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Int. J. Vehicle Systems Modelling and Testing, Vol. 9, No. 2, 2014 177 Copyright © 2014 Inderscience Enterprises Ltd. Modelling the capture of gasoline engine exhaust particulate matter in three-way catalytic converters Vineeth Valsan* and Stephen Samuel Department of Mechanical Engineering and Mathematical Sciences, Oxford Brookes University, Wheatley Campus, Wheatley, Oxford OX33 1HX, UK E-mail: [email protected] E-mail: [email protected] *Corresponding author Abstract: The aim of this work was to numerically model the effect of a three-way catalytic converter on nano-scale particulate matter from a gasoline direct injection engine. The work used a deep bed filtration model to simulate and validate experimental findings. A 1.6L, gasoline direct injection, spark ignition, turbocharged and intercooled Euro IV engine was used for experimentation. The capture efficiency was experimentally determined by measuring pre and post-catalyst particulate matter numbers using a DMS-500 differential mobility spectrometer at different dilution ratio settings. Despite variable experimental results at lower engine speeds and loads, the model was capable of predicting catalytic converter particle capture efficiency in the majority of cases. This indicates that deep bed filtration theory can indeed be used to explain nano-scale particle capture within three-way catalytic converters. The results also suggest that there is no significant agglomeration of particles within the catalytic converter. This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Keywords: nano-scale; particle capture; three-way catalytic converter; deep bed filtration; aftertreatment modelling. Reference to this paper should be made as follows: Valsan, V. and Samuel, S. (2014) ‘Modelling the capture of gasoline engine exhaust particulate matter in three-way catalytic converters’, Int. J. Vehicle Systems Modelling and Testing, Vol. 9, No. 2, pp.177–192. Biographical notes: Vineeth Valsan obtained his MSc in Automotive Engineering from Oxford Brookes University in 2012. This paper was submitted as his dissertation. He is currently working as a Junior Safety Engineer at Bureau Veritas in Kuwait. Stephen Samuel is a Senior Lecturer in Thermodynamics and Internal Combustion Engines at Oxford Brookes University. Currently, he is research theme leader for advanced engines, propulsion and vehicles. His research contributions are in the field of droplet combustion, combustion and fuel economy of internal combustion engines, combustion generated pollutants, nano-scale particulate matter from gasoline direct injection engines and powertrain modelling. He teaches automotive engines and advanced powertrain engineering subjects.

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Page 1: IJVSMT090204 VALSAN

Int. J. Vehicle Systems Modelling and Testing, Vol. 9, No. 2, 2014 177

Copyright © 2014 Inderscience Enterprises Ltd.

Modelling the capture of gasoline engine exhaust particulate matter in three-way catalytic converters

Vineeth Valsan* and Stephen Samuel Department of Mechanical Engineering and Mathematical Sciences, Oxford Brookes University, Wheatley Campus, Wheatley, Oxford OX33 1HX, UK E-mail: [email protected] E-mail: [email protected] *Corresponding author

Abstract: The aim of this work was to numerically model the effect of a three-way catalytic converter on nano-scale particulate matter from a gasoline direct injection engine. The work used a deep bed filtration model to simulate and validate experimental findings. A 1.6L, gasoline direct injection, spark ignition, turbocharged and intercooled Euro IV engine was used for experimentation. The capture efficiency was experimentally determined by measuring pre and post-catalyst particulate matter numbers using a DMS-500 differential mobility spectrometer at different dilution ratio settings. Despite variable experimental results at lower engine speeds and loads, the model was capable of predicting catalytic converter particle capture efficiency in the majority of cases. This indicates that deep bed filtration theory can indeed be used to explain nano-scale particle capture within three-way catalytic converters. The results also suggest that there is no significant agglomeration of particles within the catalytic converter. This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Keywords: nano-scale; particle capture; three-way catalytic converter; deep bed filtration; aftertreatment modelling.

Reference to this paper should be made as follows: Valsan, V. and Samuel, S. (2014) ‘Modelling the capture of gasoline engine exhaust particulate matter in three-way catalytic converters’, Int. J. Vehicle Systems Modelling and Testing, Vol. 9, No. 2, pp.177–192.

