7th CLEERS Workshop Notes by John Hoard, Ford

June 16-17, 2004

Summary

The two day workshop was held at Detroit Diesel.  Presentations are to be posted on the CLEERS web site in the near future, http://www.cleers.org/.

 

DPFs are being modeled and tested.  The deposition of soot in a filter is controlled by the Peclet number – the ratio of inertial to diffusion forces on a particle.  Different operating characteristics change the penetration into the filter wall, and the density/permeability of the soot cake. The process of understanding DPFs is very rapid, and many papers are being published.  If we already understood it, there would be few papers!

 

The pressure drop, filtration, and wash coating capability are strong functions of DPF pore size and size distribution, as well as cell density and wall thickness.  These same parameters also affect the strength.  Substrates suitable for catalyst coating are much weaker than uncatalyzed designs.  A narrow pore size distribution is highly desired. A clear understanding of material properties along with flow and temperature distribution is needed in order to assure long life.  Thermal stress is a major issue.  Catalyst coatings affect physical properties, and it is the coated filter that must survive.

 

System models are being developed to include DOC, DPF, and NOx catalysts.  Manufacturers would like a nice set of data to be provided by suppliers; the data would be put into models to allow system design.  Unfortunately, it may not be readily possible to have a simple data set to describe a complex, history dependent device like and LNT or a DPF.

 

Chalmers and GM R&D have developed a global kinetic LNT model for a model Ba/Pt/alumina catalyst.  They have also done a commercial catalyst but those results can’t be shared.

 

ORNL has tested different LNT regeneration strategies on an engine and reports the speciation of HC, CO, and H2.  Extensive instrumentation has generated useful data.

 

The LNT Subgroup is developing a standardized test protocol for LNTs, intended to get enough data to calibrate a kinetic model in a reasonable length of time.

 

LLNL is starting some interesting quantum chemical computations for cases related to LNTs.

 

ORNL is doing DRIFTS and other testing on model LNT catalysts to find mechanisms information.  Nitrate and carbonate are the dominant storage species.  There are interaction with PGM and supports.  Water is also important.  Key deactivation mechanisms include sintering of PGM and Adsorbates, and apparently coverage of PGM sites by Adsorbates.  Kinetic models of the lean storage are being developed.

 

A reformer can generate H2 and CO from fuel onboard.  These reductants can reduce the required temperature for deSOx and deNOx, potentially improving durability and efficiency.

 

There is continuing work toward HC-SCR catalysts.  Many years of research has led to better understanding of mechanisms, but the important chemistry is strongly dependent on what conditions (HC species, temperature, water, etc) are being run.  Adding H2 to HC greatly improves low temperature NOx conversion and widens the conversion window.  GM has a DOE program that is using combinatorial methods and finding improved HC-SCR catalysts.

 

ORNL has done careful analysis of exhaust in a urea SCR system, looking into urea decomposition and reaction products.  At low temperature, you might make a lot of nitrates.  At higher temperatures, you might make some deposits.  There is very large storage of urea-derived components in the SCR catalyst; a control system must model this to avoid large NH3 break through.

 

Ford is continuing development of a diesel SCR SUV.  Emission cycle results are approaching Tier 2 Bin 5 at low mileage.  Work continues, and will be followed by durability testing.

 

NREL is working with AVL to include SCR catalysts in the FIRE code.  JohnsonMatthey and University of Michigan also have SCR models.

 

Opening

Walter Putz, Senior VP Engineering, Detroit Diesel

 

This is an important time to talk about simulation for diesel aftertreatment.  New standards are forcing major developments for cars and trucks.  In the truck business, life cycle cost is critical.  We have to control aftertreatment cost and also usage costs.  The engine-aftertreatment system is a complex system and must be handled together – you can’t just put a DPF on an unmodified engine.  Simulation tools are an important part of the design and development process.

 

Workshop Overview

Dick Blint, GM Research and Stuart Daw, ORNL

 

CLEERS is Crosscut Lean Exhaust Emissions Reduction Simulation.  “Crosscut” comes from the parent DOE Diesel Crosscut Team.  CLEERS is to foster communication and collaboration – not to write software.

 

Several forums have been developed including these workshops.  Common terminology and standards are being developed.

