Fick diffusion coefficients of binary fluid mixtures consisting of methane, carbon dioxide, and propane via molecular dynamics simulations based on simplified pair-specific ab initio-derived force fields

Higgoda, Ubaya A.; Kankanamge, Chathura J.; Hellmann, Robert; Koller, Thomas M.; Fröba, Andreas P.

doi:10.1016/j.fluid.2019.112257

Cited by 14 publications

(9 citation statements)

References 97 publications

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“…Here, deviations between the calculated and the experimental a values are between (5.5 and 7.0)% for the states studied along the isochore and 12.5% for the state close to the phase transition. Comparison with a D 11 value calculated by Higgoda et al with the help of MD simulations for the investigated system at T = 353.15 K and p = 2.5 MPa shows that this mutual diffusivity is only 7.6% smaller than the thermal diffusivity measured here at the same temperature. Nevertheless, the calculated Rayleigh ratio indicates that the analyzed signal should be dominated by fluctuations in temperature or entropy.…”

Section: Resultssupporting

confidence: 44%

Diffusivities of Binary Mixtures Consisting of Carbon Dioxide, Methane, and Propane by Dynamic Light Scattering

et al. 2019

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In the present contribution, diffusivities of binary mixtures of propane (C3H8), carbon dioxide (CO2), and methane (CH4) were investigated between T = (293 and 353) K, p = (0.5 and 12) MPa, and mole fractions x = (0.0833 and 0.917) by dynamic light scattering (DLS). Which diffusivities are accessible by DLS depends on the location of the thermodynamic state points studied relative to the phase envelopes of the mixtures, the mixture density, and the derivatives of the refractive index with respect to temperature and concentration. A simultaneous determination of thermal and mutual diffusivity was possible in the liquid phases and at some higher-density states in the supercritical phase. For the gaseous and most of the supercritical state points, only a thermal, mutual, or effective diffusivity could be obtained and was identified with the help of Rayleigh ratios and temperature-, pressure-, and concentration-dependent trends. The reported diffusivities clearly improve the data situation for the mixtures studied above atmospheric pressure. The average expanded uncertainty (k = 2) for all presented mutual diffusivities is 5.8%. For the thermal diffusivity, these uncertainties are 22% and 3.0% for the cases where it was determined simultaneously with the mutual diffusivity or alone.

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Section: Resultssupporting

confidence: 44%

Diffusivities of Binary Mixtures Consisting of Carbon Dioxide, Methane, and Propane by Dynamic Light Scattering

et al. 2019

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“…Many studies have focused on the computation of self-diffusivities in mixtures [86][87][88][89]. EMD has also been widely used to predict collective diffusivity in binary, ternary, and multicomponent mixtures [30,[49][50][51][52]61,[90][91][92][93][94][95][96][97][98]. To describe collective diffusion in mixtures, the Maxwell-Stefan (MS) theory and Fick's law are the most commonly used approaches [1,11,13,17,94].…”

Section: Introductionmentioning

confidence: 99%

Finite-size effects of diffusion coefficients computed from molecular dynamics: a review of what we have learned so far

Celebi

Jamali

Bardow

et al. 2020

Molecular Simulation

115

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The number of molecules used in a typical Molecular Dynamics (MD) simulations is orders of magnitude lower than in the thermodynamic limit. It is therefore essential to correct diffusivities computed from Molecular Dynamics simulations for finite-size effects. We present a comprehensive review on finitesize effects of diffusion coefficients by considering self-, Maxwell-Stefan, and Fick diffusion coefficients in pure liquids, as well as binary, ternary, and quaternary mixtures. All finite-size corrections, both analytical and empirical, are discussed in detail. The finite-size effects of rotational and confined diffusion are also briefly discussed.

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“…The infinite size diffusion values ( D ∞ ), also known as the thermodynamic limit, for each distinct LJ mixture can be obtained by plotting the D MD against the inverse of the simulation box length (1/ L ) and extrapolating to 1/ L → 0 (Figure S1). This linear extrapolation to determine the thermodynamic limit is well-accepted in the literature, ,− and we describe the process in more detail in the Supporting Information. In Figure a, we plot the difference between D ∞ and the simulated D MD (the required finite-size correction) against the YH correction for self-diffusion of the LJ particle 1 ( D 1 , self ).…”

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confidence: 79%

Machine Learning-Based Upscaling of Finite-Size Molecular Dynamics Diffusion Simulations for Binary Fluids

Leverant

Harvey

Alam

2020

J. Phys. Chem. Lett.

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Molecular diffusion coefficients calculated using molecular dynamics (MD) simulations suffer from finite-size (i.e., finite box size and finite particle number) effects. Results from finite-sized MD simulations can be upscaled to infinite simulation size by applying a correction factor. For self-diffusion of single-component fluids, this correction has been well-studied by many researchers including Yeh and Hummer (YH); for binary fluid mixtures, a modified YH correction was recently proposed for correcting MD-predicted Maxwell–Stephan (MS) diffusion rates. Here we use both empirical and machine learning methods to identify improvements to the finite-size correction factors for both self-diffusion and MS diffusion of binary Lennard-Jones (LJ) fluid mixtures. Using artificial neural networks (ANNs), the error in the corrected LJ fluid diffusion is reduced by an order of magnitude versus existing YH corrections, and the ANN models perform well for mixtures with large dissimilarities in size and interaction energies where the YH correction proves insufficient.

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Fick diffusion coefficients of binary fluid mixtures consisting of methane, carbon dioxide, and propane via molecular dynamics simulations based on simplified pair-specific ab initio-derived force fields

Cited by 14 publications

References 97 publications

Diffusivities of Binary Mixtures Consisting of Carbon Dioxide, Methane, and Propane by Dynamic Light Scattering

Diffusivities of Binary Mixtures Consisting of Carbon Dioxide, Methane, and Propane by Dynamic Light Scattering

Finite-size effects of diffusion coefficients computed from molecular dynamics: a review of what we have learned so far

Machine Learning-Based Upscaling of Finite-Size Molecular Dynamics Diffusion Simulations for Binary Fluids

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