Capture of Automotive Particulate Matter in Open Substrates

Article pubs.acs.org/IECR

Capture of Automotive Particulate Matter in Open Substrates Jonas Sjöblom* and Henrik Ström Department of Applied Mechanics, Chalmers University of Technology, SE 412 96 Gothenburg, Sweden ABSTRACT: Open filters (with low pressure drop) have potential for energy-efficient reduction of particulate matter (PM) from engines. In the work reported here, the capture efficiency of PM in open substrates has been investigated using PM from a real engine under various flow conditions and sampling settings. The observed capture efficiency (CE) confirmed the expected trends that increased residence time and increased temperature give better CE. However, the volatile content (assumed to be hydrocarbons, HC) can increase the apparent CE due to rapid evaporation and/or shrinkage of the PM. In order to quantify these effects, a conceptual model has been implemented that can be used as an in situ analyzer of the PM properties. The results show how exhaust treatment (heating and/or dilution) changes the characteristics of the PM. These properties affect CE and can be used for subsequent catalyst optimization. In addition, the method developed here was used to analyze nucleation-mode PM from a special fuel injection strategy. The results revealed that these particles were mainly nonvolatiles, demonstrating the usefulness of this characterization methodology. Furthermore, an equation for diffusion losses in the rotary dilutor for the DMS500 is presented.

■

carbons from the lubrication oil in many cases.7 The use of a diesel oxidation catalyst (DOC) in the after-treatment system will not only serve as an oxidizer for CO, NO, and gas-phase hydrocarbons (HC) but also as a means to reduce (capture and oxidize) a fraction of the smaller PM.8 (Indeed, a DOC can be applied to pretreat PM prior to measurements,5,9 as discussed more below.) Therefore, it is very important to characterize open substrates, both for gas-phase components as well as for PM reduction (and transformation). The PSD of the particulate matter from an internal combustion engine is prone to change (via changes in gas composition and temperature) and it is therefore extremely challenging to characterize.10 A conceptual picture of PM is shown in Figure 1. The smaller fraction (nucleation mode) contains mostly condensed hydrocarbons but also small soot particles and ash. The combination of a high surface area and variations in the gas-phase conditions makes these particles susceptible to changes in size. The upstream exhaust system can induce nucleation (formation of new particles), condensation and agglomeration, which will affect the performance of downstream filters.11 Moreover, during exhaust sampling, the PM may be further transformed due to dilution, temperature change, and/or HC removal. The changes in PSD during sampling have been investigated by David et al.13 It is clear that the HC content (i.e., from lubrication oil) and the related particles that are generated must be addressed as we move to ever cleaner engines and combustion systems. In order to develop new regulatory procedures for vehicle exhaust particle measurement, an informal working group of experts (appointed by United Nations Economic Commission for Europe, World Forum for Harmonization of Vehicle

INTRODUCTION The world faces tremendous challenges to mitigate the effects from combustion emissions. Automotive vehicles will continue to contribute to emissions that affect both the environment as well as human health. Although the number of electrical modes of transportation will increase in the future, the use of chemically bound energy (i.e., liquid fuel) will continue, because of the high energy density of liquid fuels, when compared to batteries. Unfortunately, nonideal combustion results in the formation of particulate matter (PM). PM in the atmosphere has a significant impact on both global warming and ozone depletion.1 As the combustion process becomes more efficient, the formation of large particles is reduced, but the formation of smaller particles tends to increase. As the particles get smaller, respiratory deposition in the lungs increases1 and the effect of PM on human health therefore is of significant concern.2 Wall-through filters are commonly employed to reduce PM emissions.3 In such a filter, the exhaust is forced through a porous (often catalytically active) wall where the diffusion length inside the filter pores is sufficiently short to achieve high capture efficiency (CE). PM capture also occurs in open, flowthrough filters, but the CE is generally lower.4 However, in some applications (e.g., for gasoline-fueled engines), the CE using existing catalysts (which employ flow-through substrates) could be sufficient to meet PM legislation limits.5 Furthermore, the use of a flow-through filter (e.g., an oxidation catalyst) upstream from a wall-through filter (i.e., diesel particulate filter, hereafter referenced as DPF) will decrease the pressure drop buildup, since smaller particles will be captured in the upstream open filter and a narrower particle size distribution (PSD) with a shift toward larger sizes will enter the DPF.6 The PSD from diesel engines and gasoline engines (e.g., with stratified injection combustion) is usually bimodal with the larger size fraction (e.g., 50−150 nm, accumulation-mode PM) consisting mainly of soot and a smaller size fraction (5−50 nm, nucleation-mode PM) that mainly contains condensed hydro© XXXX American Chemical Society

Received: January 29, 2013 Revised: May 15, 2013 Accepted: May 30, 2013

A

dx.doi.org/10.1021/ie4004333 | Ind. Eng. Chem. Res. XXXX, XXX, XXX−XXX

Industrial & Engineering Chemistry Research

Article

Figure 1. A conceptual picture of PM. Reprinted with permission from ref 12. Copyright 1994, SAE International, Warrendale, PA.

Figure 2. The engine and the Emission After-Treatment System (EATS) setup. Legend: EO, engine out; BH, before heater; BS, before substrate; AS, after substrate; and SP, switch point.

Regulations, WP.29), has decided on a Particulate Measurement Program (PMP).14 It is important to note that measurement of PM for regulatory purposes requires a series of dilutions with HC removal for the sake of reproducibility and accuracy.15 The measurement objectives in this study are different, compared to regulatory use, since we want to be able to quantify and characterize the PM in situ as it reaches the catalyst. Thus, since PM is subject to transformation starting from its creation in the cylinder until it is measured at the detector, it is important to have a thorough understanding of all the related phenomena in order to draw conclusions at each relevant stage. The objective of this study is to understand capture phenomena in open substrates by varying the nature of the PM, the substrate conditions, and the substrate geometry. The size-dependent CE is evaluated and compared with numerical simulations, showing how the various processes influence the capture results. First, the experimental setup is described

together with the theoretical equations that correspond to the expected results. The experiments then are compared with theoretical calculations and the necessity of including additional phenomena becomes apparent. Next, a model for HC evaporation and particle shrinkage is introduced as a plausible explanation of the observations. Finally, the results from the additional model are discussed and some tentative conclusions are drawn.