 

The CLEERS team leadership is Blint GMR, Daw ORNL, Sisken DDC, Harold Kung (NW Univ), Singh DOE.

 

There are subgroups for DPF, LNT, and SCR.  This emphasis came from input by the users. The Focus Groups each have 5 or more Crosscut members, plus sponsored participants such as catalyst companies.  The Groups hold monthly net meetings.  6-7 engine mfrs, 1-2 universities, 1-2 National Labs are a typical Group makeup.

 

Kinetics of the catalysts is a key issue to be studied.  Controlled lab data is needed to develop the kinetic models.  ORNL is developing a set of shared data.

 

There is a web site that compiles information and articles.  There are now over 500 citations on the web site.  There is a quarterly newsletter.

 

All Soot Deposits Are Not Created Equal: Variations on a Theme of Peclet Number

Mansour Masoudi, Delphi

 

At the last CLEERS meeting, George Muntean reported variations in soot properties according to deposition conditions. It usually takes 2 years for a new finding to get widespread attention and be utilized in industrial applications. 

Filter pressure drop is a system in itself:

  • Filter attributes (size, length, permeability, area, inlet and outlet coefficient, segment size and cement thickness, ….)
  • Exhaust flow attributes (flow rate, temperature)
  • Soot attributes (engine emission rte, structure of deposit – dense, porous,…)

 

You can map soot mass emission rate as a function of speed, load, EGR, temperature …but in many cases we don’t have this data yet.

 

You need to understand and quantify the roles of all three of these attributes.  Most papers just give a plausible explanation, but you really need to quantify things.  This paper will deal only with the soot structure issue.

 

You can write the equation for dP; there are SAE papers that show it.  To have a good understanding, you have to know soot density and permeability.

 

The flow through the filter has an enormous effect on soot density and permeability.  High flow (>7-8 cm/sec normal velocity at the filter wall) means the soot particles tend toward ballistic impact deposit.  At low flow rates, deposition is driven more by diffusion and less by particle inertia. The latter gives a lower density, more permeable soot deposit and thus lower dP for the same total soot mass.

 

How do you quantify this? Equations were shown to estimate mean free path (MFP) of gas molecule; Knudsen number (relative significant of mean free path to diameter of PM), Stoke-Cunningham factor, and Kubawara function.  These allow estimation of the soot permeability.  The difference between a leaf in the wind versus soot in gas flow is the relation between MFP and particle size.  The Stroke Cunningham factor embodies this effect.

 

The Peclet number is ratio of inertial force to diffusion force.  Soot layer porosity is a function of Peclet number.  The density of the soot layer is then related to the porosity.

 

Thus, two major characteristics of the soot layer – density and permeability – can be identified. You can now plot these parameters vs Peclet number.  Around Pe=1, there is a strong increase in density and decrease in permeability.  In other words, if deposition velocity is large you get dense layers with low permeability.  Pe is much more important to permeability than temperature.

 

With these two values, you can calculate dP knowing the filter characteristics.  Note that dP alone does not provide a reliable estimate of PM mass, unless you know the conditions under which soot was deposited.

 

SAE 2003-01-0842 gave particle layer packing density vs Pe.  This matched the theory well, both for bare and catalyzed DPF.  The presence of the washcoat has little effect on soot deposit; deposit occurs before catalysis.

 

A slide showed good agreement between dP calculated using the above analysis; it cam very close to the test data.

 

You could implement the equations in a controller to predict the current soot loading in real time.  Delphi did this, and a graph showed good agreement with the integral value versus a measured total mass.  This kind of estimate can be used in the deSoot algorithms for triggering and/or controlling heat release during deSoot.

 

Discussion

Of course, the estimation of soot in the DPF only works at low temperatures where soot is not oxidizing.  For a real system, you will also have to have a regeneration model.

 

Does soot get “stickier” when hotter? The effect of temperature is in the analysis – i.e. in MFP. The model as presented does not make any assumption about SOF-related sticking etc.

 

If you have a loose layer, then go to high flow does it change the soot layer that was already there? The model assumes you don’t, but we don’t have hard data.  On the other hand, the model seems to agree well with data so maybe the assumption is not too bad.

