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Interface Tracking Investigation of Geometric Effects on the Bubbly Flow in PWR Subchannels

Jun Fang, Joseph J. Cambareri, Michel Rasquin, Andre Gouws, Ramesh Balakrishnan, Kenneth E. Jansen, Igor A. Bolotnov

Nuclear Science and Engineering / Volume 193 / Number 1-2 / January-February 2019 / Pages 46-62

Technical Paper – Selected papers from NURETH 2017 / dx.doi.org/10.1080/00295639.2018.1499280

Received:March 30, 2018
Accepted:July 7, 2018
Published:December 21, 2018

Absorbing heat from the fuel rod surface, water as coolant can undergo subcooled boiling within a pressurized water reactor (PWR) fuel rod bundle. Because of the buoyancy effect, the vapor bubbles generated will then rise along and interact with the subchannel geometries. Reliable prediction of bubble behavior is of immense importance to ensure safe and stable reactor operation. However, given a complex engineering system like a nuclear reactor, it is very challenging (if not impossible) to conduct high-resolution measurements to study bubbly flows under reactor operation conditions. The lack of a fundamental two-phase-flow database is hindering the development of accurate two-phase-flow models required in more advanced reactor designs. In response to this challenge, first-principles–based numerical simulations are emerging as an attractive alternative to produce a complementary data source along with experiments. Leveraged by the unprecedented computing power offered by state-of-the-art supercomputers, direct numerical simulation (DNS), coupled with interface tracking methods, is becoming a practical tool to investigate some of the most challenging engineering flow problems. In the presented research, turbulent bubbly flow is simulated via DNS in single PWR subchannel geometries with auxiliary structures (e.g., supporting spacer grid and mixing vanes). The geometric effects these structures exert on the bubbly flow are studied with both a conventional time-averaging approach and a novel dynamic bubble tracking method. The new insights obtained will help inform better two-phase models that can contribute to safer and more efficient nuclear reactor systems.