■

EXPERIMENTAL SECTION

The Engine and the Emission After-Treatment System (EATS). The experimental setup is displayed in Figure 2. The engine is a Volvo D5 diesel engine with five cylinders and a displacement volume of 2.4 L. The compression ratio is 17.3:1, it has a VNT charging system, a common rail injection system (1600 bar), and the close-coupled catalyst removed. Low-sulfur diesel fuel (Swedish MK1) was used throughout the study. The engine was run at two constant load points (low speed, low B



Article

loads, case A and B) to produce both accumulation mode and nucleation mode PM. Additional experiments were performed where nucleation mode PM was produced by turning off the fuel injection in four out of five cylinders16 (case C). The exhaust gas was led through a long pipe (7 m, including two mufflers) where the gas was cooled before being let into the Emission After-Treatment System (EATS). Thus, the cooler indicated in Figure 2 was never used in this study. The residence times for the experiments are given in Table A1 in the Appendix. The EATS is described in detail in ref 17; it essentially provides the means to create systematic variation of the emissions with respect to residence time, temperature, and gas-phase compositions. A portion of the flow was let into the EATS by inducing pressure drops using the ball valves. The resulting flow was quantified using the pressure drop correlation from Ekström et al.18 To further study the effect of residence time and dilution effect, dry pressurized air was occasionally added by means of mass-flow controllers, and the exhaust mixture was then heated to the desired temperature before entering the sampling section. The sampling section was a straight pipe to ensure an even mass flow. The pressure drop over the substrate was enough to create an almost-uniform flow distribution. The walls were insulated with glass wool (2 cm thick) and a protective insulation mat. The sampling probes were end-plugged 6-mm-diameter stainless steel tubing with 16 holes 1 mm in diameter, covering a 5-cm sampling section from the pipe center to the periphery. The temperature was measured upstream, downstream, and inside the substrate, covering five positions at different axial and radial positions in order to monitor the radial temperature gradients. Substrates. In this study, only inactive substrates were used, in order to minimize interfering phenomena that could occur in the presence of an active catalyst. Two cordierite monoliths (5.66 in. × 6 in.) from Corning, Inc. were studied: one bare substrate and one coated with 6% w/w alumina. The uncoated monolith had square channels with a (hydraulic) diameter of 1.12 mm and the coated monolith was treated as having circular channels with a diameter of 1.09 mm. Gas and PM Measurements. Gas-phase concentrations were monitored by chemiluminescence (NOX), flame ionization detection (FID) (HC), and infrared (IR) analysis (CO, CO2). Particle size distributions (PSDs) were measured with a fast scanning instrument from Cambustion (DMS500). To determine the CE, alternating sampling from two heated lines (using a pneumatic three-way switch) was performed, and the sampling line to the DMS500 was connected after this switch. The sampling line to the DMS500 had a primary dilutor and a cyclone to remove any very large particles (>1 μm) followed by a restrictor to enable a pressure of 0.25 bar in the sampling line. The sampling line temperature was kept at 70 °C in accordance with the instructions from the manufacturer. The gas-phase concentration of CO2 was used, together with the addition of air to calculate the water concentration and the dew point for the conditions inside the instrument. The primary dilution was always kept high enough to prevent water condensation inside the instrument. Secondary dilution was applied by means of a rotating disk19 and adjusted to fit the dynamic range of the detector. The PM was charged by a corona charger and separated by charge/drag and detected by circular electrodes. The flow through the substrate was kept relatively low to enable high CE. This mode of operation does not affect the mechanisms of particle motion and deposition in the system6

and therefore offers a means to address real issues in a systematic way. Evaluated Experiments and Dimensional Analysis of the Particle Capture Process. Only a few engine load points (all low loads) were used in this study. Many experiments with systematic variation of flow, temperatures, and dilution settings were performed. Only a few representative sets of experiments are reported, and the details are given in Table A1 in the Appendix. The experimental conditions under which a specific set of results were obtained are most easily labeled using the appropriate dimensionless quantities. In the absence of significant simultaneous heat transfer, as in the present experiments, a dimensional analysis of the convective transport of dispersed particles to the walls of a monolith channel produces three governing dimensionless numbers: the Sherwood number (Sh), the Reynolds number (Re), and the Schmidt number (Sc). Of these, Sh characterizes the capture efficiency (and is hence related to the experimental observation), whereas Sc is only a function of the fluid and particle properties. The effects of the varying experimental conditions (e.g., velocity and temperature) are therefore best represented by Re, which is consequently used as the most relevant label of the experimental conditions throughout this section. Data Evaluation Methods. To confirm steady-state conditions, both PSD and temperature measurements were used. By the use of pneumatic valves, sampling was done repeatedly before and after the substrate to obtain a timeaveraged value of the capture efficiency. The numbers of data points are given in Table A1 in the Appendix. The capture efficiency (CE) is defined as CE =

PSDbefore − PSDafter PSDbefore

(1)

where PSDbefore and PSDafter are the time-averaged particle size distributions upstream and downstream of the substrate, respectively. In order to obtain an estimate of the uncertainty of the CE (using the measured quantities PSDbefore and PSDafter), it is noted that direct calculation of the standard deviation is not possible due to the denominator (division by zero). Therefore, the expression for CE was reformulated as the product of two quantities, X and Y: CE = (PSDbefore − PSDafter ) ×

1 =X×Y PSDbefore

(2)

The variances of these quantities were calculated assuming equal noise level at both positions (before and after) and the variance of Y was calculated using the reciprocal of the measured values. The measure of uncertainty (of CE) was then calculated as ε = tN − 1,95% X2 var(Y ) + Y 2 var(X ) + var(X ) × var(Y ) (3)

A Theoretical Model for Capture Efficiency. To be able to assess and interpret the experimentally determined capture efficiencies, they will be compared to the predictions from a theoretical model. In an open substrate with straight channels, and in the absence of large (≥1 μm) particles, Brownian diffusion is the dominating deposition mechanism.20 Consequently, CE can be approximated using21 C



Article

Figure 3. Schematic picture of the dilution system in the DMS500.24 Reprinted from Journal of Aerosol Science, Vol 38, J.P.R. Symonds et. al., Diesel soot mass calculation in real-time with a differential mobility spectrometer, p52−68, Copyright (2007), with permission from Elsevier.

