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Modeling and simulations of high-density two-phase flows using projection-based Cahn-Hilliard Navier-Stokes equations

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arxiv 2406.17933 v6 pith:A4AEJ7I5 submitted 2024-06-25 physics.flu-dyn cs.NAmath.NA

classification physics.flu-dyncs.NAmath.NA
keywords cahn-hilliarddensityequationsflowsframeworknavier-stokesprojection-basedtwo-phase
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abstract

Accurately modeling the dynamics of high-density ratio ($\mathcal{O}(10^5)$) two-phase flows is important for many material science and manufacturing applications. This work considers numerical simulations of molten metal oscillations in microgravity to analyze the interplay between surface tension and density ratio, a critical factor for terrestrial manufacturing applications. We present a projection-based computational framework for solving a thermodynamically-consistent Cahn-Hilliard Navier-Stokes equations for two-phase flows with large density ratios. The framework employs a modified version of the pressure-decoupled solver based on the Helmholtz-Hodge decomposition presented in Khanwale et al. [{\it A projection-based, semi-implicit time-stepping approach for the Cahn-Hilliard Navier-Stokes equations on adaptive octree meshes.}, Journal of Computational Physics 475 (2023): 111874]. We validate our numerical method on several canonical problems, including the capillary wave and single bubble rise problems. We also present a comprehensive convergence study to investigate the effect of mesh resolution, time-step, and interfacial thickness on droplet-shape oscillations. We further demonstrate the robustness of our framework by successfully simulating three distinct physical systems with extremely large density ratios ($10^4$-$10^5:1$), achieving results that have not been previously reported in the literature.

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Cited by 2 Pith papers

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  1. MPFBench: A Large Scale Dataset for SciML of Multi-Phase-Flows: Droplet and Bubble Dynamics

    physics.flu-dyn 2025-02 conditional novelty 6.0 of 10

    MPF-Bench provides a large validated two-phase flow dataset and shows CNO outperforms other neural operators on 2D bubble dynamics.

  2. Geometry Matters: Benchmarking Scientific ML Approaches for Flow Prediction around Complex Geometries

    cs.LG 2024-12 conditional novelty 5.0 of 10

    On the FlowBench lid-driven cavity benchmark, vision-transformer foundation models outperform neural operators in data-limited regimes, but all models generalize poorly to out-of-range Reynolds numbers and geometry ge...

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