A comparative study between numerical methods in simulation of cavitating bubbles

Ghahramani, Ebrahim; Arabnejad, Mohammad Hossein; Bensow, Rickard

doi:10.1016/j.ijmultiphaseflow.2018.10.010

Cited by 73 publications

(44 citation statements)

References 30 publications

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“…In addition, it can be observed that the optimal coefficients in Table 10 are significantly higher than the default empirical coefficients (50, 0.01) which underestimate the cavity vapor content and the intensity of the collapse process as discussed before. These results are in accordance with the recent works by Ghahramani, Arabnejad and Bensow (2019) and Schenke, Melissaris and van Terwisga (2019). Using different cavitation models, they found that the speed of the bubble collapse is significantly underestimated with low mass transfer coefficients.…”

Section: For Each Case Indicated Insupporting

confidence: 92%

Assessment of RANS turbulence models and Zwart cavitation model empirical coefficients for the simulation of unsteady cloud cavitation

Geng

Escaler

2019

Engineering Applications of Computational Fluid Mechanics

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The numerical simulation of unsteady cavitation flows is sensitive to the selected models and associated parameters. Consequently, three Reynolds Average Navier-Stokes (RANS) turbulence models and the Zwart cavitation model were selected to assess their performance for the simulation of cloud cavitation on 2D hydrofoils. The experimental cavitation tests from a NACA65012 hydrofoil at different hydrodynamic conditions were used as a reference to tune the modeling parameters and the experimental tests from a NACA0015 were finally used to validate them. The effects of near wall grid refinement, time step, iterations and mesh elements were also investigated. The results indicate that the Shear Stress Transport (SST) model is sensitive to near wall grid resolution which should be fine enough. Moreover, the cavitation morphology and dynamic behavior are sensitive to the selection of the Zwart empirical vaporization, F v , and condensation, F c , coefficients. Therefore, a multiple linear regression approach with the single objective of predicting the shedding frequency was carried out that permitted to find the range of coefficient values giving the most accurate results. In addition, it was observed that they provided a better prediction of the vapor volume fraction and of the instantaneous pressure pulse generated by the main cloud cavity collapse.

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Section: For Each Case Indicated Insupporting

confidence: 92%

Assessment of RANS turbulence models and Zwart cavitation model empirical coefficients for the simulation of unsteady cloud cavitation

Geng

Escaler

2019

Engineering Applications of Computational Fluid Mechanics

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“…Based on our earlier experience, using larger empirical constants leads to more satisfactory results (Ghahramani et al. 2019) and sufficiently large mass flow rates can mimic a barotropic equation of state (Schenke & van Terwisga 2017). Furthermore, it has been observed that in simulating cavitating flows, the vaporization coefficient should be large, in principle as high as possible without compromising numerical stability, to satisfy near instantaneous evaporation, while the smaller condensation coefficient allows for some retardation in the condensation (Wikstrom, Bark & Fureby 2003).…”

Section: Numerical Modelsmentioning

confidence: 99%

“…To overcome the limitations with the Eulerian model, in the current study, we develop a new hybrid solver by coupling the Eulerian model with a Lagrangian model. The Lagrangian cavitation model, which has been developed in a recent study (Ghahramani, Arabnejad & Bensow 2019), is capable of tracking individual bubbles and resolving their dynamics based on an improved localized form of the Rayleigh–Plesset equation. The model has been verified with benchmark test cases including the collapse of a single bubble and a cluster of bubbles.…”

Section: Introductionmentioning

confidence: 99%

Numerical simulation and analysis of multi-scale cavitating flows

2021

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“…For further understanding of the flow dynamics, one of the test cases of this category is simulated numerically as well. In an earlier study, Ghahramani et al (2019) showed that for the FMT based homogeneous mixture models, using larger empirical constants ( C c and C v is Eq. 8 ) leads to more satisfactory results.…”

Section: Fixed Cavity Patternmentioning

confidence: 99%

“…Ghahramani et al, 2018 ). A detailed description of different numerical models can be found in the work of Ghahramani et al (2019) . In this study a transport equation based finite mass model is used which has been shown to give satisfactory results with reasonable computational cost in earlier studies ( Asnaghi et al, 2015;Asnaghi et al, 2018;Schenke and van Terwisga, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Experimental and numerical study of cavitating flow around a surface mounted semi-circular cylinder

Ghahramani

Jahangir

Neuhauser³

et al. 2020

International Journal of Multiphase Flow

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In this paper, the cavitating flow around a bluff body is studied both experimentally and numerically. The bluff body has a finite length with semi-circular cross section and is mounted on a surface in the throat of a converging-diverging channel. This set-up creates various 3D flow structures around the body, from cavitation inception to super cavities, at high Reynolds numbers ( Re = 5 . 6 × 10 4 − 2 . 2 × 10 5 ) and low cavitation numbers ( σ = 0 . 56 − 1 . 69 ). Earlier studies have shown this flow to be erosive and the erosion pattern varies by changing the flow rate and w/o the cylinder; hence, this study is an attempt to understand different features of the cavitating flow due to the cylinder effect. In the experiments, high-speed imaging is used. Two of the test cases are investigated in more detail through numerical simulations using a homogeneous mixture model. Non-cavitating simulations have also been performed to study the effect of cavitation on the flow field. Based on the observed results, vortex shedding can have different patterns in cavitating flows. While at higher cavitation numbers the vortices are shed in a cyclic pattern, at very low cavitation numbers large fixed cavities are formed in the wake area. For mid-range cavitation numbers a transitional regime is seen in the shedding process. In addition, the vapour structures have a small effect on the flow behaviour for high cavitation numbers, while at lower cavitation numbers they have significant influence on the exerted forces on the bluff body as well as vortical structures and shedding mechanisms. Besides, at very low cavitation numbers, a reverse flow is observed that moves upstream and causes the detachment of the whole cavity from the cylinder. Such a disturbance is not seen in non-cavitating flows.

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A comparative study between numerical methods in simulation of cavitating bubbles

Cited by 73 publications

References 30 publications

Assessment of RANS turbulence models and Zwart cavitation model empirical coefficients for the simulation of unsteady cloud cavitation

Assessment of RANS turbulence models and Zwart cavitation model empirical coefficients for the simulation of unsteady cloud cavitation

Numerical simulation and analysis of multi-scale cavitating flows

Experimental and numerical study of cavitating flow around a surface mounted semi-circular cylinder

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