⎛ −h A ⎞ CEchannel = (1 − PEchannel) = 1 − exp⎜ m s ⎟ ⎝ Q ⎠

Vd =

(4)

where PE is the penetration efficiency, Q the volumetric flow rate, and As the channel surface (transfer) area. The parameter hm represents the average mass-transfer coefficient: hm =

(5)

where Dp is the (size-dependent) particle diffusivity, dch the monolith channel diameter, and Sh the Sherwood number based on the channel diameter. Although the approach represented by eq 4 is general, it relies on the availability of an adequate Sherwood number correlation. The correlations used in this work are derived from numerical solutions to the corresponding heat-transfer problem.22 Therefore, the predictions of CE obtained using this approach should agree very well with the numerical predictions that would be obtained from a full computational fluid dynamics (CFD) calculation.6 Estimation of Diffusion Losses and Evaporation Losses. The diffusion losses in the pipes, sampling line, and secondary dilutor are typically not accounted for when measuring the PSD in engine emissions. This will never be a problem as long as the comparison is made using different experiments but at the same sample location. However, if the measurements are “differential”, i.e., evaluating the difference between two sampling positions (before and after the substrate), and especially if the dilution is varied, it may become necessary to account for the diffusion losses. When calculating diffusion losses in the pipes, eq 6 was used. PEpipe =

⎛ −4VdL ⎞ Nout = exp⎜ ⎟ Nin ⎝ d tU̅ ⎠

(7)

where ρg is the gas density and μ is the dynamic viscosity. This equation, which assumes a turbulent flow, has been evaluated by Kumar et al.23 and was found to agree with experimental observations, even though the Reynolds number (Re) in the pipe is not turbulent (typically, Re = 1700). The secondary dilutor is not a direct dilution with air. The dilution effect is obtained by transporting a portion of the gas in a rotating disk while the major part gets cleaned from particulates and mixed again with the flow from the rotating disk (see Figure 3). The residence time in the rotating disk is significant (up to 0.3 s); consequently, there is a loss of particles by diffusion to the inner walls of these secondary diluter pockets. Because of the insignificant velocity of the gas bulk inside the pockets during the transport, inertial particle impaction mechanisms are neglected. The penetration efficiency for a given particle size is then given by the expression

DpSh dch

2/3 0.04U̅ ⎛ ρg Dp ⎞ ⎜ ⎟ Re1/4 ⎝ μ ⎠

PE DMS‐2dil = 1 −

1 V

∫ X (t ) d V X0

(8)

where the time-dependent mass fraction of particles of a given size (X(t)) can be obtained from the solution to the partial differential equation: ⎛ ∂ 2X ∂X ∂ 2X ⎞ = Dp⎜ 2 + 2 ⎟ ∂t ∂y ⎠ ⎝ ∂x

(9)

The initial condition is that at t = 0, X = X0 in the entire domain, and the boundary conditions are that X = 0 on all boundaries at all times. The partial differential equation described by eq 9 is discretized on a computational grid using the commercial software ANSYS Fluent 13.0. A first-order implicit formulation is used for the term on the left-hand side, and a second-order central-differencing scheme is used for the terms on the right-

(6)

PE is the penetration efficiency, L the pipe length, dt the pipe diameter, U̅ the average linear velocity, and Vd the deposition velocity: D



Article

heating and dilution air in the EATS system, the primary dilution (which determines the extracted sample flow), and the secondary diluter. The purpose of this figure is to illustrate the variability in PSD which is used the subsequent analysis. Evaluated Experiments: Effects of Heating, Add-Air, and Sample Dilution. The main objective of the present work is to study the capture efficiency of PM in open substrates. However, the PSD that is eventually let into the substrate in these experiments is not only a function of the engine settings but is also dependent on the gas pretreatment. In many of the experiments shown here, the PSD “before the substrate” (BS) is analyzed in order to identify and qualitatively assess the effect of sample pretreatment and sampling conditions. These effects include heating upstream of the substrate, addition of air upstream of the heater, and sampling effects. The main observations from these experiments are as follows: (1) The substrate temperature (realized by the upstream heater) and the residence time between the heater and the substrate are two important factors that must be considered. The removal of condensed material on the larger particles may shift the PSD toward a smaller particle distribution. However, the effects on smaller particles are more important: When increasing the temperature (and the residence time from the heater to the substrate), the HC evaporation and the diffusion losses are larger for smaller particles and an apparent shift to larger peak size is observed with increasing temperature/decreasing flow, as seen in Figure 5.

hand side. One partial differential equation is solved for each particle size in this CFD setup. The numerical solution for this set of equations is obtained for a total duration of 1 s, using a time step of 1 ms. The values of X obtained from this simulation are used to retrieve the time- and size-resolved penetration efficiencies in the secondary diluter via eq 8. To facilitate further processing of the experimental data and avoid the use of look-up tables, the results are correlated into a function that provides an accurate estimation of the penetration efficiency as a function of the retention time and the particle size. A simple functional form is obtained using the assumptions of a one-dimensional, stagnant system with a bulk maintained at a constant concentration:1 PE DMS‐2dil = 1 −

2AW V

Dpτ π

=1−

2AW V

ατ π

(10)

where AW is the pocket wall area, V the pocket volume, and τ the residence time in the pocket. The pocket geometry (in the DMS500) is a cylinder with a diameter of 4 mm and a height of 7.5 mm. The residence time is calculated using a sample flow of 7.5 slpm, operating pressure of 0.25 bar, temperature of 30 °C, and the residence volume of 8 pockets (due to the specific design). In eq 10, the particle diffusivity was then replaced by a size-dependent vector (α) that takes into account both the effects of the specific geometry of the secondary diluter pockets and the effect of a varying bulk concentration, as well as the effective diffusivity: α=

b1 d p2

(1 + b2e−b3d p) (11)

where the parameters b were fitted to the CFD calculations (b = [b1 b2 b3] = [1.71 × 10−23; 1.05; 1.89 × 1011]). Thus, compensation for the losses in the secondary diluter is finally accomplished via the insertion of eq 11 into eq 10.

■

RESULTS AND DISCUSSION Particle Size Distributions (PSDs) from Different Engine Settings. The effect of different speed, load, and injection inhibition is shown in Figure 4. The PSDs show accumulation mode peaks at 70 nm (case A, B), nucleation mode peaks at 25 nm (case B), and the induced nucleation mode peak at 10−15 nm (case C). The peak heights depend on both engine operation mode as well as on the

Figure 5. PSD upstream from the bare cordierite substrate for different Re values. For temperatures, flows, and residence time in the heater upstream from the substrate, see Table A1 in the Appendix. The size of the error bars is 2 × std(PN).