Biographical notes: Vineeth Valsan obtained his MSc in Automotive Engineering from Oxford Brookes University in 2012. This paper was submitted as his dissertation. He is currently working as a Junior Safety Engineer at Bureau Veritas in Kuwait.

Stephen Samuel is a Senior Lecturer in Thermodynamics and Internal Combustion Engines at Oxford Brookes University. Currently, he is research theme leader for advanced engines, propulsion and vehicles. His research contributions are in the field of droplet combustion, combustion and fuel economy of internal combustion engines, combustion generated pollutants, nano-scale particulate matter from gasoline direct injection engines and powertrain modelling. He teaches automotive engines and advanced powertrain engineering subjects.

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178 V. Valsan and S. Samuel

1 Introduction

Until recently particulate matter (PM) emissions were considered mainly a compression ignition engine problem and PM emitted by spark ignition engines were not accounted for by emissions regulations bodies. However, with improving exhaust aftertreatment technology and emerging research into the adverse effects of combustion generated PM upon human wellbeing, legally enforceable limits on soot from gasoline engine equipped vehicles are being set. Current legislation for European gasoline vehicles (Euro V) requires PM emission from gasoline engines to be limited to 0.005 g/km but does not limit particulate numbers (PN). As of September 2014, when the new Euro VI comes into effect, all new direct injection gasoline vehicles will be expected to emit less than 6.0 × 1011 particle number count/km (EC, 2008).

Vehicle manufacturers can attempt to meet these regulations through engine calibration or through the addition of exhaust after-treatment systems. However, both these approaches are likely to come with a few drawbacks. Engine optimisation for minimal PM emission can result in increased gaseous pollutants, decreased power output, increased fuel consumption or a combination of the three. Installing hardware, in addition to existing three-way catalytic converters will significantly increase fuel consumption and reduce usable engine power by adding weight to the car and increasing back pressure in the exhaust line.

Three-way catalytic converters (TWC) that are currently in use are designed to simultaneously oxidise carbon monoxide (CO) and unburnt hydrocarbons into carbon dioxide (CO2) and reduce oxides of nitrogen (NOx) into pure nitrogen (N2) by providing a catalytic surface for the reactions to occur. They are installed on exhaust lines of gasoline engines. Until recently they were assumed to have no effect on soot. However, recent work by Whelan et al. (2010) showed that this assumption needs to be re-evaluated.

2 Background

The reduced time available for charge mixing in direct injection engines leads to charge heterogeneity and localised fuel rich regions within the combustion chamber (Cromas and Ghandi, 2005). This leads to pyrolysis – the decomposition of the fuel in inadequate levels of oxygen – of fuel leading to the formation of soot precursors. These precursors can undergo cyclisation to create aromatic rings which in turn capture alkyl groups and form polycyclic aromatic hydrocarbons (PAH) (Turns, 2006). The PAHs coagulate to form soot particles. As these soot particles travel through the flame they continue to be exposed to pyrolysing fuel and experience further growth and agglomeration. Finally, as the particles pass through an oxidising region of the flame, the soot formation process is completed.

The PM size distribution from gasoline direct injection engines has been reported to be bi-modal; the nucleation mode and the accumulation mode. Nucleation mode particles are reported to have a geometric mean diameter (GMD) between 4 and 40 nm while the accumulation mode particles range between 40 and 500 nm (Whelan et al., 2010b). This corresponds well with He et al.’s (2010) observation of peaks at 10 nm and 70 nm.

While the number of particles is dependent on engine operating speed and loading conditions, the general shape of the PM distribution curve seems not to change

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Modelling the capture of gasoline engine exhaust particulate matter 179

significantly for a given engine. The GMD of PM emissions is also dependent on engine load and speed characteristics available (Sturgess et al., 2008).

