Skip to main content
SHARE
Publication

CTF Improved Drag Model and Flow Regime Transition Criteria

by Belgacem Hizoum, Vineet Kumar, Robert K Salko Jr
Publication Type
Conference Paper
Book Title
19th International Topical Meeting on Nuclear Reactor Thermal Hydraulics (NURETH-19)
Publication Date
Publisher Location
Illinois, United States of America
Conference Name
19th International Topical Meeting on Nuclear Reactor Thermal Hydraulics (NURETH-19)
Conference Location
Brussels, Belgium
Conference Sponsor
American Nuclear Society
Conference Date
-

The demand for accurate prediction of two-phase flow behavior in a boiling water reactor (BWR) requires a comprehensive understanding of flow regime, void fraction, heat transfer, and pressure drop. The CTF subchannel code, which is used for the Thermal/Hydraulic (T/H) solution in the Consortium for Advanced Simulation of Light Water Reactors (CASL)-developed Virtual Environment for Reactor Application (VERA) core simulator, is being further developed for BWR applications. In support of this goal, the present work highlights some of the two-phase closure model developments towards improving the CTF void fraction prediction, especially for subcooled boiling. The drift-flux approach has been well-developed for upward dispersed two-phase flows and proven to be accurate in predicting void fraction in bubbly and slug flow regimes. In this work, these kinematic constitutive relations for the drift-flux velocity have been implemented into CTF to describe the interfacial drag of bubbly flow as an alternative to the existing model for better void fraction prediction. The success of these constitutive relations also relies on a good flow regime map that accounts for flow conditions and channel geometry. A more reliable flow regime transition criteria that account for the flow condition has also been implemented in this study for modeling the flow regime transition criteria. The newly implemented models are shown to give improved void fraction predictions in comparison to experimental data.