Numerical modeling of silicon oxide particle formation and transport in a one-dimensional low-pressure chemical vapor deposition reactor

Suh, S. M.; Zachariah, M. R.; Girshick, Steven L.

doi:10.1016/s0021-8502(02)00047-2

Cited by 22 publications

(11 citation statements)

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“…The only mechanism included in this study is Brownian coagulation; other internal/external forces such as electrostatic or Van Der Waals forces are neglected (Hamaker 1937;Lifshitz 1956;Langbein 1974;Kerminen 1994;Friedlander 2000;Suh et al 2002). As a result, the source term,ω k , represents Brownian particle-particle interactions.…”

Section: Particle Transportmentioning

confidence: 99%

Effects of Turbulent Fluctuations on Nanoparticle Coagulation in Shear Flows

Garrick

2011

Aerosol Science and Technology

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The effects of fluid turbulence on the coagulation of aerosols are studied quantitatively and qualitatively. Direct numerical simulation data is used to isolate the effect of the small or subgrid-scale (SGS) particle-particle interactions on nanoparticle coagulation in three-dimensional flows. The rate of particle growth is decomposed into the contribution of the large-scales and small-scales interactions. The contribution of the small-scale interactions is presented as a function of time, space, flow dynamics, and coagulation Damköhler number. Results show that small-scale interactions act to both increase and decrease particle growth. The probability density functions (PDFs) of the SGS growth rate exhibit a negative bias, which increases with time and coagulation Damköhler number. Additionally, PDFs conditioned on the Q-criterion suggest that the contribution of the small-scale interactions primarily act to reduce particle growth in regions characterized by fluid rotation.

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Section: Particle Transportmentioning

confidence: 99%

Effects of Turbulent Fluctuations on Nanoparticle Coagulation in Shear Flows

Garrick

2011

Aerosol Science and Technology

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“…A finite rate chemistry model for the precursor chemistry is applied to model silane combustion (Suh et al 2001;Suh et al 2002). We use the mechanism provided by Suh et al (2001) including 63 species and 264 reactions at their reported rates for atmospheric pressure.…”

Section: Numerical Configurationmentioning

confidence: 99%

Sparse-Lagrangian PDF Modelling of Silica Synthesis from Silane Jets in Vitiated Co-flows with Varying Inflow Conditions

Neuber

Kronenburg

Stein

et al. 2020

Flow Turbulence Combust

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This paper presents a comparison of experimental and numerical results for a series of turbulent reacting jets where silica nanoparticles are formed and grow due to surface growth and agglomeration. We use large-eddy simulation coupled with a multiple mapping conditioning approach for the solution of the transport equation for the joint probability density function of scalar composition and particulate size distribution. The model considers inception based on finite-rate chemistry, volumetric surface growth and agglomeration. The sub-models adopted for these particulate processes are the standard ones used by the community. Validation follows the "paradigm shift" approach where elastic light scattering signals (that depend on particulate number and size), OH-and SiO-LIF signals are computed from the simulation results and compared with "raw signals" from laser diagnostics. The sensitivity towards variable boundary conditions such as co-flow temperature, Reynolds number and precursor doping of the jet is investigated. Agreement between simulation and experiments is very good for a reference case which is used to calibrate the signals. While keeping the model parameters constant, the sensitivity of the particulate size distribution on co-flow temperature is predicted satisfactorily upstream although quantitative differences with the data exist downstream for the lowest coflow temperature case that is considered. When the precursor concentration is varied, the model predicts the correct direction of the change in signal but notable qualitative and quantitative differences with the data are observed. In particular, the measured signals show a highly non-linear variation while the predictions exhibit a square dependence on precursor doping at best. So, while the results for the reference case appear to be very good, shortcomings in the standard submodels are revealed through variation of the boundary conditions. This demonstrates the importance of testing complex nanoparticle synthesis models on a flame series to ensure that the physical trends are correctly accounted for.

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“…The solution of these equations provides the hydrodynamic ÿeld. A moment method is used to represent the particle ÿeld (Lee, Chen, & Gieseke, 1984;Pratsinis, 1988;Suh et al, 2002;Settumba & Garrick, 2003). The method assumes a lognormal particle size distribution.…”

Section: Mathematical Formulationmentioning

confidence: 99%

“…where c is the mean thermal speed, the accommodation coe cient and is the uid density (Friedlander, 2000;Suh et al, 2002). The di usion term in our original work is given by…”

Section: Mathematical Formulationmentioning

confidence: 99%

“…That is, a di usion coe cient which is based on the geometric mean particle volume is used for all particles independent of size (Moody & Collins, 2003). The results are compared to simulations in which particles di use according to their size (Pratsinis & Kim, 1989;Terry, McGraw, & Rangel, 2001;Suh, Zachariah, & Girshick, 2002;Settumba & Garrick, 2003). The e ects of di usion on the mean particle size, and geometric standard deviation are investigated for two initial volume fractions.…”

Section: Introductionmentioning

confidence: 99%

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