Molecular Dynamics Simulations of Rayleigh–taylor Instability

Moscinski, Jacek; Alda, Witold; Bubak, Marian; Dzwinel, Witold; Kitowski, Jacek; Pogoda, Marek; Yuen, David A.

doi:10.1142/9789812819444_0003

Cited by 13 publications

(12 citation statements)

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“…21 As shown in Ref. 22, also in microscale, they comprise the Rayleigh-Bénard and Kelvin-Helmholtz instability patterns. The latter accompanies all the processes connected with the counterflow motion of fluid portions, while the former occurs at the beginning of mixing.…”

Section: Modeling Of Microscale Hydrodynamic Instabilitiesmentioning

confidence: 86%

Complex fluid-dynamical phenomena modeled by large-scale molecular-dynamics simulations

Alda

Dzwinel

Kitowski

et al. 1998

Computers in Physics

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We carried out large-scale molecular-dynamics simulations of the classical Rayleigh-Taylor ͑RT͒ phenomenon in a Lennard-Jones molecular liquid. We have observed from these simulations, involving 10 6-10 7 particles, the development of hydrodynamic instabilities from two different kinds of interacting particles. A free surface is introduced by deploying an overlying void. For a box with a dimension up to about 1 m and two layers having different particle sizes, the fingering type of instability is observed as a result of oscillations caused by the gravitational field. In this gridless scheme, surface waves can be captured self-consistently. For equally sized particles, a spontaneous ''fluctuation driven'' mixing with a long start-up time is observed. These moleculardynamics results suggest the possibilities of upscaling the RT phenomenon. For conducting these numerical experiments, which require at least ϳ10 5 time steps, a single simulation would require 100-200 Tflops of massively parallel computer power.

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Section: Modeling Of Microscale Hydrodynamic Instabilitiesmentioning

confidence: 86%

Complex fluid-dynamical phenomena modeled by large-scale molecular-dynamics simulations

Alda

Dzwinel

Kitowski

et al. 1998

Computers in Physics

Self Cite

View full text Add to dashboard Cite

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“…The particles defined by mass m i , position r i , velocity v i and angular velocity ω i interact with each other via a two-body, short ranged forces given by Eqs. (3)(4)(5)(6).…”

Section: Decomposition Of Computational Boxmentioning

confidence: 99%

“…Among many parallel implementations of molecular dynamics code [1,3,5,6,29] two approaches for particle s redistribution between processors are employed.…”

Section: Decomposition Of Computational Boxmentioning

confidence: 99%

“…greater communication overhead, 2. more frequent occurrence of cache misses, than in standard implementations of MD method in multiprocessor environment (e.g., in [3,5]). …”

Section: Decomposition Of Computational Boxmentioning

confidence: 99%

“…molecular dynamics (MD), used in in large-scale simulations [1][2][3][4][5][6][7], 2. finite element methods (FEM), employed in direct numerical simulations (DNS) [8][9][10], are still too demanding and, in many cases, they are not adequate for resolving fine enough features at the mesoscale.…”

Section: Introductionmentioning

confidence: 99%

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Parallel implementation of the fluid particle model for simulating complex fluids in the mesoscale

Boryczko

Dzwinel

Yuen

2002

Concurrency and Computation

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Dissipative particle dynamics (DPD) and its generalization -the fluid particle model (FPM) -represent the "fluid particle" approach for simulating fluid-like behavior in the mesoscale. Unlike particles from molecular dynamics (MD) method, the "fluid particle" can be viewed as a "droplet" consisting of liquid molecules. In FPM, "fluid particles" interact by both central and non-central, short-range forces with conservative, dissipative and Brownian character. In comparison to MD, FPM method in 3-D requires two to three times more memory load and three times more communication overhead. Computational load per step per particle is comparable to MD due to the shorter interaction range allowed between "fluid particles" than between MD atoms. The classical linked-cells technique and decomposing the computational box into strips allow for rapid modifications of the code and for implementing non-cubic computational boxes. We show that the efficiency of the FPM code depends strongly on the number of particles simulated, geometry of the box, and the computer architecture. We give a few examples from long FPM simulations involving up to 8 million fluid particles and 32 processors. Results from FPM simulations in 3-D of the phase separation in binary fluid and dispersion of colloidal slab are presented.Scaling law for symmetric quench in phase separation has been properly reconstructed. We show also that the microstructure of dispersed fluid depends strongly on the contrast between kinematic viscosities of this fluid phase and the bulk phase. This FPM code can be applied for simulating mesoscopic flow dynamics in capillary pipes or critical flow phenomena in narrow blood vessels.

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