup, peak fuel enthalpy, the ratio of peak fuel enthalpy to pulse width, fission gas release during base-irradiation, and grain size of a fuel pellet. The parametric study of the model, producing a physically reasonable trend of FGR for each parameter, shows that the pellet average burnup and the ratio of peak fuel enthalpy to pulse width are two of the most important parameters. Depending on the combination of input values for the five parameters, the application of the model to a fuel rod under typical RIA conditions of light water reactor produces 1.7–14.0% of FGR for the pellet average burnup ranging from 20 to 70 MW d/kg U.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