(2) The addition of air (from mass-flow controllers) not only increases the flow (lowers the residence time and consequently lowers the diffusion losses) but also changes the gas-phase concentrations, which have an effect on the evaporation process (increases evaporation). The PSD for different amounts of add-air is displayed in Figure 6. The addition of air affected the portion of exhaust allowed into the EATS. Since the substrate pressure drop is dependent on the pressure drop over the ball valve, an addition of air only partially increased the total flow, thus reducing the portion of raw exhaust flow. This was verified by mass balances for CO2 concentrations. The formation of nucleation mode particles has been demonstrated during dilution;25

Figure 4. Engine-out measurements of the PSDs used in this study. Engine conditions are given in Table A1 in the Appendix. E



Article

account in the evaluations using eqs 6 and 7, 10 and 11, and 12. However, in the analysis of the CE, the diffusion losses in pipes and dilutors will tend to cancel each other out, since they apply independent of concentration. Results from Capture Efficiency Experiments. Comparison of CEs for Different Experimental Conditions. The capture efficiency (CE) for different flow conditions for a bare monolith substrate is shown in Figure 8.

Figure 6. PSDs before the alumina substrate for similar temperatures but different flows and different amounts of added air. For temperatures, flows and residence times see Table A1 in the Appendix. The size of the error bars are 2 × std(PN).

however, in this study, we use low-sulfur fuel, the number concentrations are relatively low, and (probably most important) the diluted mixture is heated to high temperatures after dilution, thus preventing any nucleation, even though the residence times are high. In Figure 6, the PSDs decrease in amplitude upon dilution (cases B1−B3) and decrease even further upon higher relative dilution when lowering the substrate flow (cases B2 and B3). (3) The dilution factors (primary and secondary dilution) influence the PSD via the diffusion losses. The secondary dilution was the major source of dilution losses. Since the DMS sample line was connected to a switch point (SP in Figure 2), ∼1.5 m from the sample probe position, diffusion losses could be expected. Furthermore, when scrutinizing the equipment, it was discovered that the tubing connected to “after substrate” was 1.4 m whereas the tubing connected to “before substrate” was 1.0 m. However, by applying eqs 6 and 7, a back-calculated PSD was obtained by dividing the measured PSD with the combined penetration efficiencies, PEupstr and PEdownstr, respectively.

Figure 8. Capture efficiency for different flow and temperatures for a bare cordierite substrate. Experiments (solid lines) and theory (dashed lines). Size of error bars are 2 × ε, from eq 3.

From Figure 8, it can be seen that a higher Re value (lower residence time) results in a lower CE. It is also clear from Table A1 in the Appendix that the residence time is more important than the temperature: The higher temperature for case A2 (263 °C) compared to case A3 (157 °C) is only increasing the CE slightly, whereas a reduced flow (case A1) increases the CE significantly. When comparing with the theoretical predictions, it is observed that the experimentally determined CE is much higher than what is expected from theory and this will be further elaborated later. Figure 9 illustrates the CE for different flow conditions over a coated substrate. From this figure, it is shown (similar to the

PE upstr = PEpipe1 × PE DMS‐tube × PE DMS‐2dil PEdownstr = PEpipe2 × PE DMS‐tube × PE DMS‐2dil

(12)

The penetration efficiencies (affecting the measured PSD) are displayed in Figure 7. All these effects were taken into

Figure 9. Capture efficiency for different flows induced by add-air for an alumina coated substrate. Experiments (solid lines) and theory (dashed lines). Size of error bars is 2 × ε, from eq 3.

bare substrate in Figure 8) that higher Re results in lower CE and also that the theoretical expression predicts much lower CEs than the experiments. Furthermore, the CE for the uncoated substrate (Figure 8) is higher than that for the coated substrate (Figure 9) at similar Re values, despite a larger channel diameter. This is due to the different characteristics of the PM, as will be discussed later. In one set of experiments, the engine was run without injection in four out of five cylinders (combustion only in one cylinder, cf. Figure 4). The results are shown in Figure 10.

Figure 7. Penetration efficiency for the different sampling parts (sample pipes and secondary dilutor) as well as the combined penetration efficiency for worst-case conditions in this study (1dil = 4, 2dil = 100) using eqs 6 and 10. τpipe1 = 0.09 s, τpipe2 = 0.12 s, τDMS‑tube = 0.14 s, τ2dil = 0.14 s. F



Article

CE was also calculated using eqs 4 and 5 for comparison and displayed in Table 1. Table 1. Total Capture Efficiencies Using Number-Based and Mass-Based Data Number-Based Capture Efficiencies

Figure 10. Capture efficiency for different flow conditions using small particles. Experiments (solid lines) and theory (dashed lines). Size of error bars is 2 × ε, from eq 3.

From Figure 10, it is shown (similar to Figures 8 and 9) that the residence time is the most important parameter for increasing the CE (indicated by the Re value). When comparing it with the theoretical expression, it is however seen that the differences between the experimentally determined CEs and the theoretical predictions are much smaller than in the previous cases. For example, when comparing case C3 with case B4 (having similar Re values), the theory predicts similar CE, but the experiments differ significantly. Since the main difference is the different engine operation, the difference thus relates to the occurrence of different particle characteristics. Many sensitivity analyses were performed to investigate whether experimental uncertainties are the reason for the discrepancy between the experiments and the theoretical calculations. The effects of errors in the determination of velocities were investigated by comparison of the applied pressure drop correlation with mass-flow controllers, by making mass balances during dilution as well as by CFD calculations. The effect of radial temperature distributions (temperature was measured at five different positions inside the substrates at different radial and axial positions) was investigated using both eq 4 and the CFD simulations. The effect of sampling dilution (erroneous correction for diffusion losses) was also experimentally investigated. However, it could be concluded that none of the plausible experimental errors that were suggested could even partially explain the grand differences in Figures 8 and 9. This observation, together with the fact that the CEs of certain experiments (cf. Figure 10) were much closer to the theoretical predictions, led to the conclusion that the characteristics of the PM itself could be a major explanation for the discrepancies observed. Number-Based vs Mass-Based Capture Efficiency. Since the reduction of the smallest PM poses the greatest challenge for emission control, the focus in this study is on number-based capture efficiencies. Exhaust emission legislation applies also to particulate mass, even though legislation compliance becomes easier when increasing the engine efficiency (thus producing smaller PM). The total particulate mass is more dependent on larger particulates, because it scales with the cube of the diameter. Since the CE decreases with increased particle size (in the investigated size range), the mass-based CE is lower than the number-based CE. The total CE was calculated by using the total particulate number and the total particulate mass. The capture efficiency was calculated by eq 1 but using the total number instead of the distribution. By using eqs 1−3 with time-averaged data and the standard deviations, the CE and corresponding uncertainty was calculated. The theoretical

