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           turbulence model for the self-similar growth of the Rayleigh-Taylor and Richtmyer-Meshkov instabilities

Dimonte, Guy; Tipton, Robert

doi:10.1063/1.2219768

Cited by 130 publications

(180 citation statements)

References 62 publications

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“…The self-similarity itself does not define the specific form of the z-averaged profiles, only suggesting that these profiles are smooth functions of z/h. In particular, selfsimilarity is in principle consistent with specific parabolic predictions for the size of the dominant eddy and total kinetic energy contained in the mixing layer [17,26],…”

Section: Structure Of the Mixing Layermentioning

confidence: 74%

“…Our analysis of the mixing zone develops and ex-tends previous experimental [6,10,12] and numerical [6,13,14,15,16,17,18] observations on the subject, and it is also guided by phenomenological considerations discussed in [19]. The essence of the phenomenology, which utilizes the classical Kolmogorov-41 approach [20], can be summarized in the following statements: (i) The mixing zone width, h, and the energy containing scale, R 0 , are well separated from the viscous, η, and diffusive, r d , scales.…”

Section: Introductionmentioning

confidence: 99%

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Self-similarity and universality in Rayleigh–Taylor, Boussinesq turbulence

Vladimirova

Chertkov

2009

Physics of Fluids

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We report and discuss case study simulations of the Rayleigh-Taylor instability in the Boussinesq, incompressible regime developed to turbulence. Our main focus is on a statistical analysis of density and velocity fluctuations inside of the already developed and growing in size mixing zone. Novel observations reported in the article concern self-similarity of the velocity and density fluctuations spectra inside of the mixing zone snapshot, independence of the spectra of the horizontal slice level, and universality showing itself in a virtual independence of the internal structure of the mixing zone, measured in the re-scaled spatial units, of the initial interface perturbations.

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Section: Structure Of the Mixing Layermentioning

confidence: 74%

Section: Introductionmentioning

confidence: 99%

Self-similarity and universality in Rayleigh–Taylor, Boussinesq turbulence

Vladimirova

Chertkov

2009

Physics of Fluids

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“…Engineering models are essential for complex applications for which LES is not currently feasible. This influence of initial conditions has important implications for engineering models of RT mixing which range from simple models, such as the buoyancy-drag model of Dimonte & Schneider [15] or the kL model of Dimonte & Tipton [33], to more advanced models, such as the BHR model of Besnard et al [34], the multi-fluid model of Youngs [18,35] or the 2SFK model of Llor [36]. All of these models are used to represent mixing of miscible fluids at high Reynolds number.…”

Section: Discussionmentioning

confidence: 99%

The density ratio dependence of self-similar Rayleigh–Taylor mixing

Youngs

2013

Phil. Trans. R. Soc. A.

107

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Previous research on self-similar mixing caused by Rayleigh–Taylor (RT) instability is summarized and a recent series of high resolution large eddy simulations is described. Mesh sizes of approximately 2000 ×1000 × 1000 are used to investigate the properties of high Reynolds number self-similar RT mixing at a range of density ratios from 1.5 : 1 to 20 : 1. In some cases, mixing evolves from ‘small random perturbations’. In other cases, random long wavelength perturbations ( k −3 spectrum) are added to give self-similar mixing at an enhanced rate, more typical of that observed in experiments. The properties of the turbulent mixing zone (volume fraction distributions, turbulence kinetic energy, molecular mixing parameter, etc.) are related to the RT growth rate parameter, α . Comparisons are made with experimental data on the internal structure and the asymmetry of the mixing zone (spike distance/bubble distance). The main purpose of this series of simulations is to provide data for calibration of engineering models (e.g. Reynolds-averaged Navier–Stokes models). It is argued that the influence of initial conditions is likely to be significant in most applications and the implications of this for engineering modelling are discussed.

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“…Simulations in these areas often use subgrid-scale models [8,9] to approximate the energy transfer to and from unresolved scales. These models rely on coefficients that need to be set to provide the best description of the underlying physics.…”

Section: Introductionmentioning

confidence: 99%

Influence of the Richtmyer-Meshkov instability on the kinetic energy spectrum.

Weber

2010

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The fluctuating kinetic energy spectrum in the region near the Richtmyer-Meshkov instability (RMI) is experimentally investigated using particle image velocimetry (PIV). The velocity field is measured at a high spatial resolution in the light gas to observe the effects of turbulence production and dissipation. It is found that the RMI acts as a source of turbulence production near the unstable interface, where energy is transferred from the scales of the perturbation to smaller scales until dissipation. The interface also has an effect on the kinetic energy spectrum farther away by means of the distorted reflected shock wave. The energy spectrum far from the interface initially has a higher energy content than that of similar experiments with a flat interface. These differences are quick to disappear as dissipation dominates the flow far from the interface.4 5

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K - L turbulence model for the self-similar growth of the Rayleigh-Taylor and Richtmyer-Meshkov instabilities

Cited by 130 publications

References 62 publications

Self-similarity and universality in Rayleigh–Taylor, Boussinesq turbulence

Self-similarity and universality in Rayleigh–Taylor, Boussinesq turbulence

The density ratio dependence of self-similar Rayleigh–Taylor mixing

Influence of the Richtmyer-Meshkov instability on the kinetic energy spectrum.

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