Uncertainty Quantification in Support of Severe Accident Analysis Code User Confidence Using MELCOR-DAKOTA

Abstract

Abstract Uncertainty and Sensitivity analysis methods are often used in severe accident analysis for validating the complex physical models employed in the system codes that simulate such scenarios. This is necessitated by the large uncertainties associated with the physical models and boundary conditions employed to simulate severe accident scenarios. Uncertainty analysis involves primarily the estimation of the distribution of a key figure-of-merit (FOM) following the propagation of uncertainty in selected input parameters through a system code. The input parameters are sampled within defined ranges based on assigned probability distribution functions (PDFs) for the required number of code runs/realizations using stochastic sampling techniques. Input parameter selection is based on their importance to the key FOM, which is determined by the parameter identification and ranking table (PIRT). Sensitivity analysis investigates the contribution of each uncertain input parameter to the uncertainty of the selected FOM. In this study, the integrated severe accident analysis code MELCOR was coupled with a design analysis kit for optimization and terascale applications (DAKOTA), an optimization and uncertainty quantification tool in order to investigate the effect of input parameter uncertainty on hydrogen generation. The methodology developed was applied to the Fukushima Daiichi unit 1 nuclear power plant (NPP) accident scenario, which was modeled in another study. The results show that there is approximately 22.46% uncertainty in the amount of hydrogen generated as estimated by a single MELCOR run given the uncertainty in selected input parameters. The sensitivity analysis results also reveal that MELCOR input parameters: COR_SC 1141(melt flow rate per unit width at breakthrough candling), COR_ZP (porosity of fuel debris beds), and COR_EDR (characteristic debris size in the core region) contributed most significantly to the uncertainty in hydrogen generation.