case

experiment

theory

A1 A2 A3 B1 B2 B3 B4 C1 C2 C3

25% 7% 5% 7% 24% 21% 26% 15% 27% 54%

± ± ± ± ± ± ± ± ± ±

8% 4% 4% 5% 12% 13% 14% 13% 18% 52%

4% 3% 3% 3% 5% 8% 4% 4% 6% 6%

Mass-Based Capture Efficiencies experiment

theory

± ± ± ± ± ± ± ± ± ±

5% 3% 2% 3% 8% 8% 8% 7% 9% 28%

9% 1% 1% 1% 13% 7% −1% 5% 18% 34%

6% 13% 13% 5% 5% 9% 5% 16% 15% 11%

From Table 1, it can likewise be seen that there are significant deviations between experiments and theory, especially for the number-based CE. Development of a Conceptual Model for PM. The observed changes due to flow conditions correlate well with theory, i.e., higher residence time increases the CE. However, the drastic difference between experiments and simulations suggests that the assumption that the PM can be described as inert spheres is incorrect. Indeed, when looking at Figure 1 (in the Introduction section), particulate matter is generally considered a heterogeneous material consisting of different compounds, and this must be taken into account. There are numerous studies that confirm the HC content in smaller particles and this effect being even stronger for engines running at low loads7 (as in this study). A simple conceptual model (similar to that observed in ref 26) was therefore constructed to explain the experimental findings. This model should be able to take into account the loss of material by total evaporation (of particles that are entirely made out of HCs) as well as shrinkage (by evaporation from particles with condensed HCs on their surface). The PM displayed in Figure 11 will have a varying (sizedependent) HC content, depending on pretreatment. Tetra-

Figure 11. Conceptual model for the PM used for capture experiments. The black color represents nonvolatile component (soot), and the brown color represents the volatile component (HC).

contane (C40H82) was chosen as a model substance for the volatile component (HC) and all material properties were taken from Giechaskiel and Drossinos. 27 To describe evaporation from the PM in the substrate, the particle shrinkage was calculated as follows: G



Article

Figure 12. Particle size distribution (PSD) and the fitted hydrocarbon (HC) contribution (left) necessary to explain the experimental capture efficiency (CE) (right) for the alumina substrate. The top panel shows case B3 (add-air), and the bottom panel shows case B4 (no add-air).

d(dp) dt

=

sets of sigmoidal functions (generalized logistic function, adapted from ref 28) were applied, both in equation form:

2Mαc(p∞ − pd ) ρp NA 2πmkBT

(13)

HCdistribution = A +

where M is the molecular mass (kg/mol) of the hydrocarbon (here, assumed to be tetracontane (C40H82)), αc a condensation coefficient, p the partial pressure in the bulk and at the PM surface, NA Avogadro’s number, m the mass of one vapor molecule, and kB Boltzmann’s constant. All calculations were performed using data from ref 27. Tetracontane is a bigger molecule than any fuel HC and is even on the large side for lubrication oil. This will ensure that the simulations do not overestimate the rate of the evaporation process, and the saturation pressure (Pd) at the temperatures used in this investigation is indeed very low. However, since the bulk concentration (expressed as P∞) is close to zero (as could be expected in the monolith channel where ample surface area is available for adsorption), the evaporation will be very rapid, as shown in ref 27 and as confirmed in our calculations (not shown). The conceptual model is consequently applied in the substrate channels only. One could argue that HC evaporation could occur in the heated pipes as well; however, this effect will be much smaller, because of the smaller surface area as well as the larger hydraulic diameter in the pipe (4 mm in sample pipe, 4.7 mm in the DMS sampling tube), when compared to the substrate diameter (∼1 mm). To be able to explain the experimental capture efficiencies, two different types of particles were necessary. One type of particle was pure volatiles and the other type had a nonvolatile core (i.e., soot) with a shell of volatile HCs. To take into account the size-dependent distribution of soot and HC, two

(K − A ) (1 + Q e−B(t − Mi))1/ ν

(14)

In the equation above, the parameters A and K are the asymptotic values at smaller and larger particles, respectively; Q and v represent the “rounding” near the asymptotes, B is the maximum slope, Mi the position of the inflection point (if Q = v), and t is the dependent variable (in this case, the logarithm of the particle size, log10(Dp)). Two sets of distributions were applied: HCpart and HCfrac. HCpart is the number fraction of pure volatile particles, whereas HCfrac is the mass fraction of volatiles of the remaining particles (when HCpart was subtracted). In the parameter estimation procedure, Q was set equal to v. For HCfrac, five parameters were tuned. For HCpart, K was fixed to zero and Q (= v) was fixed to the same value as for HCfrac. This gave a total of eight (8) parameters that were tuned to fit the experimental data. The parameter tuning was performed using a nonlinear optimization function (lsqnonlin) in Matlab. To model both shrinkage and capture, a tanks-in-series model was developed by applying the local Sh value (in eq 5) and solving for 10 tanks in series and 100 time steps in each tank during evaporation (the residence time in each tank was given by the flow). The PM with a large HC content (indicated by HCfrac) will be reduced in size (as it proceeds through the channel) and, consequently, attain a different CE, because of a different diffusivity (which is a function of the particle size). It turned out, however, that evaporation was fast (as was also H



Article

Table 2. Tuned Parameter Values for HC Contribution in Eq 14 for the Experiments Displayed in Figures 12−15 HCfrac

HCfrac HCpart

HCfrac

HCpart

used for case

A

K

Q and v

B

Mi

A

B

Mi

A1 B1 B3 B4 C2

0.569 0.076 0.274 0.393 0.073

0.013 0.001 0.000 0.000 0.021

0.215 0.370 0.245 0.343 0.056

8.017 13.256 7.862 10.148 14.604

1.512 1.546 1.619 1.728 1.637

0.856 0.887 0.767 0.744 0.580

3.777 8.725 6.475 7.469 3.658

1.821 1.385 1.553 1.738 1.017

Figure 13. PSD (left) and CE (right) for the experiments with bare cordierite and without dilution. These are the same experimental results as those used in Figure 8, case A1 (Re = 2.7).

