CFD modelling of soot entrainment via thermophoretic deposition and crevice flow in a diesel engine

Tan, Shin Mei; Ng, Hoon Kiat; Gan, Suyin

doi:10.1016/j.jaerosci.2013.08.007

Cited by 14 publications

(6 citation statements)

References 28 publications

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“…Discrete phase models have long been developed to understand contamination in the cleanroom and soot entrainment in the gas phase (Longest and Xi 2007; Woo et al 2012; Majlesara et al 2013; Tan et al 2013; Lee and Yook 2014). The thermophoretic force on aerosol particles in the gas phase is modeled using the equation proposed by L. Talbot et al (Talbot et al 1980), which is directly proportional to the particle diameter, gas kinematic viscosity and inversely proportional to the particle mass.…”

Section: Resultsmentioning

confidence: 99%

Measuring the Soret coefficient of nanoparticles in a dilute suspension

Chao

Öztekin

et al. 2014

J Nanopart Res

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Thermophoresis is an efficient process for the manipulation of molecules and nanoparticles due to the strong force it generates on the nanoscale. Thermophoresis is characterized by the Soret coefficient. Conventionally, the Soret coefficient of nanosized species is obtained by fitting the concentration profile under a temperature gradient at the steady state to a continuous phase model. However, when the number density of the target is ultralow and the dispersed species cannot be treated as a continuous phase, the bulk concentration fluctuates spatially, preventing extraction of temperature-gradient induced concentration profile. The present work demonstrates a strategy to tackle this problem by superimposing snapshots of nanoparticle distribution. The resulting image is suitable for the extraction of the Soret coefficient through the conventional data fitting method. The strategy is first tested through a discrete phase model that illustrates the spatial fluctuation of the nanoparticle concentration in a dilute suspension in response to the temperature gradient. By superimposing snapshots of the stochastic distribution, a thermophoretic depletion profile with low standard error is constructed, indicative of the Soret coefficient. Next, confocal analysis of nanoparticle distribution in response to a temperature gradient is performed using polystyrene nanobeads down to 1e-5% (v/v). The experimental results also reveal that superimposing enhances the accuracy of extracted Soret coefficient. The critical particle number density in the superimposed image for predicting the Soret coefficient is hypothesized to depend on the spatial resolution of the image. This study also demonstrates that the discrete phase model is an effective tool to study particle migration under thermophoresis in the liquid phase.

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Section: Resultsmentioning

confidence: 99%

Measuring the Soret coefficient of nanoparticles in a dilute suspension

Chao

Öztekin

et al. 2014

J Nanopart Res

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“…A combination of methyl butanoate (MB) and n-heptane reaction mechanisms by [15] is utilised as a surrogate fuel model to represent the chemical kinetics of the biodiesel fuel, which is henceforth referred to as the Combined Biodiesel Surrogate (CBS). MB is used to represent the oxygenated content of the FAME while n-heptane represents the straight chain HC.…”

Section: Chemistry and Liquid Properties Of Surrogate Fuel Parametersmentioning

confidence: 99%

“…Prior to setting the composition of the simulated fuel, the fatty acids compositions of SME, PME and CME biodiesels are compiled [16][17][18][19]. The properties of the biodiesel fuels such as the oxygen content, chemical composition and molecular mass are then determined [15]. In the computation, the chemistry of the biodiesel fuel is represented by the combination of the MB and n-heptane mechanisms while maintaining the oxygen content in the fuel composition.…”

Section: Chemistry and Liquid Properties Of Surrogate Fuel Parametersmentioning

confidence: 99%

Numerical Studies of In-Cylinder Combustion and Soot Emission Characteristics of Biodiesel Fuels from Different Feedstock

Tan

Gan

2020

ARFMTS

Self Cite

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“…Furthermore, modern computer programs nowadays have been developed with high environment. Examples given here are the researchers who investigated the emissions characteristics but with various engine parameters using different computer software such as KIVA [26][27][28][29][30][31][32], Star-CD [33][34][35], open foam CFD [36,37], AVL FIRE [38,39], Converge CFD [40,41] and also ANSYS Fluent [42][43][44][45]. Although there are many investigations conducted through several simulation software, the results reporting on soot mass concentration are varied.…”

Section: Introductionmentioning

confidence: 99%

Numerical investigation of soot mass concentration in compression ignition diesel engine

Ibrahim¹,

Mahmood²,

Abdullah³

et al. 2016

J. MECH. ENG. SCI.

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Soot particles, carbon monoxide, oxides of nitrogen, oxides of sulphur, and hydrocarbon are the emissions produced from diesel engine combustion. Those emissions species are undesirable since they give detrimental impacts to the atmosphere and human well-being. Several numerical investigations conducted by various researchers provide different soot mass concentration values. As an alternative, this study was carried out to investigate the soot mass level produced by a single cylinder diesel engine, using a commercial multidimensional computational fluid dynamic software. The result obtained from simulation effort was then validated by experimental testing during the same engine condition (engine speed of 1600 rpm at 40% load). Soot mass predicted by simulation gives a value of 3.43 × 10 -8 kg at end of simulation, while measured soot mass via experimental testing gives a value of 1.52 × 10 -8 kg. Both results differ by 56% thus indicating that the simple soot model applied was not sufficient to represent the actual soot mass emitted through exhaust manifold. This leads to the conclusion that more detailed soot model is needed to make the simulation results more meaningful and comparable to the experimental testing.

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CFD modelling of soot entrainment via thermophoretic deposition and crevice flow in a diesel engine

Cited by 14 publications

References 28 publications

Measuring the Soret coefficient of nanoparticles in a dilute suspension

Measuring the Soret coefficient of nanoparticles in a dilute suspension

Numerical Studies of In-Cylinder Combustion and Soot Emission Characteristics of Biodiesel Fuels from Different Feedstock

Numerical investigation of soot mass concentration in compression ignition diesel engine

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