Bubble Knowledge can make Nuclear Reactors Safer

Wednesday, June 6, 2018 @ 12:06 PM gHale

Researchers are using the Argonne Leadership Computing Facility’s Mira supercomputer to better understand boiling phenomena, bubble formation, and two-phase bubbly flow in nuclear reactors.
Source: Image courtesy of Igor Bolotnov, North Carolina State University

Inside nuclear reactors, boiling water, bubbles, and turbulent flows affect safety and efficiency.

For a long time, modeling turbulent bubbly flows was a challenging, time-consuming problem because researchers were largely limited to experiments that yielded only a few bubbles at a time.

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Now, they can simulate the thousands of bubbles needed to model and predict the behavior of the flows in nuclear reactors because a team from North Carolina State University developed a bubble-tracking method. The supercomputer-based method produces a level of detail that cannot be observed directly in experiments.

With a fundamental understanding of the bubbly flows that occur in nuclear reactors, researchers can improve reactors’ performance. Advanced modeling and simulation tools can help advance reactor safety and efficiency.

In addition, efforts like this are helping the nuclear industry adopt novel approaches in reactor analysis. Such analyses are crucial for successful reactor designs.

As a result of the new method, researchers are shedding light on boiling phenomena, bubble formation, and turbulent liquid/gas flows in nuclear reactor geometries. Using Argonne Leadership Computing Facility (ALCF) supercomputers, researchers devised a way to conduct a direct numerical simulation of fully resolved deformable bubbles.

The team’s approach performs smaller simulations to obtain statistically steady-state conditions and extract physically based numerical data for the development of coarser scale models. The Department of Energy’s (DoE) Consortium for Advanced Simulation of Light Water Reactors used the results from the detailed simulations to develop a new generation of boiling models to be included in an advanced virtual reactor multiphysics model.

Additionally, the simulations produced detailed distributions of bubble concentration and estimated the variation of the forces acting on the bubbles, providing insights to advance the understanding of turbulent two-phase flows.

Large-scale runs of the new approach demonstrated the new bubble tracking approach, as well as the data processing and collecting techniques at scale for future simulations. The team’s method can collect detailed two-phase flow information at the individual bubble level. This advanced analytical framework will help researchers gain insights from the “Big Data” produced by the large-scale simulations.

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