Figure 14. PSD (left) and CE (right) for the experiments with dilution and an alumina-coated substrate. These are the same experimental results as those used in Figure 9, case B1 (Re = 2.4).

found in ref 27) and, in most cases, all available HC evaporated in the first part of the channel (first tank). Note that this conceptual model is neither complete nor precise to describe the real PM. Real PM have been characterized using, e.g., TEM7,29 as nonspherical particles in accumulation mode (whereas this conceptual model treats all particles as being spherical). In addition, the type of volatiles for real PM is a wide distribution of different volatile species. These species are not only distributed at the periphery but probably also located at the interior of an accumulation mode PM. Furthermore, all these properties are mutually size-dependent, which indicates the very complex nature of real PM. However, this conceptual model should be physically sound and complex enough to fit experimental data. This would enable easier

interpretations of the discussed parameters, enhancing our understanding of the experimental system. One example using an alumina substrate where everything else is constant except the addition of air is displayed in Figure 12. In the left panels of Figures 12−15, the sigmoid functions are displayed, together with a cumulative surface plot that displays the distribution between nonvolatiles, semivolatiles (determined by HCfrac), and pure volatiles (determined by HCpart). From Figure 12, it is clear that, when adding air to the exhaust (upper panel), a portion of volatile HC is reduced by the addair alone, as seen by the smaller area for the HC part (brown color). Results from Fitted Parameters for HC Contributions. All experiments could be used to tune the parameters to fit to the I



Article

Figure 15. PSD (left) and CE (right) for the experiments with partial injections. These are the same experimental results as those used in Figure 10, case C2 (Re = 14.6).

volatile nucleation-mode particles (core particles, primary soot particles) have also been measured by varying the combustion process,30 and, indeed, in this study, the nucleation-mode particles were mainly nonvolatile (soot) particles. The use of the (noncatalytic) open filter as an indicator of the PM characteristics has thus been demonstrated. The results show (especially for undiluted experiments, cf. Figures 8 and 13) that the substrate channel serves as an important HC trap. This phenomenon has been observed previously, both as a part of an after-treatment system5,21,31 and as a pretreatment before PM measurements9,32 where volatile PM was removed more efficiently than solid (soot) PM. When quantitatively investigating the performance of PM removal in a catalyst (with a flow-through design), the presented methodology is therefore useful. The fitted HC contributions can be used as a plausible explanation for the experimental observations on CE in open filters (differential conditions). However, there are additional phenomena that must be addressed. This work is ongoing in our department and includes using synthetic PM to confirm issues of diffusion losses and thermophoretic losses, as well as details of the evaporation processes and instrumental effects such as charging effects or low-signal performance (e.g., limitof-detection issues).

data. For the sake of clarity, only one individual CE experiment from each of the previous figures (Figures 8−10) is displayed. The individual parameter values are given in Table 2. Note that confidence intervals were not calculated, since there is a large parameter correlation. Other functions (than the sigmoid function chosen here) could also be considered to describe the HC contribution. However, the results offer a plausible explanation to the experimental findings. From the left panel of Figure 13, it is clear that the HC contribution is mainly from pure volatiles. In the right panel, the experimental and fitted CEs are displayed. It can be seen how a small portion of HC (inducing a particle shrinkage) can explain the apparent increase in CE for dp > 100 nm. From Figure 14, it is clear that the amount of pure HC (HCpart) is much smaller, compared to that of case A1 (Figure 13). Only a small portion of small-sized volatile PM is needed to explained the increased CE, compared to theory. From Figure 15, it is clear that the contribution from pure volatiles is very small. These results indicate that, even though the conditions are similar to the CE experiments using fivecylinder combustion, the PM is mainly solids (soot) and not volatiles, as would usually be expected. Interpretation of the Results from the Conceptual Model. As seen in, e.g., Figure 5, the PSD before the substrate was significantly affected by diffusion losses. Moreover, by inspecting the fitted HC contribution, we can observe how an addition of air reduced the HC content of the remaining particles entering the substrate. This explains how the CE over the substrate can be so different, even though the flow conditions are not. As seen from the fitted HC distributions for the experiments at partial injection (Figure 15), the HC content is very low and the experimental CE is very close to what would be expected from the theoretical prediction. An interesting finding (although tentative) is thus that by “diluting” (or freezing) the raw emissions (in this case, with air from the noninjected cylinders), no accumulation-mode PM was observed. Initially, we expected the nucleation-mode particles to be mainly volatiles, but the analysis of CE, in conjunction with the fitting of the HC contribution, clearly shows the presence of small solid (carbonaceous) particles. In the literature, nucleationmode particles have been observed to be formed after the engine11 or to be vaporized during heat treatment,26 often classifying the nucleation-mode particles as volatiles. Non-

■

CONCLUSIONS The capture efficiency (CE) of particulate matter (PM) in open substrates has been investigated with PM from a real engine, using different flow conditions and different sampling settings. The CE confirmed the expected trends in that increased residence time gives better CE and increased temperature gives better CE. The residence time had a more dominanting effect on the CE, as also shown by the influence of the Reynolds number (Re). The evaluation of the CE at low CE values (i.e., differential conditions) revealed phenomena that would be impossible to study for normal diesel particulate filters (DPFs), which operate at high CE (i.e., integral conditions). Comparison with theoretical predictions (obtained from correlated numerical simulations) revealed that secondary (hydrocarbon, HC) effects needed to be included to explain the experimental data. A conceptual model for HC contribution was implemented, and parameters were successfully tuned to J



Article

Table A1. Experimental Conditions for the Evaluated Experiments case

figure(s)

substrate

speed [rpm]

load [Nm]

Tsubstrate [°C]

Qsubstrate [l/ min]

Re

Nbef

Naft

1dil

2dil

Add-air [l/ min]

τEO‑BH [s]

τBH‑BS [s]

τBS‑AS [s]

A1 A2 A3 B1 B2 B3 B4 C1 C2 C3

5, 8, 13 5, 8 5, 8 6, 9, 14 6, 9 6, 9, 12 6, 9, 12 10 10, 15 10

cordierite cordierite cordierite alumina alumina alumina alumina alumina alumina alumina

