Monte Carlo simulation of aggregate morphology for simultaneous coagulation and sintering

Schmid, Hans‐Joachim; Tejwani, Saurabh; Artelt, C.; Peukert, Wolfgang

doi:10.1007/s11051-004-2161-x

Cited by 59 publications

(54 citation statements)

References 22 publications

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“…As the ratio increased, the fractal dimension started to increase from 1.8 up to 2.6. This trend was similar to previous reports (Artelt et al 2003;Schmid et al 2004). Owing to the melting point of titania and the limitation of the initial particle size on the BD simulation, the fractal dimension of fully sintered agglomerates could not be obtained, unlike in the MC simulation (Schmid et al 2004).…”

Section: Resultssupporting

confidence: 93%

“…Pairwise sintering of primary particles is assumed, even though it is not a natural occurrence (Kulkarni and Biswas 2003). In previous simulations, the structure of agglomerates during sintering was well presented with the MC simulations (Schmid et al 2004(Schmid et al , 2006Al Zaitone et al 2009). Nevertheless, pairwise sintering was used here because it reduced the computation time, and we believe it did not have much effect on the overall fractal dimension of the agglomerate.…”

Section: Simulation Methodologymentioning

confidence: 64%

“…The fractal dimension can vary with respect to the ratio of the collision and sintering characteristic times (Artelt et al 2003). The relationship between the fractal dimension of agglomerates and the characteristic times of collision, sintering, and surface growth has been obtained by performing Monte Carlo (MC) simulations (Schmid et al 2004(Schmid et al , 2006Al Zaitone et al 2009). However, these MC simulations assumed two things: a steady-state condition and monodispersed agglomerates.…”

Section: Introductionmentioning

confidence: 99%

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Coagulation Coefficient of Agglomerates with Different Fractal Dimensions

Cho

Chung

Biswas

2011

Aerosol Science and Technology

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A coagulation coefficient of agglomerates with different fractal dimensions has not been considered in the past, even though there is a possibility of variations in the fractal dimension of agglomerates at any instant. In this study, a Brownian dynamics simulation was performed with simultaneous collision and sintering, and variations in the fractal dimension of agglomerates were observed. A coagulation coefficient expression for agglomerates with two different fractal dimensions was proposed. The coagulation coefficient based on the different fractal dimensions was at most 140% higher than that based on the average fractal dimension. To determine an accurate coagulation coefficient of agglomerates, the fractal dimension of each agglomerate has to be considered.

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Section: Resultssupporting

confidence: 93%

Section: Simulation Methodologymentioning

confidence: 64%

Section: Introductionmentioning

confidence: 99%

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Coagulation Coefficient of Agglomerates with Different Fractal Dimensions

Cho

Chung

Biswas

2011

Aerosol Science and Technology

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“…Xing et al, 1997) and D f,max = 3, respectively, and has been recently approved by structure simulations (Schmid et al, 2004). D f,0 =D f ( =1) accounts for the fractal dimension at identical characteristic collision and fusion time and is assumed to be equal to the average value between the two limiting cases.…”

Section: Agglomerationmentioning

confidence: 99%

Modelling titania formation at typical industrial process conditions: effect of surface shielding and surface energy on relevant growth mechanisms

Artelt

Schmid

2006

Chemical Engineering Science

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“…On the other hand, in the stochastic models, the microstructure evolution during sintering is considered to be driven by the minimization of one or several predefined energy functionals, and is simulated using Monte Carlo methods (or coupled with finite element method) [32] [37]. Bjørk et al explored the effect of particle size distributions on microstructural evolution during sintering using a similar Monte Carlo simulation [38].…”

Section: Introductionmentioning

confidence: 99%

Modeling morphology evolution and densification during solid-state sintering via kinetic Monte Carlo simulation

Chen

Jiao³

2016

Modelling Simul. Mater. Sci. Eng.

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Microstructure control is an important subject in solid-state sintering and plays a crucial role in determining post-sintering material properties, such as strength, toughness and density, to name but a few. The preponderance of existing numerical sintering simulations model the morphology evolution and densification process driven by surface energy minimization by either dilating the particles to be sintered or using the vacancy annihilation model. Here, we develop a novel kinetic Monte Carlo model to model morphology evolution and densification during free sintering. Specifically, we derive analytically a heterogeneous densification rate of the sintering system by considering sintering stress induced mass transport. The densification of the system then is achieved by modeling the sintering stress induced mass transfer via applying effective particle displacement and grain boundary migration with an efficient two-step iterative interfacial energy minimization procedure. Coarsening is also considered in the later stages of the simulations. We show that our model can accurately capture the diffusion-induced evolution of particle morphology, including neck formation and growth, as well as realistically reproduce the overall densification of the sintered material.The computationally obtained dynamic density evolution curves for both two-particle sintering and many-particle material sintering are found in excellent agreement with the corresponding experimental master sintering curves. Our model can be utilized to control a variety of structural and physical properties of the sintered materials, such as the pore size and final material density.

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Monte Carlo simulation of aggregate morphology for simultaneous coagulation and sintering

Cited by 59 publications

References 22 publications

Coagulation Coefficient of Agglomerates with Different Fractal Dimensions

Coagulation Coefficient of Agglomerates with Different Fractal Dimensions

Modelling titania formation at typical industrial process conditions: effect of surface shielding and surface energy on relevant growth mechanisms

Modeling morphology evolution and densification during solid-state sintering via kinetic Monte Carlo simulation

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