The impact of TWCs on PM is not particularly well documented. However, there appears to be agreement between the researchers on the general particulate filtration profile of the devices. Significant reduction in nucleation mode particles, the smaller of the two modes, has been reported by multiple sources (Ericsson and Samson, 2009; Chen et al., 2010; He et al., 2010; Whelan et al., 2010a). Ericsson et al. claim that the accumulation mode particles are largely unaffected is corroborated to some extent by Whelan et al.’s work who showed a slight increase in accumulation particles under most operating conditions.

The work of Whelan et al. (2010a) also showed that at low engine speeds the TWC is very efficient at removing particles smaller than 50 nm. In fact at 1,600 r/min, up to 60% of particles between 5 and 10 nm are filtered out of the exhaust by the TWC. The effect drops to about 6% or less as engine speed increases. This is accompanied by a simultaneous increase in particles of size greater than 100 nm. However, very limited details are available in the published domain that provide a theoretical background or an approach that can enable the researchers to capture the effect of TWC on engine-out PM number numerically. This is the scope of the present work.

3 Numerical simulation methodology

The present work employed deep-bed filtration theory for the investigation of the effect of a TWC on nano-scale PM from a GDI engine.

Exhaust flow within the model involves solving the Navier-Stokes equations, combining the conservation of momentum, energy and continuity equations. Plug flow (one-dimensional) is assumed implying that all flow is averaged across the flow direction. Time integration is done implicitly with the main solution variables being mass flow, pressure and total enthalpy. The system is discretised into multiple flow volumes connected by boundaries. In an implicit method the variables are solved for all sub-volumes simultaneously.

The software used for numerical simulation has existing TWC objects that can be used. While the TWC model can theoretically be used to define a continuous regeneration process in which PM is defined as pure carbon and oxidised into CO2 and CO, it does not allow PM filtration. In order to model the TWCs PM filtration capabilities, a diesel particulate filtration (DPF) model (another pre-existing object) needs to be added in series to the exhaust line.

The DPF model assumes that PM is trapped on a filter substrate wall, which can be discretised into individual layers containing spherical unit collectors. The number of layers can in effect change the characteristics of the filtration process.

A review of packed bed filtration theories identifies Brownian diffusion and direct interception as the primary collection mechanisms for particles in the size range of typical diesel aerosol (less than 1,000 nm in diameter) (Konstandopoulos and Johnson, 1989; Ohara et al., 2007). Capture through Brownian diffusion involves particles deviating from their direction of flow due to Brownian diffusion movement leading to their collection when they come in contact with the filter material. Interception collection

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180 V. Valsan and S. Samuel

occurs when particles following the flow line come in direct contact with filter material (Ohara, et al., 2007).

The filtration efficiency due to Brownian diffusion (ηD) and direct interception (ηR) combined is what is predicted by the deep bed filtration model shown below (Konstandopoulos and Johnson, 1989).

2/3( )Dη k g ε Pe−=

where

k constant

Pe i c

p

U dPeclet number

D=

dc pore diameter

Dp 13

Bp

p

k Tparticle diffusion coefficient Kn

μd= +⎡ ⎤⎣ ⎦π

kB Boltzmann’s constant

T temperature

dp particle diameter

Knp particle Knudsen number 1

pdα

ε wall porosity

Ui wupore velocity

ε=

uw exhaust velocity.

The function g(ε) determines the flow field within the medium with the capture efficiency of a single collector determined by setting g(ε) = 1.

[ ]( )

32 ( )

1.51

R R sR

g εη N

N=

+

where

3 23εs

ε−

=

pR

c

dN Interception Parameter

d= =

Both processes are predictably dependent upon the flow Reynolds number which in turn is a function of viscosity and velocity. In the case of Brownian diffusion, filtration efficiency is likely to decrease with increasing particle diameter while the opposite is true

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Modelling the capture of gasoline engine exhaust particulate matter 181

of the interception mechanism. High filtration efficiencies for smaller particles are attributed to diffusion collection while interception mechanism dominates in collection of larger particles.

The total efficiency predicted by the deep bed filtration model (η) is a combination of Brownian diffusion efficiency (ηD) and direct interception efficiency (ηR).