1200 1200 1200 1800 1800 1800 1800 1200 1200 1200

30 30 30 80 80 80 80 −25 −25 −25

222 263 157 164 163 160 154 150 151 136

66 228 210 169 49 46 39 501 286 34

2.7 8.1 10.8 8.6 2.5 2.4 2.0 26.6 15.1 1.9

185 235 62 98 77 102 88 86 64 125

197 122 110 114 94 128 57 142 59 64

1 1 1 1 1 4 4 1 1 1

100 100 100 100 100 16 16 50 50 50

0 0 0 22 22 22 0 22 22 0

2.40 1.98 2.00 0.97 1.43 1.46 1.59 1.81 1.87 2.85

1.95 0.52 0.71 0.86 3.00 3.17 3.84 0.30 0.53 4.55

1.74 0.50 0.55 0.68 2.36 2.48 2.95 0.23 0.40 3.36

the experiments. This model includes a loss of pure volatile particles and shrinkage of semivolatile particles. Although the model offers a plausible explanation for the phenomena observed, conclusive validation still remains. However, assuming that this model is applicable, a number of important conclusions could be drawn: (1) The particle size distribution (PSD) changes that are mainly caused by shrinkage and evaporation should be taken into account when simulating the catalyst reduction in open filters. (2) Via analysis of the fitted HC contribution, interferences of the PM characteristics can be made in situ (in terms of volatile content). This information would be very hard to obtain by other means. (3) The use of partial injection strategy could be a useful way to produce small PM that is suitable for kinetic studies and for the bridging between laboratory-scale soot (e.g., Printex U) and real diesel PM emissions.

■

penetration efficiencies (PEupstr and PEdownstr), using eq 12. (5) Calculate the capture efficiencies and corresponding confidence intervals, using eqs 1−3. B2. To Obtain Theoretical Capture Efficiencies. (1) Using the measured values of the pressure drop (and the temperature and the absolute pressure), calculate the channel Reynolds number (Re) and the gas velocity from the correlation.18 Also, using the substrate geometry, calculate the total flow rate. (2) Calculate the Sherwood number (Sh) from the correlations. Note: use different correlations for average Sh or local Sh values (see eq 10 or eq 17 in ref 22). Also note that Sh is dependent on size, through the particle Brownian diffusivity.33 (3) Calculate the mass-transfer coefficients using eq 5 and the size-dependent particle diffusivity. (4) Calculate the theoretical capture efficiency using eq 4.

■

APPENDIX

A. Details of the Experimental Conditions

AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected].

Table A1 describes the conditions for the experiments. For an explanation of column headings, see the Nomenclature section. For residence times (τ), also see Figure 2.

Notes

The authors declare no competing financial interest.

■

B. Steps and Notes on the Data Acquisition and Analysis for Each Case

B1. To Obtain Experimental Capture Efficiencies. (1) Confirm system steady-state conditions by repeated switching between the upstream (before) and downstream (after) positions. These positions correspond to “BS” and “AS” in Figure 2. (2) From time periods of steady-state conditions, extract PSD data corresponding to the upstream and downstream positions. The size of the data matrices are Nbef × 38 and Naft × 38, respectively (in the case of using a DMS500 with 38 bins ranging from 4 nm to 1000 nm in size). Also take individual reciprocal values and calculate the standard deviation for later use in eq 3. Since sample extraction may influence measures of pressure drop, use the downstream time periods for readings of pressure drop, absolute pressure, and temperatures. (3) Calculate average and standard deviations for all sizes. (4) Calculate the size-dependent diffusion losses in the secondary dilutor (see eqs 10 and 11), DMS sampling tube (operating at 0.25 bar; see eqs 6 and 7), and heated tubes (see eqs 6 and 7). Compensate for the total diffusion losses by dividing by the corresponding K

NOMENCLATURE α = parameter vector for secondary dilutor losses [m2 s−1] αc = condensation coefficient [-] AS = surface area (channel) [m2] Aw = area (pocket wall) [m2] CE = capture efficiency [%] Dp = particle diffusivity [m2 s−1] dch = channel diameter [m] dp = particle diameter [m] dt = tube diameter [m] ε = uncertainty measure for CE [%] hm = average mass-transfer coefficient [m s−1] kB = Boltzmann constant [J] L = length (pipe) [m] μ = dynamic viscosity [Pas] m = mass of one molecule [kg] M = molecular mass [kg/mol] Naft, Nbef = number of observations [-] Nin, Nout = number of particles Arb NA = Avogadro’s number [mol−1] p∞, pd = partial pressure in bulk, at particle surface [Pa] PE = penetration efficiency [%] dx.doi.org/10.1021/ie4004333 | Ind. Eng. Chem. Res. XXXX, XXX, XXX−XXX