 

Modeling DPFs

Prof. Athanasios Konstandopoulos, Aerosol Particle Technology Laboratory, CERTH/CPERI

 

The Center studies fine particles as suspensions in gas media – either as pollutants or intentional (catalysts in industrial processes).

 

Their lab has 24 full time engineers and scientists, all funded by industry contracts and grants.

 

To make the DPF system work, you need a DPF simulation tool for a variety of users, and also algorithms such as virtual sensors for aftertreatment management and control.  Different users (substrate mfrs, coaters, engine makers,…) have different uses for the models.

 

There are a number of DPF configurations – Cd, SiC, sintered metal, fibrous ceramic….   This is like a Cambrian explosion – lots of diversity that will eventually move toward a single winning version.

 

An interesting graph shows number of simulation papers versus time from 1980 to now.  We are about ½ way through sigmoid shaped curve typical of new technology development.  We are right about the peaks of the publication rate!  The curve predicts that by 2012 there will be little new publication – either the technology goes away or else you understand it by then!

 

There are multiple scales of DPF systems – microns pore size, wall thickness, entire filter, and entire exhaust system.  We have to understand all these scales.  You can’t run a single model that includes the finest scale over the full volume in a reasonable computational time.  So, you use different models for different purposes.

 

At the nano/micro scale you use Monte Carlo, particle dynamics, etc.

 

At meso scale, unit cell filtration codes, single and multi channel DPF simulation (WALTER), and exhaust line simulator (ELVIS).

 

It is important to avoid short cuts that use heuristic models – they are not general and cause problems later.  Each level must be mathematical!

 

At the macro scale, couple to CFD and FEA with user defined functions, to calculate stress, temperature field, etc.

 

Diesel aggregates have a fractal structure – soot is not spherical!  The fractal overlap seems to be around 0.174.  Recently, extensive data was taken of effective density versus mobility diameter for different engines and speed/loads.  As diameter increases, density drops.  The fractal dimension also varies with size, running about 2.4 on average. 

 

The normalized size distribution is remarkably similar over a range of engines.  Sigma is about 1.89.  This is handy for instrumentation and for modeling.  The shape can be explained by a model of oxidative fragmentation.  That model says the larger a particle gets, the more likely it will break.  The fragmentation must be random in order to fit the data (i.e., soot does not break in half or some other constant fraction, but rather at a random location).

 

The models of DPFs include wall scale, channel scale, and DPF scale features.  (See slide for more detail).   Current commercial models of the walls have gas temperature but not surface temperature considered; that’s a future enhancement.

 

The micro scale is done with Lattice Boltzmann.  You need a reconstruction of the wall structure; they make digital models from pictures.  This can be done with several algorithms for granular media, with a library or grain types, packing, and resulting structure.  This can also be done for fibers.  The porous structures we use have a lot of wasted porosity; regions the flow will not really go through.  The comparison of model to test results is quite good.

 

Next, there are algorithms for catalyst coatings – both uniform and non-uniform.  Coaters sometimes think there is such a thing as a uniform coating!  A uniform coating, however, reduces the cross section of the flow path.  Non uniform coating depends on coating method.  Coating is usually by dip-and-blow.  Here, the catalyst is removed where the flow velocity is high.  The different methods can give very different permeability versus loading and fraction of pores filled.

 

Coating can also be done on a filtration method.  This gives good results.

 

The dynamics of soot filtration are being modeled also.  Initial deposition is in the wall, followed by a cake development.  Layer characteristics can be predicted.

 

DPFs are very good at collecting small particles; if you see small PM downstream, they have condensed after the filter.

 

Soot deposits first at the high flow locations.  That area plugs first and the flow redistributes to cleaner areas.  You have to feed back soot deposit into the flow field calculation.

 

Soot cake personality is characterized by the Pe number.  You get more wall deposition at higher Pe.

 

Ice particle growth on a window is very similar to soot deposition growth dynamics!

 

Soot cake geometry can become non-convex at low Pe number.

 

A quenched diffusion flame burner was used to provide reference soot distributions.  With this, we learned that input PM size affects the soot cake density; larger PM makes looser cake.  We don’t see this so much in engines because the soot input size is more constant than in this bench experiment.