D R D Rη η η η η= + −

The modelling approach used in GT-Suite requires a specific diameter to be input for each particle type rather than the size spectral density curve obtained from the DMS-500 particle spectrometer. For this reason two different types of PM are defined: nucleation mode and agglomeration mode. The diameters of the two types of particles correspond to the two peaks in Figure 1: a typical size spectral density curve obtained through experimentation. In this particular example, nucleation mode particles are 13.3 nm in diameter and those in agglomeration mode are 86.6 nm. The peaks vary depending upon engine operating conditions being used. The number of particles of each mode was obtained by averaging the particle spectrum from trough to trough.

Figure 1 Typical particle number spectrum

The particle numbers were then converted into mass fractions. This requires knowledge of measured exhaust temperature, rate of fuel injection and exhaust manifold pressure and other data such as fuel-to-air ratio and turbine efficiency curves of turbocharger. It was assumed that the particles have a density of 2,000 kg/m3 (Bashirnezhad et al., 2008).

The DPF model has several adjustable parameters. These model parameters need to be adjusted in order for it to match experimental results. Three major parameters which appear to have a significant effect on capture efficiency are:

1 pore diameter

2 filter wall thickness

3 filter porosity.

An optimisation function was used to select parameters such that the predicted results are comparable with experimental data.

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182 V. Valsan and S. Samuel

The optimisation process showed that the pore diameter appears to have the greatest impact on both nucleation and agglomeration mode particles; increasing pore diameter leads to decreased capture efficiencies of both particles. The other factors showed an impact on nucleation mode particle filtration but negligible effect on agglomeration mode particles. Therefore pore diameter was given priority over the other two when running the optimisation function. Porosity and wall thickness were set to values similar to those found in typical DPFs.

Finally, the chosen parameters were:

• pore diameter = 0.63 mm

• filter wall thickness = 0.31 mm

• porosity = 0.48.

Using these parameters, model results were found to imitate experimentally determined filtration efficiencies quite well at most engine operating conditions.

4 Experimentation methodology

4.1 Experimental setup

Figure 2 is a schematic diagram of the experimental setup used with all the major components. An inline turbocharged, direct injection, spark ignition, gasoline Euro IV engine was used for experimentation. Engine specifications are given in Table 1.

Figure 2 Experimental setup

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Modelling the capture of gasoline engine exhaust particulate matter 183

Table 1 Engine specifications

Bore (mm) 77.0 Stroke (mm) 85.8 Displacement (cc) 1,598 Compression ratio 10.5:1 Rated power 132 kW @ 6,000 r/min Rated torque 240 Nm @ 4,000 r/min

A Schenck W150 eddy current dynamometer with a CADET V12 control system was used. It is capable of maintaining engine speed within ± 2 rev/min and ± 0.5 Nm. Once steady state conditions were reached at a specific engine speed and loading conditions, the exhaust was sampled.

The exhaust temperature was measured by a thermocouple located downstream of the TWC. Fuel flow rate was measured using a gravimetric fuel flow meter.

Particle number within the exhaust was then measured using the Cambustion DMS-500 particle spectrometer. Engine exhaust could be sampled at two locations: prior to the catalytic converter or ‘pre-catalyst’ and post catalytic conversion or ‘post-catalyst’. Since only one of these could be sampled by the DMS-500 at a time, the sampling source could be selected using a heated selector valve which was maintained at 190°C. The high temperature is maintained so that the change in exhaust temperature during the sampling process was minimal. After passing through the valve, the sample was pumped into the particle spectrometer.

4.2 Particle spectrum data collection

The DMS-500 particulate spectrometer provides a number/size spectrum for particles in a diameter range between 5 and 1,000 nm (Cambustion, 2012). The DMS output is a particle number concentration matrix as a function of diameter and time. The output is then averaged over the duration of measurement to yield a plot of mean concentration versus particle diameter (Xu et al., 2011) such as the one shown in Figure 1. Figure 1 shows results obtained for an engine operating condition of 2,000 rev/min and 40 Nm (pre-catalyst) with a dilution ratio of 100.