Article

(13) Kittelson, D. B.; Arnold, M.; Watts, W. F. Review of Diesel Particulate Matter Sampling Methods, Final Report; University of Minnesota: Minneapolis, MN, 1999. (14) UNECE Particle Measurement Programme (PMP). https:// www2.unece.org/wiki/x/JYAm. (15) PMP_group. Conclusions on Improving Particulate Mass Measurement Procedures and New Particle Number Measurement Procedures Relative to the Requirements of the 05 Series of Amendments to Regulation No. 83; 2004. (16) Thor, M.; Andersson, I.; McKelvey, T., Modeling, Identification, and Separation of Crankshaft Dynamics in a Light-Duty Diesel Engine. SAE Techn. Pap. 2009, 2009-01-1798. (17) Sjöblom, J. Bridging the gap between lab scale and full scale catalysis experimentation. Top. Catal. 2013, 56 (1−8), 287−292. (18) Ekström, F.; Andersson, B., Pressure Drop of Monolithic Catalytic Converters, Experiments and Modeling. SAE Tech. Pap. 2002, 2002-01-1010. (19) Cambustion. Sampling Engine Exhaust with the DMS500 2008. Application Note DMS03. http://www.cambustion.com/sites/ default/files/instruments/DMS/dms03v04.pdf. (20) Ström, H.; Sasic, S.; Andersson, B. Effects of the Turbulent-toLaminar Transition in Monolithic Reactors for Automotive Pollution Control. Ind. Eng. Chem. Res. 2011, 50 (6), 3194−3205. (21) Johnson, J. E.; Kittelson, D. B. Deposition, diffusion and adsorption in the diesel oxidation catalyst. Appl. Catal., B 1996, 10 (1− 3), 117−137. (22) Tronconi, E.; Forzatti, P. Adequacy of lumped parameter models for SCR reactors with monolith structure. AIChE J. 1992, 38 (2), 201−210. (23) Kumar, P.; Fennell, P.; Symonds, J.; Britter, R. Treatment of losses of ultrafine aerosol particles in long sampling tubes during ambient measurements. Atmos. Environ. 2008, 42 (38), 8819−8826. (24) Symonds, J. P. R.; Reavell, K. S. J.; Olfert, J. S.; Campbell, B. W.; Swift, S. J. Diesel soot mass calculation in real-time with a differential mobility spectrometer. J. Aerosol Sci. 2007, 38 (1), 52−68. (25) (a) Khalek, I. A.; Kittelson, D. B.; Brear, F., Nanoparticle Growth During Dilution and Cooling of Diesel Exhaust: Experimental Investigation and Theoretical Assessment. SAE Tech. Pap. 2000, 200001-0515. (b) Wu, Y.; Clark, N.; Carder, D.; Shade, B., Nano Particulate Matter Evolution in a CFR1065 Dilution Tunnel. SAE Tech. Pap. 2009, 2009-01-2672. (26) Montajir, R. M.; Kawai, T.; Goto, Y.; Odaka, M., Thermal Conditioning of Exhaust Gas: Potential for Stabilizing Diesel NanoParticles. SAE Tech. Pap. 2005, 2005-01-0187. (27) Giechaskiel, B.; Drossinos, Y. Theoretical Investigation of Volatile Removal Efficiency of Particle Number Measurement Systems. SAE Int. J. Engines 2010, 3 (1), 1140−1151. (28) Richards, F. J. A flexible growth function for empirical use. J. Exp. Bot. 1959, 10 (29), 290−300. (29) Barone, T. L.; Lall, A. A.; Storey, J. M. E.; Mulholland, G. W.; Prikhodko, V. Y.; Frankland, J. H.; Parks, J. E.; Zachariah, M. R. SizeResolved Density Measurements of Particle Emissions from an Advanced Combustion Diesel Engine: Effect of Aggregate Morphology. Energy Fuels 2011, 25 (5), 1978−1988. (30) Benajes, J.; Novella, R.; Arthozoul, S.; Kolodziej, C. Particle Size Distribution Measurements from Early to Late Injection Timing Low Temperature Combustion in a Heavy Duty Diesel Engine. SAE Int. J. Fuels Lubr. 2010, 3 (1), 567−581. (31) Karjalainen, P.; Ronkko, T.; Lahde, T.; Rostedt, A.; Keskinen, J.; Saarikoski, S.; Aurela, M.; Hillamo, R.; Malinen, A.; Pirjola, L.; Amberla, A. Reduction of Heavy-Duty Diesel Exhaust Particle Number and Mass at Low Exhaust Temperature Driving by the DOC and the SCR. SAE Int. J. Fuels Lubr. 2012, 5 (3), 1114−1122. (32) Kittelson, D. B.; Watts, W. F.; Savstrom, J. C.; Johnson, J. P. Influence of a catalytic stripper on the response of real time aerosol instruments to diesel exhaust aerosol. J. Aerosol Sci. 2005, 36 (9), 1089−1107.

PSD = particle size distribution; PSD = dN/d log(dp) Q = volumetric flow rate [m3 s−1, dm3 min−1] Re = Reynolds number [-] ρg, ρp = density for gas, particle [kg m−3] Sh = Sherwood number [-] t = time [s] τ = residence time [s] T = temperature [K, °C] U̅ = average linear (pipe) velocity [m s−1] V = volume [m3] Vd = deposition velocity [m s−1] X = mass fraction [-] x, y = space coordinates [m] Abbreviations

AS = after substrate BH = before heater BS = before substrate CE = capture efficiency DOC = diesel oxidation catalyst DPF = diesel particulate filter EATS = Exhaust After-Treatment System EO = engine out HC = hydrocarbon PE = penetration efficiency PM = particulate matter, particulate mass PMP14 = Particulate Measurement Programme PN = particulate number PSD = particle size distribution

■

REFERENCES

(1) Hinds, W. C. Aerosol Technology. 2nd Edition; John Wiley & Sons, Inc.: New York, 1998. (2) European Environment Agency. Air Pollution in Europe 1990− 2004; EEA Report No. 2/2007, 2007. (3) Konstandopolus, A. G.; Kostoglou, M.; Vlachos, N.; Kladopoulou, E. Advances in the science and technology of diesel particulate filter simulation. Adv. Chem. Eng. 2008, 33, 213−275. (4) Knoth, J. F.; Drochner, A.; Vogel, H.; Gieshoff, J.; Kogel, M.; Pfeifer, M.; Votsmeier, M. Transport and reaction in catalytic wall-flow filters. Catal. Today 2005, 105 (3−4), 598−604. (5) Lehtoranta, K.; Matilainen, P.; Kinnunen, T. J. J.; Heikkilä, J.; Rönkkö, T.; Keskinen, J.; Murtonen, T. Diesel Particle Emission Reduction by a Particle Oxidation Catalyst. SAE Tech. Pap. 2009, 2009-01-2705. (6) Ström, H.; Sasic, S.; Andersson, B. Design of automotive flowthrough catalysts with optimized soot trapping capability. Chem. Eng. J. 2010, 165 (3), 934−945. (7) Lu, T.; Cheung, C. S.; Huang, Z. Size-Resolved Volatility, Morphology, Nanostructure, and Oxidation Characteristics of Diesel Particulate. Energy Fuels 2012, 26 (10), 6168−6176. (8) van Setten, B. A. A. L.; Makkee, M.; Moulijn, J. A. Science and technology of catalytic diesel particulate filters. Catal. Rev. 2001, 43 (4), 489−564. (9) Swanson, J.; Kittelson, D. Evaluation of thermal denuder and catalytic stripper methods for solid particle measurements. J. Aerosol Sci. 2010, 41 (12), 1113−1122. (10) Kittelson, D. B. Engines and nanoparticles: A review. J. Aerosol Sci. 1998, 29 (5−6), 575−588. (11) Ericsson, P.; Samson, A. Characterization of Particulate Emissions Propagating in the Exhaust Line for Spark Ignited Engines. SAE Tech. Pap. 2009, 2009-01-2654. (12) Johnson, J. H.; Bagley, S. T.; Gratz, L. D.; Leddy, D. G., A Review of Diesel Particulate Control Technology and Emissions Effects1992 Horning Memorial Award Lecture. SAE Int. 1994, 940233. L



Article

(33) Einstein, A. The motion of elements suspended in static liquids as claimed in the molecular kinetic theory of heat. Ann. Phys.−Berlin 1905, 17 (8), 549−560.

M


Capture of Automotive Particulate Matter in Open Substrates

Recommend Documents