Scalable multi-phase flows in complex domains using adaptive octree meshes
Efficiently and accurately simulating partial differential equations (PDEs) in and around arbitrarily defined geometries, especially with high levels of adaptivity, has significant implications for different application domains. A key bottleneck in the above process is the fast construction of a *good* adaptively-refined mesh. In this work, we present efficient octree-based adaptive discretization approach capable of carving out arbitrarily shaped regions from the parent domain: an essential requirement for fluid simulations around complex objects and for modeling multi-phase flows. Both explicit and implicit definitions of geometries are supported. We evaluate our approach using a range of applications with varying geometric and physics complexity. These include Large Eddy Simulations (LES) of flows around complex geometries: accurately computing the drag coefficient of a sphere across Reynolds numbers $1− 10^6$ encompassing the drag crisis regime; simulating flow features across a semi-truck for investigating the effect of platooning on efficiency; and a case with multiple complex objects, to evaluate COVID-19 transmission risk in classrooms. We also study problems with more complex physics, solving thermodynamically-consistent Cahn-Hilliard Navier-Stokes systems that model two-phase flows, including the single bubble rise and Rayleigh-Taylor instability and primary jet atomization.
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