The exhaust sample was drawn into the DMS-500 through a heated line. There are two dilution stages: primary and secondary. After primary dilution – set at a constant ratio of 5 – the exhaust is passed through a cyclone filter to remove particles of diameter larger than 1000 nm. Secondary dilution then takes place. The ratio can be varied between 20 and 500. Total dilution ratio is obtained by multiplying the primary and secondary ratios.

A scroll vacuum pump finally draws the sample through an electrically conductive tube where particles are ionised. The particles then pass into the classifier column where it encounters high voltages causing them to get deflected into one of the 22 grounded electrometer rings (Cambustion, 2012). The specific ring that the particle lands in is a function of its charge and aerodynamic drag; this information can be used to determine its diameter (Price et al., 2006).

Sample dilution is done to avoid condensation and agglomeration inside the sampling line (Dementhon and Martin, 1997). This can have a major impact on the measured

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184 V. Valsan and S. Samuel

number of particles in the stream and therefore needs to be selected carefully. Volatile components, which are mostly in the nucleation mode particle size range, will be most affected by dilution. Dilution alters the saturation conditions leading to condensation or evaporation back into gaseous form (Suresh and Johnson, 2001). Increased dilution will reduce the partial pressure of these components and prevents their condensation. This was shown experimentally by Suresh and Johnson (2001) using a heavy-duty diesel engine when a clear reduction in nucleation mode particles could be seen with increased dilution.

On the other hand, the temperature of the dilution air also has a significant role to play. If the temperature of the exhaust sample is decreased by mixing with colder dilution air, the saturation pressure of the volatile particles is decreased simultaneously and this can also lead to condensation. Whelan et al. (2012) observed this effect. There were large increases in the number of nucleation mode particles with increasing dilution ratio while agglomeration mode particle number remained largely the same. Since Whelan et al.’s results were obtained using a gasoline direct injection engine under similar testing conditions to those that were used in this project, these results were considered more pertinent when analysing results.

The variation in particle numbers with changing dilution ratio starts to decline at higher dilution ratios (Whelan et al., 2012). As a result, the decision was made to use dilution ratios of 100 or greater for each test as the influence of this factor is minimal above this level. Since the primary objective of the project is to study particulate filtration capabilities of the catalytic converter, the effect of dilution ratio was assumed to be minimal as sampling conditions associated with both the pre-catalyst and post-catalyst exhaust at a given engine operating condition were identical. The repeatability of these tests over three tests carried out over three days in terms of coefficient of variance (COV) was 8.7% (Whelan et al., 2012).

This COV was used to determine the standard deviation of particle concentrations measured by experiment. This error can then be used to determine standard deviation of calculated experimental particle collection efficiency. The standard deviation of efficiency (ση) (Taylor, 1997) was then plotted as error bars on all the experimental results.

0.087COVμ

= =σ

where

σ standard deviation of sample dataset

μ mean of sample dataset

[ ] [ ][ ]

i f

i

PN PNη

PN−

=

2 2 2 2η f f i ic c= +σ σ σ

where

η experimentally determined particle collection efficiency

[PN]i particle number concentration pre-catalyst

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Modelling the capture of gasoline engine exhaust particulate matter 185

[PN]f particle number concentration post-catalyst

σi standard deviation of measured pre-catalyst particle concentration

σf standard deviation of measured post-catalyst particle concentration

ση standard deviation of calculated particle filtration efficiency.

1[ ] [ ]f

f i

ηcPN PN∂

= = −∂

2

[ ][ ] [ ]

fi

i i

PNηcPN PN∂

= =∂

Therefore,

2 2 2

2 4

[ ]

[ ] [ ]f f i

ηi i

PN

PN PN= +

σ σσ

The particle spectrometer directly measures standard deviation GMD of particles collected. This data is utilised to determine error expected in modelling results. The process is described in detail later.

