A guided diffusion model trained on DNS data reconstructs bubble-phase velocity fields in bubbly flows from liquid measurements, reproducing key statistics and supporting 3D reconstruction via 2D slice patching.
Gappy Reconstruction of Bubbly Flows by Guided Diffusion Models
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abstract
Experiments in multiphase flows are often limited in their ability to simultaneously obtain velocity measurements in different phases. At the same time, flow reconstruction from phase-limited measurements is a challenging problem due to the substantially different velocity statistics across the phases. We address this problem for buoyancy-driven bubbly flows in the pseudo-turbulence regime by using a guided diffusion model. We train the model using two-dimensional slices of the velocity field extracted from fully resolved three-dimensional direct numerical simulations. The model generates physically realistic velocity fields both unconditionally and when conditioned on the surrounding liquid flow. The reconstructed bubble-phase velocity field accurately reproduces key statistical features of the flow. We further show that a simple patching procedure for adjacent two-dimensional slices enables a reasonable reconstruction of the three-dimensional flow inside a bubble. These results establish the potential of diffusion models to serve as generative priors for three-dimensional turbulent multiphase flows, opening a route toward the reconstruction of unobserved or experimentally inaccessible velocity fields from sparse, partial, or phase-limited measurements.
fields
physics.flu-dyn 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Gappy Reconstruction of Bubbly Flows by Guided Diffusion Models
A guided diffusion model trained on DNS data reconstructs bubble-phase velocity fields in bubbly flows from liquid measurements, reproducing key statistics and supporting 3D reconstruction via 2D slice patching.