Uncertainty analysis of condensation heat transfer benchmark using CFD code GASFLOW-MPI

摘要

With the development of the computational capability, Computational Fluid Dynamics (CFD) is more and more widely used as the best estimate approach for the design and safety issues related to reactor thermal hydraulics. The conjugate condensation heat transfer is a widespread phenomenon in nuclear safety analysis, such as a key heat transfer process for Passive Containment Cooling Systems (PCCS) in advanced passive reactors. However, the uncertainty analysis of CFD predictions applied to reactor thermal hydraulics is still immature. In this work, the uncertainty quantification of the condensation heat transfer in COPAIN experiment was performed using a well-validated parallel CFD code GASFLOW-MPI. Six sources of uncertainty were considered here, including the inlet velocity/temperature/turbulent intensity, outlet pressure and condensation heat transfer coefficient, as well as the computational mesh size. For the first five uncertainty sources, the deterministic sampling method was employed in this work to reduce the required number of sampling points. Unlike the ensemble in the random sampling Monte Carlo method, the deterministic sampling method represents the probability density function with an ensemble that has the same statistical moments but contains much fewer samples. Two ensembles which were 2nd and 4th order statistical moments accuracy, respectively, were employed in this work and their performances were compared. The Richardson extrapolation method was used to quantify the uncertainty propagated from the computational mesh size. The contributions of each uncertainty source to the condensation heat flux and temperature distribution were also performed. The results of the uncertainty quantification are consistent well with the experimental data in COPAIN facility, suggesting that the deterministic sampling method and Richardson extrapolation method are powerful tools for uncertainty quantification of CFD calculations.