 

The soot microstructure changes as you oxidize the soot.  This has been modeled (series of slides shown).  Density is linear with oxidation rate.  The model agrees well with regeneration experiments.

 

At the DPF level, there are practical issues such as the glue thermal characteristics in segmented filters.  If the glue is a good insulator, there are larger temperature gradients in the catalyst.  You can get regeneration of some segments without other regenerating.  Yu can get partial regeneration when the temperature is non uniform.

 

As others have said, partial regeneration destroys the correlation between dP and soot mass.

 

They are now trying to use hooks in CFD codes to put the DPF models in. These codes can predict soot deposition geometry and thermal distribution.

 

A DOC in front helps the temperature for regeneration on the DPF.

 

The ash distribution (SAE 2003-01-1963) along the length depends on the shear on the ash layer, and the ash shear strength.

 

You have to know the origin and pathways of ash. It can be by pyrolysis on the filter, or combustion upstream.  Ash can re-entrain at high shear.  Ash stickiness changes depending on its history. More work is needed to characterize ash!

 

Future emission control systems will be very complex, whether you like it or not.  You will have multiple catalysts/DPF units, sensors and virtual sensors, and control algorithms.  Models will have to be made faster to use in vehicle ECU.  Fiat paper at FISITA F2004V068 showed DPF control algorithms based on this work.  Their paper shows the value of modeling!

 

Discussion

The regeneration oxidation model shown in these slides is very simple. We have more complex models but no time today to show it.  NO2 and O2 mechanisms are both important.  A 2-layer model is used: some soot in contact with catalyst, some not, so there is an effectiveness factor.  HC or wetness of soot is also important.  We don’t see an effect of wet soot; it becomes dry by the time it is hot enough to react.  You might get some added heat release from Pt oxidation of the desorbing HC.

 

Relationship Between Pressure Drop and Pore Microstructure for DPF from Experimental and Capillary Pore Modeling.

Greg Merkel, Corning

 

You can show dP versus flow at various soot loadings; generally a parabolic fit.  Or, you can look at dP versus soot loading at various flow conditions.

 

dP affected by

  • Length
  • diameter
  • plug length
  • channel width
  • wwall thickness
  • soot in pores
  • pore microstructure
  • soot cake

 

We are particularly interested in soot penetration into wall.  How much gets in, how deep does it penetrate, location within the pores (uniform coating or local?), and packing density in pores.

 

Slide shows 6 different pore structures developed.  These have different dP characteristics – initial dP, slope of initial dP versus soot loading.

 

The simplest case is a filter that only has a surface cake, no penetration. Useful filters, though, have an initial steeper dP slope.  Some filters have a low initial slope that seems to be building a uniform coating on the pore walls, followed by the steeper and then the shallower slope.

 

Each of the above three cases is modeled as two overlaying slabs with different thickness and permeability.

 

The flow is treated with an approximation of randomly oriented capillary pores.  The Hagen-Poiseuille equation relates dP to capillary geometry.  You sum over all pores to get an equation for the wall.  Permeability can then be estimated, and is linear in porosity and quadratic in pore diameter.   K=C phi d2.

 

This model was used to compare to literature data.  The agreement is pretty good to porous glass  and MgO ceramics of known characteristics.  Agreement is also pretty good for cordierite (Cd).

 

The soot deposit permeability also has to be modeled.  The Konstandopoulos method was used.  Soot cake porosity is typically >90%.

 

These are combined then to calculate filter dP.  Assumptions for following slides are listed; high SV, 180K.

 

For the case of a soot cake forming without permeation into the wall a low slope versus loading is predicted. For the case where soot uniformly fills surface and near surface pores you get a steeper slope. For the case where soot deposits uniformly coat the capillary walls you get very little pressure increase until the model begins to close the entire pore.

 

One can then compare modeled dP to experimental data with Cd DPFs.

  • Case 1 – 50% porosity, pore d50=8 micrometers: the excremental data is closet to the “soot cake on wall” model.  You get to the soot cake phase very early.
  • Case 2 – 70% porosity, d50-16: Still looks like soot cake build.  If soot does get into the surface pores, it must be quite porous.
  • Case 3 -  50%, d50 30 but wide pore size distribution: Now we see low slope, steep slope, then low slope again.  This seems to be coating pores, then plugging pores, then soot cake.
  • Cake 4 – 50% 12 micron pores: Steep initial followed by lower slope. The initial slope corresponds to a very low porosity in the pores, ~80%.

