Tutorial: Langevin Dynamics methods for aerosol particle trajectory simulations and collision rate constant modeling

Suresh, Vikram; Gopalakrishnan, Ranganathan

doi:10.1016/j.jaerosci.2021.105746

Cited by 15 publications

(3 citation statements)

References 172 publications

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“…Future work examining other complexities of particle charging such as charge fluctuations, external electric fields, particle material (finite ε r ) and high space charge effect (wherein the nominal inter-ion separation n ∼ a p ) is necessary to improve the current state of the art. The LD methodology [95] has also been successfully applied for collision rate constant model development to describe other particles processes such as coagulation [88,102] of and vapor condensation [58,66] onto particles as well, and can be used to include stated aspects of particle charging. (b) This experimentally validated LD-based β i model (equation ( 6)) is applied to describe particle charging in flow-through plasma afterglows taking into account the spatial variation of ions and electron concentrations in the experiments performed by Sharma et al [23].…”

Section: Discussionmentioning

confidence: 99%

“…At intermediate pressures, or for intermediate Kn D , the LD methodology of Chahl and Gopalakrishnan [64] to develop a model H = H (Kn D , Ψ E , S D ) consists of using the Langevin equation [94] to describe ion trajectories near a charged grain to infer the grain-ion collision time distribution in the presence of gas molecules. The effect of neutrals on the motion of the ion is captured implicitly through the use of a neutral drag force on the ion and a stochastic Gaussian function to mimic ion thermal motion [95]. The grain-ion collision time distribution is calculated in a periodic domain by performing a statistically significant number (∼2000) of trials as described elsewhere [56-58, 64, 87].…”

Section: Scaling Analysis Of Particle Chargingmentioning

confidence: 99%

“…Both numerical methods were used between 10 ⩽ Kn D ⩽ 100 to ensure that the choice of numerical methods had no influence on the calculated H values. The details of the numerical methods and other simulation details are described elsewhere [64,95] and we present only the simulation results and a revised fit for µ (Kn D , Ψ E , S D ) here. Figure S1 (available online at stacks.iop.org/JPD/54/ 275205/mmedia), SI presents the variation of µ (Kn D , Ψ E , S D ) for Ψ E = 60, 100, 200, 300 and for S D = 20, 100, ∞.…”

Section: Scaling Analysis Of Particle Chargingmentioning

confidence: 99%

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Modeling nanoparticle charge distribution in the afterglow of non-thermal plasmas and comparison with measurements

Suresh¹,

Li²,

Felipe³

et al. 2021

J. Phys. D: Appl. Phys.

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Particle charging in the afterglows of non-thermal plasmas typically take place in a non-neutral space charge environment. We model the same by incorporating particle–ion collision rate constant models, developed in prior work by analyzing particle–ion trajectories calculated using Langevin Dynamics (LD) simulations, into species transport equations for ions, electrons and charged particles in the afterglow. A scaling analysis of particle charging and additional LD calculations of the particle–ion collision rate constant are presented to extend the range of applicability to ion electrostatic to thermal energy ratios of 300 and diffusive Knudsen number (that scales inversely with gas pressure) up to 2000. The developed collision rate constant models are first validated by comparing predictions of particle charge against measured values in a stationary, non-thermal DC plasma from past PK-4 campaigns published in Ratynskaia et al (2004 Phys. Rev. Lett. 93 085001) and Khrapak et al (2005 Phys. Rev. E 72 016406). The comparisons reveal excellent agreement within ± 35 % for particles of radius 0.6 , 1.0 , 1.3 μ m in the gas pressure range of ∼ 20 − 150 Pa . The experiments to probe particle charge distributions by Sharma et al (2020 J. Physics D: Appl. Phys. 53 245204) are modeled using the validated particle–ion collision rate constant models and the calculated charge fractions are compared with measurements. The comparisons reveal that the ion/electron concentration and gas temperature in the afterglow critically influence the particle charge and the predictions are generally in qualitative agreement with the measurements. Along with critical assessment of the modeling assumptions, several recommendations are presented for future experimental design to probe charging in afterglows.

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