A new turbulent viscosity modification method based on multiphase compressibility for cavitating flows
The alteration of physical properties in cavitating flows due to phase transition presents challenges for accurately expressing turbulent viscosity in Reynolds-Averaged Navier–Stokes models. Addressing this issue is crucial for capturing cavitating flow characteristics effectively. This study introd...
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Published in | Physics of fluids (1994) Vol. 36; no. 6 |
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Format | Journal Article |
Language | English |
Published |
Melville
American Institute of Physics
01.06.2024
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Subjects | |
Online Access | Get full text |
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Summary: | The alteration of physical properties in cavitating flows due to phase transition presents challenges for accurately expressing turbulent viscosity in Reynolds-Averaged Navier–Stokes models. Addressing this issue is crucial for capturing cavitating flow characteristics effectively. This study introduces a modification to turbulent viscosity by considering the compressibility of the vapor–liquid mixture, applied within the k-omega Shear-Stress Transport (SST
k
−
ω) model framework. Simulations of cavitating flow around the Clark-Y hydrofoil, National Advisory Committee for Aeronautics 66 hydrofoil, and wedge are conducted to validate the proposed method. Results indicate that the modified model can reproduce the cavity inception, development, cutoff, and shedding processes observed in the experiment. Notably, the modification model accurately reproduces distinct cavitating flow features such as U-shaped cavities, secondary shedding, and high-pressure phenomena resulting from collapse. Moreover, the predicted time-averaged velocity, time-averaged Reynolds stress component, and dominant frequencies of pressure and phase volume fraction surpass those of the original SST
k
−
ω model, demonstrating improved performance. These findings highlight the enhanced accuracy and reliability of the proposed SST-multiphase compressibility modification
k
−
ω model for simulating cavitating flows, thus contributing to improved understanding and prediction capabilities in relevant engineering applications. |
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ISSN: | 1070-6631 1089-7666 |
DOI: | 10.1063/5.0209124 |