5 Results and discussion

5.1 Impact of input parameters upon model results

Input parameters related to inlet exhaust conditions differ from model parameters defined in the numerical methodology section in that they are determined by experiment. The factors that do not appear to affect the filtration efficiencies are just as important as the ones that do as they provide clues to the capture mechanism occurring within the catalytic converter. One such input parameter is the actual number of particles in the inlet to the TWC. In a diesel particulate filter, efficiency can be expected to change once all the capture sites are occupied by retained particles; i.e., a soot cake begins to form. In the gasoline engine being tested however particle concentration in the exhaust was never observed to rise above 8.39 × 10–3 μg/cm3; a relatively low number. These low numbers could imply that a significant proportion of the capture sites within the TWC are never filled. Modelling results show no impact upon capture efficiency of nucleation mode particles with changing particle concentration around this magnitude. Very minute changes in efficiency were observed with agglomeration mode particles but never greater than 1%.

Since the number of particles entering the TWC is relatively low, all the model parameters associated with soot layer formation were set such that no soot cake formation is accounted for by the model. This implies that, unlike DPFs, there is no increase in pressure drop across the TWC due to soot cake build up.

The GMD of incoming particles was found to play a prominent role in determining the filtration efficiency predicted by the model. Since the diameters of particles input into the simulation model are obtained through experiment, any error in GMDs measured by

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186 V. Valsan and S. Samuel

the particle spectrometer will translate into an error in the efficiency calculated by the model.

With the three major model parameters highlighted in numerical simulation methodology section optimised to produce results that most accurately replicate experimental results, the effect of varying particle diameter was studied against filtration efficiency predicted by the model. Figure 3(a) shows efficiency variation within a typical nucleation mode particle size range and Figure 3(b) shows variation within an agglomeration mode particle size range. For this particular set of results, flow conditions were similar to those observed at 2,400 rev/min and 100 Nm.

Figure 3 Model filtration efficiency vs. particle diameter for, (a) nucleation mode (b) agglomeration mode

(a) (b)

The results clearly indicate a decreasing efficiency of particle collection with increasing diameter. Next, a similar trend was searched for in experimentally determined filtration efficiencies. Figure 4 shows experimentally determined collection efficiency of the TWC vs. particle diameters measured by the DMS-500. Error in measured efficiency is determined using the process highlighted in earlier sections while the error in particle diameter measurement is provided directly by the particle spectrometer. Only nucleation mode particles studied under varying engine operating conditions are shown as the variation of capture efficiency of agglomeration mode particles is insignificant when errors associated with measurement are factored in.

Figure 4 Experimentally determined capture efficiency of nucleation mode particles vs. particle diameter (see online version for colours)

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Modelling the capture of gasoline engine exhaust particulate matter 187

According to the deep bed filtration model, increasing particle diameter should increase filtration efficiency through direct interception but decrease collection through Brownian diffusion. Therefore, these results would suggest that Brownian diffusion is the main capture mechanism for both types of particles.

Since particle diameters are clearly the most important input parameter in determining model filtration efficiency, the error in measured particle diameters input into the model need to be carefully examined. The standard deviation in mean diameters calculated by the DMS-500 can be compared to the charts such as Figure 3 in order to estimate error in model results. Thus similar graphs were made for each engine operating condition.

5.2 Filtration efficiency results

The numerical model was run for varying engine operating conditions and the calculated efficiencies were compared to those determined experimentally. Model results error bars were plotted based on the procedure highlighted in section ‘Impact of input parameters upon model results’ while experimentally determined efficiency error was calculated using the procedure described in the section ‘Particle spectrum data collection’.

Three different engine speeds 1,600 rev/min, 2,400 rev/min and 3,200 rev/min were studied with engine load ranging from 20 Nm to 120 Nm. Figure 5 shows the model vs. experimental filtration efficiencies of nucleation mode particles.

Figure 5 Experimental vs. model filtration efficiencies of nucleation mode particles (see online version for colours)

The results corroborate previous research work that indicated that nucleation mode particles experience high filtration efficiencies while there are almost negligible effects on accumulation mode particles (Ericsson and Samson, 2009). Other claims made by past researchers such as higher filtration efficiencies at lower engine speeds cannot be verified or discarded based on these results (Whelan et al., 2010a).