 

How do you control that initial slope?  A study done with 110 Cd filters having various structures.  38-57% porosity, different pore size and distribution.  These generated a wide range of dP slopes.  The Konstandopoulos model was used to generate permeabilities and coefficients, and these were fit to the experimental variables.

 

Pore size distribution defined by the diameter of 10, 50, and 90 percentile. Also, d90-d10 is width of distribution.  You can also normalize the parameters:  (d90-d10)/d50 etc.  These parameters were part of the dP data fitting.

 

Permeability of a clean wall correlates well (r2 = 0.87) with d502.

 

A soot filled wall permeability is dominated by (d50-d10)/d50.  The d502 term is now minor.  High porosity and narrow pore size distribution maximize permeability.  (d50-d10)/d50 seems to be related to whether the flow path seems like large pores connected by small necks, or more like a constant width channels.  A large value is more-connected.  Narrow necks increase the likelihood of dense soot packing in the pore necks.

 

Summary:

Pore microstructure is important

A random oriented capillary model matches data

It has been extended to the two layer wall + soot cake model

Experiments show desired pore size distribution characteristics.

 

Discussion

Prof K: interesting work.  What “pore size” means is a matter of definition.  There are solid reasons for the data shown.  Permeability always scales with a characteristic size squared and a constant around 1.6.  Deposition is dominated by stagnation regions.  It is a matter of style if you prefer to model flow in pores versus flow around objects.  We think it is easier in denser media to model flow around objects.  the sharp regions of slope are related to percolation mechanisms.  I don’t think the flow acceleration mechanism is really likely; the flow can move to another channel instead of accelerating.  The high slope is related to the penetration depth.  The best filter is the one that keeps the filter wall separate from the soot cake – but that is the worst if you want to get catalyst and soot into contact!  Of course, the capillary pore model is only a rough approximation.

 

The pore distributions are based on mercury porosimetry, so they are volume related.

 

In real filters the pores have a lot of interconnection, while your model has none.  How can it match the data as well as it does? I don’t really know! It does seem surprising that such a simple model fits the data so well.  Perhaps it is because the porosimetry only measures the “accessible” pores.

 

We are also now doing image analysis of real filters.

Regeneration Characteristics of DPFs Under Transient Exhaust Conditions

Tariq Shamin, U Mich Dearborn

 

Most available DPF regeneration data is at steady state.  In the real world, regeneration will have to occur under transient driving conditions.  Can we regenerate at lower temperature by some form of modulation.

 

Their model is based on Bisset (like everyone else’s!).  This is a fairly simple model.  A Corning EX-80DPF was used, 100 cpsi, 188 x 152 mm, 50% porosity.

 

The model’s predicted temperature matched published data pretty well for a SS regeneration.

 

Using the model several cases were evaluated.  If inlet temperature is sinusoidally varied +/-50C at 0.01 Hz, the Toutlet varies less than the Tbed.  Wall temperature is not symmetric around the mean value.  dP also varies sinusoidally.

 

Exhaust flow rate modulation does not affect the filter temperature but strongly affects dP.

 

For a soot loaded filter at Tinitial 510C (below light off), +/-50C inlet temperature modulation at 0.01 Hz you get more temperature rise and faster regeneration.  At 0.1 Hz, the effect is damped out and it looks more like SS.

 

For higher temperature, 500C – close to soot ignition temperature, you see larger effect of modulation.

 

Modulation of exhaust flow rate has major dP effect, and small improvement in regeneration.

 

For a CDPF at 250C modulation has a larger effect of enhancing soot regeneration.  At 300C (regeneration will go by itself), modulation increases regeneration completeness and speed.

 

Discussion

This arises because chemical reaction rates are nonlinear: for the same average T, modulation increases the net reaction rate.  Low frequency is necessary since thermal mass will prevent fast modulation from having an effect.

 

Did you normalize for the total energy input? Is there an advantage in regeneration versus total energy? For soot regeneration, you see accumulated effect.

