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Compressible N-phase fluid mixture models
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Fluid mixture models are essential for describing a wide range of physical phenomena, including wave dynamics and spinodal decomposition. However, there is a lack of consensus in the modeling of compressible mixtures, with limited connections between different classes of models. On the one hand, existing compressible two-phase flow models accurately describe wave dynamics, but do not incorporate phase separation mechanisms. On the other hand, phase-field technology in fluid dynamics consists of models incorporating spinodal decomposition, however, a general phase-field theory for compressible mixtures remains largely undeveloped. In this paper, we take an initial step toward bridging the gap between compressible two-phase flow models and phase-field models by developing a theory for compressible, isothermal N-phase mixtures. Our theory establishes a system of reduced complexity by formulating N mass balance laws alongside a single momentum balance law, thereby naturally extending the Navier-Stokes Korteweg model to N-phases and providing the Navier-Stokes Cahn-Hilliard/Allen-Cahn model for compressible mixtures. Key aspects of the framework include its grounding in continuum mixture theory and its preservation of thermodynamic consistency despite its reduced complexity.
Forward citations
Cited by 3 Pith papers
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Global weak solutions to a compressible Navier--Stokes/Cahn--Hilliard system with singular entropy of mixing
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An entropy-stable and kinetic energy-preserving macro-element HDG method for compressible flows
A macro-element HDG method with entropy-stable, kinetic energy-preserving fluxes satisfies a discrete entropy inequality and achieves about 2.7x speedup over standard HDG on vortex benchmarks.
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Equivariant U-Shaped Neural Operators for the Cahn-Hilliard Phase-Field Model
E-UNO, a U-shaped Fourier neural operator with a D4 equivariance loss, predicts Cahn-Hilliard microstructure evolution more accurately than FNO and UNO baselines in reported experiments.
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