Uncertainty Quantification and Sensitivity Analysis Applications to Fuel Performance Modeling

摘要

Best-estimate fuel performance codes such as BISON currently under development at the Idaho National Laboratory, utilize empirical and mechanistic lower-length-scale informed correlations to predict fuel behavior under normal operating and accident reactor conditions Traditionally, best-estimate results are presented using the correlations with no quantification of the uncertainty in the output metrics of interest. However, there are associated uncertainties in the input parameters and correlations used to determine the behavior of the fuel and cladding under irradiation Therefore, it is important to perform uncertainty quantification and include confidence bounds on the output metrics that take into account the uncertainties in- the inputs. In addition, sensitivity analyses can be performed to determine which input parameters have the greatest influence on the outputs. In this paper we couple the BISON fuel performance code to the DAKOTA uncertainty analysis software to analyze a representative fuel performance problem The case studied in this paper is based upon rod 1 from the IFA-432 integral experiment performed at the Halden Reactor in Norway The rodlet is representative of a BWR fuel rod The input parameters uncertainties are broken into three separate categories including boundary condition uncertainties (e.g., power, coolant flow rate), manufacturing uncertainties (e.g., pellet diameter, cladding thickness), and model uncertainties (e.g., fuel thermal conductivity, fuel swelling) Utilizing DAKOTA, a variety of statistical analysis techniques are applied to quantify the uncertainty and sensitivity of the output metrics of interest. Specifically, we demonstrate the use of sampling methods, polynomial chaos expansions, surrogate models, and variance-based decomposition. The output metrics investigated in this study are the fuel centerhne temperature, cladding surface temperature, fission gas released, and fuel rod diameter. The results highlight the importance of quantifying the uncertainty and sensitivity in fuel performance modeling predictions and the need for additional research into improving the material models that are currently available