 

What would you have to change in your model to reduce the effect of frequency?  Is it a material property, a soot property, or….? I don’t know if it is mass or heat transport limited.  It seems likely it is the heat capacity or thermal mass effect.

 

Physical Durability of Cd Ceramic Filter

Suresh Gulati, Research Fellow and Consultant, Corning

 

DPF requirements:

  • Filter efficiency
  • Soot capacity
  • Ash storage
  • Low dP
  • Low fuel penalty
  • High heat capacity
  • High regeneration efficiency
  • Durability

 

Durability:

  • Key requirements
  • Key physical properties
  • Strength vs porosity
  • vs geometry
  • Model for isostatic strength
  • OxiCat vs DPF
  • Model for regeneration stresses
  • Allowable stress for safe regeneration

 

LD needs something like 200K miles durability.  You can’t have (large scale) cracks, although a flow through catalyst substrate can.  HC needs 435K miles.

 

Stresses and relevant strength characteristics:

  • Coating and calcining – tensile
  • Packaging – compressive
  • Heat treat mat – compressive & tensile
  • Degreening – tensile
  • Regeneration - tensile

 

Physical properties

  • Compressive strength
  • Isostatic
  • Modulus of rupture
  • Fatigue behavior
  • Elastic modulus  (Emod)
  • Thermal expansion

 

Maximum stress occurs somewhere in the center, not at the ends where the plugs are. Property measurements focus on unplugged section.

 

A flex test measures bending strength – axial, direction of flow – or face plane – tangential.

 

You can do a crush test in 3 directions.

 

A 3D isostatic test pressurizes in all directions.  It finds what stress can be applied during canning. A 2D isostatic tests can also be used to predict canning.

 

The focus is on isostatic and bending strengths.

 

Strength measurements are usually made in a short time, while real parts have loads applied for years.  So, the service load must be lower than the ultimate strength measured.

 

Web thickness and porosity each affect strength. Wall crush strength in the axial direction drops exponentially with porosity.  Thus, 35% porosity gives about 3000 psi, while 50% porosity has about 1500 psi strength, about half.

 

Web strength is 17000 exp(-4.9/P) where P is porosity.  Isostatic strength is 2.8 (web strength) (t/L), t= wall thickness, L = cell width.

 

Ceramics are weaker in tension than in compression.  Canning is more compressive, thermal stress is tensile.

 

The honeycombs also have a ceramic skin; this improves canning strength significantly.  The inner part is called the matrix. During isostatic tests (or canning) the stress in the skin is much higher than in the matrix.  Even so, the matrix is where you see failure first because it is so much weaker than the skin material.  For 900/1, stress skin / stress matrix =11.  For 400/6.5, 6.6.  Typically, canning loads are lower than strength by factors of >2.5.

 

DPF vs DOC

  • Higher Tpeak
  • More porous
  • Lower strength
  • Larger temperature Trange
  • Large axial T gradient
  • More T cycling
  • Outer zone temperature is lower
  • High fatigue loading
  • DPF is about 5 times heavier than catalyst

 

Thermal expansion: up to about 200C, the material actually shrinks.   There is only 140 ppm length change 0-400C. This grows to 1150 ppm at 800C.  axial and circumferential growth rates are different. If the center gets too much hotter than the outside, then the skin is put into tension and you may get a ring off crack.

 

This depends on Tpeak, CTE, Tgradients, how many cycles over lifetime.

 

A simple model allows input of measured temperatures. A linear T difference (hot center, cooler outside) crates maximum stress in the middle.  When there is a T difference along the axis, you get an added stress.  You have to add these two stresses.

 

The stress also depends on the aspect ratio, L/D.  Longer DPFs experience more stress.

 

A table compares EX-80 to RC, 100/17 vs 200/19.  Measured temperature profiles were put into the model. The radial T variation causes high stress.  Highest stress is in the middle (between inlet and outlet).  A 6” filter would have about half the stress of a 12” one.

 

To avoid cracks, the highest stress must not exceed the statistical lowest strength. To allow less than 0.1% failure probability, allowable stress is 0.18 So or about 130 psi.  Either you need a stronger product, or you reduce the thermal stress by regeneration more frequently.