Despite large fluctuations in experimental results at lower engine speeds, for the most part the figures show that within the allowances made for experimental and model errors, there appears to be agreement between the two sets of results. In case of nucleation mode particles the results match in 10 out of 14 cases. With agglomeration mode particles,

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188 V. Valsan and S. Samuel

model results only match in 8 out of 14 cases but this is probably due to the variability in experimental results with respect to these larger particles. This suggests that deep bed filtration theory, to a large extent, can be used to explain particle filtration within TWC for nucleation mode particles.

5.3 The potential for agglomeration

An interesting observation from experimental results is that at most engine speeds and loads the catalytic converter appeared to have negative collection efficiencies with respect to the larger agglomeration mode particles; i.e., the pre-catalyst number of agglomeration mode particles is lower that the post-catalyst number. There are three possible explanations for this phenomenon:

1 particles collected within the TWC from previous experiments are continuously blown out due to pressure fluctuations

2 particles being filtered are agglomerating into larger particles rather than being retained within the TWC

3 this is due to the degree of experimental repeatability.

The first possibility is discounted as it would imply that some tests will indicate significant positive filtration efficiencies of agglomeration mode particles. Actual experimental results show that this is not true; collection efficiencies of agglomeration mode particles are consistently approaching zero or negative. The second and third possibilities need careful consideration.

Taking the variance of data sets into account, which can be seen in the form of error bars in experimentally determined filtration efficiencies of agglomeration mode particles (see Figure 6), there is the likelihood that the filtration is actually just zero; i.e., there is no significant collection of particles of the larger size range at all. The experimental filtration efficiencies calculated are compiled into a single figure below.

Figure 6 Experimental filtration efficiency of agglomeration mode particles (see online version for colours)

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Modelling the capture of gasoline engine exhaust particulate matter 189

As described earlier, the variance in experimental results at lower rev/min, especially in this engine, is much greater and measurements at higher rev/min are more reliable. Therefore the theory that the actual filtration efficiency is simply closer to zero rather than negative is lent more credence by the observation that collection efficiencies at higher engine speeds and loads include zero when error bars of one standard deviation are added to the data sets.

If particles are agglomerating within the TWC, the GMD of particles collected pre-catalyst must be lower than those collected post catalyst; at least under operating conditions under which negative filtration efficiencies were observed.

A negative experimental filtration efficiency was observed in ten cases for agglomeration mode particles (see Figure 6). The pre and post-catalyst agglomeration mode particle diameters of these ten cases are plotted above in Figure 7. It can be seen that of the ten cases, only five show an increase in diameter post-catalyst compared to pre-catalyst. In the other six cases, the opposite is true. Moreover, when the measurement error (shown as error bars in Figure 7) is taken into consideration, the statistical significance of these apparent changes in GMD become even less convincing.

Figure 7 GMDs of agglomeration mode particles (see online version for colours)

Finally, the possibility that nucleation mode particles were agglomerating into particles of comparable size to agglomeration mode particles was tested using the GT-Suite model applying the principle of conservation of mass. A mathematical function that calculated the mass of the collected nucleation mode particles was created. An equal amount of agglomeration mode particles by mass was factored into collection efficiency calculations in order to determine a ‘pseudo-efficiency’ of collection of agglomeration particles. This ‘pseudo-efficiency’ would naturally be lower than the actual model calculated efficiency as it accounts for complete agglomeration of collected nucleation mode particles. However the difference between this calculated ‘pseudo-efficiency’ and actual collection efficiency was never higher than 1 × 10–3 because of the low mass of nucleation mode particles entering the TWC. It is definitely not enough to account for negative filtration efficiencies.

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190 V. Valsan and S. Samuel

Since agglomeration can be safely discounted as a cause of the measured negative efficiencies, it is most likely that it is only a result of statistical variation between measurements. Capture of agglomeration mode particles is probably negligible.

5.4 Possible causes for mismatch between model and experimental results

There can be several reasons for disagreement between model and experimental results.

