Estimation of Solid-liquid Interfacial Energy from Gibbs-Thomson Effect: A Molecular Dynamics Study

Shibuta, Yasushi; Suzuki, Toshio

doi:10.2355/isijinternational.51.1664

Cited by 35 publications

(20 citation statements)

References 29 publications

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“…This is in agreement with the information Table I. Thermodynamic and kinetic parameters estimated from molecular dynamics simulations [41][42][43][44][45][46][47][48][49] Element (potential) 46b Sun et al 45c Asadi et al 48d Asta et al 42e Hoyt et al 41f Hoyt et al 44g Hashimoto et al 47h Asadi et al 49i Morris 43 . in Table I, in which the kinetic coefficient of the h100i direction is larger than that of the h110i direction.…”

Section: Solidification In Large-scale Molecular Dynamics Simulationsupporting

confidence: 78%

“…The estimated values of these properties in representative papers [41][42][43][44][45][46][47][48][49] are summarized in Table I. As can be seen in the table, the kinetic coefficient of the bcc h100i orientation is slightly higher than those of the h110i and h100i orientations in general.…”

Section: Solidification In Large-scale Molecular Dynamics Simulationmentioning

confidence: 83%

“…[41][42][43][44][45][46][47][48][49] In particular, the anisotropy in the interfacial parameters is a key factor determining the morphology of dendrite structures, although there are few reliable experimental values for the degree of anisotropy in the interfacial parameters. Therefore, an important role of molecular dynamics simulations is to provide interfacial parameters estimated from atomic-scale information for use in mesoscale simulations, which is a basic concept of multiscale modeling.…”

Section: Introductionmentioning

confidence: 99%

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Solidification in a Supercomputer: From Crystal Nuclei to Dendrite Assemblages

Shibuta

Ohno

Takaki

2015

JOM

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Thanks to the recent progress in high-performance computational environments, the range of applications of computational metallurgy is expanding rapidly. In this paper, cutting-edge simulations of solidification from atomic to microstructural levels performed on a graphics processing unit (GPU) architecture are introduced with a brief introduction to advances in computational studies on solidification. In particular, million-atom molecular dynamics simulations captured the spontaneous evolution of anisotropy in a solid nucleus in an undercooled melt and homogeneous nucleation without any inducing factor, which is followed by grain growth. At the microstructural level, the quantitative phase-field model has been gaining importance as a powerful tool for predicting solidification microstructures. In this paper, the convergence behavior of simulation results obtained with this model is discussed, in detail. Such convergence ensures the reliability of results of phase-field simulations. Using the quantitative phase-field model, the competitive growth of dendrite assemblages during the directional solidification of a binary alloy bicrystal at the millimeter scale is examined by performing two-and threedimensional large-scale simulations by multi-GPU computation on the supercomputer, TSUBAME2.5. This cutting-edge approach using a GPU supercomputer is opening a new phase in computational metallurgy.

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Section: Solidification In Large-scale Molecular Dynamics Simulationsupporting

confidence: 78%

Section: Solidification In Large-scale Molecular Dynamics Simulationmentioning

confidence: 83%

Section: Introductionmentioning

confidence: 99%

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Solidification in a Supercomputer: From Crystal Nuclei to Dendrite Assemblages

Shibuta

Ohno

Takaki

2015

JOM

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“…solid-liquid interfacial energy including the anisotropy in several metals, e.g., Ni, [22][23][24][25] Cu, 23,24) Au, 26) Ag, 24,26) Al, 23,24) Pb 23,24,27) and Fe [28][29][30] as well as the model system such as hard-sphere 31) and Lennard-Jones systems. 32) Importantly, the kinetic coefficients were studied for several physical systems characterized by different interatomic potentials such as Lennard-Jones potential, 33,34) embedded atom method (EAM), 26,28,35) Finnis-Sinclair (FS) potential 30) and hard-sphere model.…”

Section: A Molecular Dynamics Study Of Partitionless Solidification Amentioning

confidence: 99%

A Molecular Dynamics Study of Partitionless Solidification and Melting of Al–Cu Alloys

et al. 2017

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“…where Γ is the Gibbs-Thompson coefficient (Γ=10 -7 mK [32]), D is the diffusion coefficient of solute in the liquid (D=10 -9 m 2 s -1 [33]), m is the slope of the liquids line, l m is the composition of the last liquid to solidify, C0 is the overall composition, tSL is the solidification time of the primary phase, and the units of 2 are m [34]. Use of Eq.…”

Section: Solidification Kineticsmentioning

confidence: 99%

Microstructural investigation of D2 tool steel during rapid solidification

2013

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Rapid solidification yields significant enhancement in mechanical properties through reduced microsegregation and the formation of metastable phases. Impulse Atomization (IA) in helium and nitrogen and Water Atomization (WA) have been utilized to produce powders of D2 tool steel of different sizes from 90-1000 µm. SEM analysis was carried out to characterize the effect of particle size and cooling gas on the microstructure. It was determined that higher cooling rates result in smaller 2 and a lower percentage of eutectic. SEM image analysis along with coarsening model were used to predict eutectic and primary phase undercooling of different particles. Small particles exhibited a higher amount of undercooling (both primary phase and eutectic undercooling). The particles exposed to a He atmosphere during atomization had a larger amount of eutectic undercooling whereas the nitrogen-cooled samples had larger dendrite spacing. The fraction of primary phase that solidified during the recalescence was then calculated based on the amount of primary phase undercooling under adiabatic conditions. In smaller particles, it was found that there was a larger amount of primary phase solidified during recalescence due to a higher amount of primary undercooling. Based on primary phase undercooling values, critical nuclei radius of austenite and assuming homogenous nucleation the number of austenite unit cells in the stable nucleus was calculated. 1-IntroductionD2 tool steels are widely used in industry owing to a combination of good wear and abrasion properties [1]. The D2 tool steel contains a high volume fraction of carbides that precipitate during the eutectic transformation. The spacing and size of these particles is key for mechanical properties as smaller particles that are closely spaced will result in improved wear resistance [2]. Carbide spacing is mainly controlled by solidification kinetics, and specifically, the secondary dendrite arm spacing of the primary (Fe-rich) phase. The use of a conventional casting process to create components will result in a large secondary dendritearm spacing and thus coarse carbides, while rapid solidification will refine the microstructure to ensure an even distribution of carbides and thus improve strength [3].It is well known that 2 is strongly influenced by cooling rate, as shown in Eq. 1 below [4], where λ2 is the secondary dendrite arm spacing, Δ is the cooling rate composed of a temperature range ∆ and a solidification time tSL, and B and n are experimentally-determined constants with units of μm (°C/s) n and dimensionless, respectively.

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Estimation of Solid-liquid Interfacial Energy from Gibbs-Thomson Effect: A Molecular Dynamics Study

Cited by 35 publications

References 29 publications

Solidification in a Supercomputer: From Crystal Nuclei to Dendrite Assemblages

Solidification in a Supercomputer: From Crystal Nuclei to Dendrite Assemblages

A Molecular Dynamics Study of Partitionless Solidification and Melting of Al–Cu Alloys

Microstructural investigation of D2 tool steel during rapid solidification

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