Abstract
Computational fluid dynamics (CFD) has become an effective method for researching two-phase flow in reactor systems. However, the uncertainty analysis of Computational fluid dynamics simulation is still immature. The effects of uncertainties from two-phase models and boundary conditions have been analyzed in our previous work. In this work, the uncertainties from a turbulence model on the prediction of subcooled boiling flow were analyzed with the DEBORA benchmark experiments by a deterministic sampling method. Seven parameters in the standard k-ε model, which interrelated momentum, energy, turbulent kinetic energy, and dissipation rate, were studied as uncertainty sources, including Cμ, Cμ,g, C1ε, C2ε, σk, σε, and Prt. Radial parameters were calculated to study the effects of uncertainties from the turbulence model. The contributions of each uncertainty source on void fraction and liquid temperature were also analyzed. It was found that the models can simulate subcooled boiling flow accurately and uncertainty analysis by deterministic sampling can give a reference interval to increase the reliability of results. The C2ε and C1ε, parameters in the production term and dissipation term of transport equations, dominate the radial distributions of void fraction and liquid temperature.
Highlights
Subcooled boiling flow has received attention from industrial designers due to its high heat transfer coefficient
In most Computational fluid dynamics (CFD) analysis on subcooled boiling flow, the boundary conditions and models are all treated as deterministic values
There might be some uncertainties from boundary condition measuring or the simplification of the model that needs to be considered for the numerical simulation of boiling flow (Bestion et al, 2016)
Summary
Subcooled boiling flow has received attention from industrial designers due to its high heat transfer coefficient. The default value of parameter Cμ is obtained by the experiments which have a dynamic equilibrium between the production and the dissipation of pulsation kinetic energy in the boundary layer, while it may be not applicable to the flow that deviates from the dynamic equilibrium (Rodi, 1984). The uncertainties of these parameters can be transmitted to the CFD results.
Sign-in/Register to view highlights & summary 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.