1 Pore diameters, porosity, etc., are assumed to be uniform throughout the catalytic converter by the deep bed filtration model. This is not necessarily true. If a wide range of pore diameters is found within the TWC filtration efficiency can be become unpredictable. The optimisation of model parameters may also not be perfect since the inbuilt optimisation function only allows for one parameter to be solved for at a time.

2 The catalytic converter entering a regeneration mode similar to that observed in diesel particulate filters can result in large fluctuations in particulate mass and numbers. When the temperature is high enough and there is a significant amount of PM collected within the catalytic converter leading to a relatively high pressure drop across the TWC, it gets oxidised and ejected from the device. Since it has been shown that there is indeed capture of particles within the TWC, it has to be assumed that such a regeneration mode must occur periodically. If not, the particles collected within the catalytic converter will increase the back pressure within the exhaust to unacceptable levels. Since the concentrations of these particulates are low compared to those from diesel engines, regeneration modes must occur relatively infrequently; therefore the assumption that there is no regeneration used in the modelling process is still justifiable.

3 In order to convert the measured particle number concentration to mass fractions required by the model, an assumption had to be made about the density of the particles. If these densities are significantly different in reality, it could introduce some error into model results.

6 Conclusions

The primary objective of the project, which was to model the particulate capture capabilities of a three-way catalytic converter, was achieved by optimising certain parameters in an existing deep bed filtration model. Three major model parameters were identified to have a significant impact upon predicted particle capture efficiency: pore diameter, filter wall thickness and porosity. Of the three, pore diameters had the greatest impact on filtration efficiencies of both particle types. For the present experimental data it was found that using a pore diameter of 0.63 mm, filter wall thickness of 0.31 mm and porosity of 0.48, the deep bed filtration model predicts experimental TWC filtration results quite well. Therefore existing deep bed filtration models can indeed be utilised to study particle filtration characteristics of catalytic converters attached to gasoline engine exhaust lines.

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Modelling the capture of gasoline engine exhaust particulate matter 191

For the smaller nucleation mode particulates, found to be within a range of 5 nm to 20 nm in diameter, the filtration efficiencies calculated by the model were comparable to experimental results within one standard deviation for ten cases out of 14. Efficiency ranged between 20% and 60%. In the case of the agglomeration mode particles, ranging between 65 nm and 90 nm in diameter, model results fell within a single standard deviation of experimental results in 8 out of 14 cases.

The results indicate that the particle mean diameter is the most significant parameter associated with the incoming exhaust that affects particulate filtration. In case of both nucleation and agglomeration mode particles, increasing particle diameter led to decreased capture efficiency. Based on deep bed filtration theory, this implies that the particle capture is dominated by a Brownian diffusion process. Other factors such as the actual number of particles and temperatures do not appear to have a significant impact.

Experimental results appeared to show negative filtration efficiencies with regard to agglomeration mode particles under certain engine testing conditions, i.e., there were more particles of the larger size range post-catalyst compared to pre-catalyst. The possibility that this was caused by agglomeration of particles within the TWC was investigated. It was finally concluded that the capture of agglomeration model particle is probably negligible.

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spray angle on soot formation in turbulent spray flames’, Proceedings of World Academy of Science, Engineering and Technology, Vol. 31, ISSN 1307-6884.

Cambustion (2012) DMS-500 Fast Particulate Spectrometer User Manual 2.0 [online] http://www.combustion.com/products/dms500/aerosol (accessed 17 September 2012).

Chen, L., Braisher, M., Crossley, A. and Stone, R. (2010) The Influence of Ethanol Blends on Particulate Matter Emissions from Gasoline Direct Injection Engines, SAE Technical Paper.

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List of symbols and abbreviations

TWC Three-way catalytic converter DISI Direct injection spark ignition GDI Gasoline direct injection PM Particulate mass PN Particulate number PAH Poly-aromatic hydrocarbon DPF Diesel particulate filter DI Direct injection PFI Port fuel injection EGR Exhaust gas recirculation DR Dilution ratio DMS Differential mobility spectrometer