 

For a 3 g/L soot load, stress is about 217 psi.  7.0 è 412.  If you want to go to 1 in a million failures, you need even lower stress. (these numbers are for RC).

 

Regeneration stress parameter isx defined as Emod x (length change center vs edges).  This correlates pretty well with regeneration stress.

 

Summary:

Thermal cracks can be avoided by decreasing both coated Emod and CTE by 40%..  In addition, allowable stress can be increased about 15% by reducing variability of MOR in the skin region.

 

Discussion

This argues you need to couple CFD to FEA codes in the long run.  Are we close? It is important to get exhaust flow well distributed.  Low flow at the OD contributes to larger T differences.  Soot should also be evenly distributed.  The inlet cone needs to be a diffuser.  We could also imagine a cell geometry that helps flow distribution.

 

Doesn’t a DPF self-adjust the flow top an extent?  High flow channels soot up faster, so the flow should gradually redistribute to other cells. It might help to direct more flow to the OD – this reduces the dT.  Mass and temperature distribution don’t necessarily go together. It is also harder to regenerate the OD cells.

 

The T gradient can be larger is the Tpeak is lower – since the CTE grows at higher temperature.

 

Have you looked to see what happens to the ceramic with fatigue aging? Yes; we see a slow crack growth due to stress corrosion at high humidity – near the OD.

 

Controlled regeneration is not really an issue.  The only major issue is an uncontrolled regeneration, where you start regeneration then drop to idle.

Discrete Particle Modeling of DPF

Mark Stewart, PNNL

 

Model development started this year.  We are modeling flight and deposition of individual particles.

 

There are multiple length scales of interest in a DPF.  We are looking at the finest scale, discrete particles.  In the long run, this work should lead to characteristics o\at pore and channel scale – we don’t think you would routinely use such a small scale model.

 

PNNL started with a 3D digital map of filter microstructure.  Corning EX80 was cut and imaged.  Images were combined to give a 3D model.

 

Flow is solved with Lattice Boltzman.  Time step is 3 x 10-8.

 

Initially we assume that all PM acts the same as 100 nm particles.  The PM motion is from gas velocity with Brownian motion superimposed.  Particles that hit a wall always stick.  For now there is no re-entrainment of oxidation.

 

As PM deposits, the flow field gets an added flow resistance.

 

Preliminary results:  An example condition was run, taking 88 ours to run on 64 Itanium2 processors at 1.5 GHz.  About 1 million particles introduced, about 14K escaped.  Typically 3K in flight at any time.  The model illustrates deep bed filtration transitioning to cake filtration.  There is little change in flow deep into the wall.  On the surface, the high velocity areas move as deposits build.  The bulk of gas flow follows a few major channels. (note: this is EX80, a wide pore size distribution – these flow channels are a reason for narrow pore distribution)

 

The model makes reasonable predictions of porosity at two conditions.  Qualitative agreement is good between photos of real soot and the model predictions.

 

A method is being developed to run experiments for validation.  A single channel filter is placed in exhaust.  Gas is drawn out of the channel, so the outside walls are the filter medium.  You can then analyze the soot cake without cutting up the sample.

 

Next steps: compare data to projections – dP, capture efficiency, deposit location and structure.  Then, apply to other substrate materials.

 

 

Discussion

How reproducible are these calculations?  You have “random” motion.  So far, we have not changed the random seed, so it runs the same each time.

 

What factors go into the Darcy resistance when a particle hits the wall?  So far, we are using a simple linear relation between the number of particles in a volume and the Darcy resistance. A future step will be more rigorous.

 

EX-80 has a broad pore size distribution.  Have you tried anything else?  Not yet but we plan to.  We would be very happy if someone gave us the 3D model!  We plan to digitize other images of the same EX-80 (different portions) to see if the results come out the same.

 

Prof K noted that you would prefer to have a model to build the filter geometry rather than the laborious method of cutting up a piece.

 

Simulation of a Coupled DOC and a DPF model

Tony Triano, Mich Tech University

 

Research sponsored by John Deere and DOE.

 

JMI CRT is DOC + DPF, intended to convert NO to NO2 and use NO2 to regenerate the soot.

