Self-diffusion of large solid clusters in a liquid by molecular dynamics simulation

Heyes, D. M.; Nuevo, María J.; Morales, Juan J.

doi:10.1080/00268979609484532

Cited by 12 publications

(19 citation statements)

References 28 publications

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“…Previous investigations on the self-diffusion coefficient of nanoparticles in domains without the presence of a temperature gradient also indicated departure from Einstein's diffusion relationship for micron-sized particles; however, they were in agreement with the inverse proportionality relationship of the self-diffusion coefficient with the particle radius [34]. In addition, other studies with hard sphere or/and assembled nanoparticles using similar assembly models as the current investigation but without the presence of a temperature gradient indicated an overall lower self-diffusion coefficient for nanoparticles [27] compared to fluid molecules/atoms. The literature is in direct disagreement with the findings of this study concerning the activity of nanoparticles in isothermal domains and excluding thermal quantum effects.…”

Section: Formulation Of a New Type Of Heat Transfer Mechanism Valid Fsupporting

confidence: 74%

“…The third and final test involved plotting the normalised velocity autocorrelation function (VACF) of a nanoparticle and a normal argon fluid atom (more information can be found in an earlier study performed [25]). All three tests indicated good agreement with the literature [26][27][28][29][30][31][32][33][34][35].…”

Section: Code Developmentsupporting

confidence: 69%

“…1 [27]. Inside subdomain A, a crystallic structure is enforced with the atoms in that area interacting with an ε equal to 5 ND units.…”

Section: Code Developmentmentioning

confidence: 99%

“…1) is used to depict the interatomic potential energy in between a pair of liquid argon atoms in a 2D domain and is based on [26,27] while the required dynamic quantities are obtained using the leapfrog integration method.…”

Section: Code Developmentmentioning

confidence: 99%

See 3 more Smart Citations

Revealing the complex conduction heat transfer mechanism of nanofluids

Sergis¹,

Hardalupas²

2016

Nano Online

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Nanofluids are two-phase mixtures consisting of small percentages of nanoparticles (sub 1-10 %vol) inside a carrier fluid. The typical size of nanoparticles is less than 100 nm. These fluids have been exhibiting experimentally a significant increase of thermal performance compared to the corresponding carrier fluids, which cannot be explained using the classical thermodynamic theory. This study deciphers the thermal heat transfer mechanism for the conductive heat transfer mode via a molecular dynamics simulation code. The current findings are the first of their kind and conflict with the proposed theories for heat transfer propagation through micron-sized slurries and pure matter. The authors provide evidence of a complex new type of heat transfer mechanism, which explains the observed abnormal heat transfer augmentation. The new mechanism appears to unite a number of popular speculations for the thermal heat transfer mechanism employed by nanofluids as predicted by the majority of the researchers of the field into a single one. The constituents of the increased diffusivity of the nanoparticle can be attributed to mismatching of the local temperature profiles between parts of the surface of the solid and the fluid resulting in increased local thermophoretic effects. These effects affect the region surrounding the solid manifesting interfacial layer phenomena (Kapitza resistance). In this region, the activity of the fluid and the interactions between the fluid and the nanoparticle are elevated. Isotropic increased nanoparticle mobility is manifested as enhanced Brownian motion and diffusion effects

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Section: Formulation Of a New Type Of Heat Transfer Mechanism Valid Fsupporting

confidence: 74%

Section: Code Developmentsupporting

confidence: 69%

“…1 [27]. Inside subdomain A, a crystallic structure is enforced with the atoms in that area interacting with an ε equal to 5 ND units.…”

Section: Code Developmentmentioning

confidence: 99%

Section: Code Developmentmentioning

confidence: 99%

See 2 more Smart Citations

Revealing the complex conduction heat transfer mechanism of nanofluids

Sergis¹,

Hardalupas²

2016

Nano Online

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show abstract

“…This is achieved by limiting to a large extent the detail of interatomic interactions. Following the validation of this code extension, it was possible to advance to the final "solid particle" model [24] at which a more elaborate procedure is followed to assemble the nanoparticles, while the computational time for the simulations increases significantly. The final assembly routines are based on [24].…”

Section: 3 Nanoparticle Assembly and Handling Processesmentioning

confidence: 99%

Molecular Dynamic Simulations of a Simplified Nanofluid

Sergis¹,

Hardalupas²

2014

CMST

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This study describes the methodology that was developed to run a Molecular Dynamics Simulation (MDS) code to simulate the behaviour of a single nanoparticle dispersing in a fluid with a temperature gradient. A soft disk model described by the Lennard-Jones potential is used to simulate the system. The nanoparticle is assembled via the use of four subdomains of interatomic interactions and hence presents in full resolution the transfer of energy from the fluid-to-solidto-fluid subdomains. A cluster computing system (HTCondor) was used to perform a large scale deployment of the MDS code. The obtained showcase results were successfully evaluated using three widely documented tests from the associated literature (Randomness, Radial Distribution and Velocity Autocorrelation Distribution Functions). It was discovered that the nanoparticle travels a larger distance in the fluid than the distance travelled by a fluid molecule (recovery region). The findings were confirmed by calculating the Green-Kubo self-diffusivity coefficient halfway through the simulation at which an enhancement of 156% was discovered in favour of the Nanoparticle. This might be the physical mechanism responsible for the experimentally observed thermal performance enhancement in nanofluids.

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Effective particle size from molecular dynamics simulations in fluids

Welch

Rasmussen

et al. 2017

Theor. Comput. Fluid Dyn.

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We report molecular dynamics simulations designed to investigate the effective size of colloidal particles suspended in a fluid in the vicinity of a rigid wall where all interactions are defined by smooth atomic potential functions. These simulations are used to assess how the behavior of this system at the atomistic length scale compares to continuum mechanics models. In order to determine the effective size of the particles, we calculate the solvent forces on spherical particles of different radii as a function of different positions near and overlapping with the atomistically defined wall and compare them to continuum models. This procedure also then determines the effective position of the wall. Our analysis is based solely on forces that the particles sense, ensuring self-consistency of the method. The simulations were carried out using both Weeks-ChandlerAndersen and modified Lennard-Jones (LJ) potentials to identify the different contributions of simple repulsion and van der Waals attractive forces. Upon correction for behavior arising the discreteness of the atomic system, the underlying continuum physics analysis appeared to be correct down to much less than the particle radius. For both particle types, the effective radius was found to be ∼ 0.75σ , where σ defines the length scale of the force interaction (the LJ diameter). The effective "hydrodynamic" radii determined by this means are distinct from commonly assumed values of 0.5σ and 1.0σ , but agree with a value developed from the atomistic analysis of the viscosity of such systems.

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Self-diffusion of large solid clusters in a liquid by molecular dynamics simulation

Cited by 12 publications

References 28 publications

Revealing the complex conduction heat transfer mechanism of nanofluids

Revealing the complex conduction heat transfer mechanism of nanofluids

Molecular Dynamic Simulations of a Simplified Nanofluid

Effective particle size from molecular dynamics simulations in fluids

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