 

The DOC model has routines to calculate exhaust properties, dP, gas phase energy conservation and species conservation.  The resulting values are input to a DPF model.  The DPF model calculates exhaust properties, velocity field, inlet temperature, regeneration chemistry, wall temperature, dP and filter effectiveness.

 

The DOC model is in SAE 2003-01-3176.  It includes kinetics, reaction rates, mass and energy conservation.  Reaction rates are assumed first order in both reaction species and oxygen.  All chemical reactions occur at the walls, and at wall temperature.

 

NO conversion to NO2 versus temperature is well modeled against a set of experimental data.

 

The DPF model is like Prof K’s; DPF only, no catalyst in it.  The code has been licensed to GM and U-Wisc Madison.  The NO2 +C reaction was added.

 

The DOC is  JMI part, 10.5 x 6”, 300 cpsi.  DPF: 200 cpsi, 52% porosity, 10.5 x 12”. Tested on John Deere engine.

 

Including the NO2 in the model predicts much better deSoot; this agrees well with the model.  NO2/C ratio needs to be high enough for CRT to work well.

 

An issue: if you have a soot cake and are oxidizing in the wall, are you still accumulating on the soot cake?

Discussions

On an engine, it is hard to tell is you are getting oxidation at the same rate as deposition – the mass balance is obscured by CO/CO2 in engine exhaust.  You will need some lab data too.

 

CRT will be more of a problem when you have lower FG NOx.

 

How do you know you have repeatable data? The filter was baked in air for a long time to start clean.  We have not run a lot of repeat yet.

 

Wisconsin Aftertreatment Model

Andrea Strzelec, U Wisc

 

Sponsored by GM.

 

A system model is needed, including engine exhaust and thermal models, a soot emission model, and an aftertreatment model.

 

We used a 1D engine model of a 4.9L Isuzu engine.  The model was calibrated against 8 Mode California cycle.  Fuel step transients and thermal outputs were reasonable.

 

The exhaust thermal model is 1D in gas flow and 2D in heat transfer.  Flow pulsations and the resulting heat transfer effects are included.  It was created in WAVE.  The model was validated at 18 SS operating conditions.  Gas stream temperatures were within 1%, and wall temperatures within 3%.

 

The engine soot emission model is a neural network and physical model. See SAE 2003-01-3227.

 

The DPF model is based on MTU’s adaptation of Prof K’s model, 2003-01-0841.  Input data from WAVE through Simulink.

 

There will also be a model for DPNR – DPF plus LNT models.  For now the kinetics are separate but will be integrated later.

 

The component models can be combined into a system model.  Practical issues include selection of a reasonable time step, and synchronizing the various models.  Computational demands are also an issue.

 

3 SS and 2 transient cases were run to test the model.  Results are preliminary but look reasonable.  Work is continuing.

 

Discussion

How long does it take to run 350 sec?  25 hours on 1 Pentium 4 3 GHz PC. The bottleneck is the neural net. There are several ideas to speed it up.

 

Catalyst Performance Maps – Needs and Perspectives from an Engine Manufacturer

Houshun Zhang, DDC (“DaimlerChrysler NAFTA Truck Business Unit”!!)

 

The paradigm is that suppliers will provide a map of component behavior.  An example is a turbocharger, where performance maps are provided that manufacturers can use for performance simulations.  We would like a similar sort of map for catalyst/DPF function. The map must include the necessary input values over the useful range.  One does not need to know details of the catalyst if you have an adequate model.

 

For aftertreatment models in system controls and strategy development we use 1D and 2D models. For real time control in the product we use 0D models.  3D typically requires more computation time and more detailed knowledge; we typically rely on universities or suppliers for those models.

 

National Labs and universities can assist in developing more physically based maps, and detailed micro kinetics.  Suppliers can map and provide standardized format data.

 

Each catalyst has its own unique map.  Further, each kind of catalyst (DOC, DPF, LNT, SCR…) has its own map criteria.

 

An example of what is wanted for a DPF:

 

  • DPF map is useful for model calibrations – tuning to engine data
  • System integration parameters – dP, soot loading, emission reduction, matching engine to catalyst and catalyst to engine.
  • Maps should have a generic format.  Each company can interface to its own model set.

 

The map must

  • Cover range of SV, T, soot loading, NOx/PM ratio, species emissions associated with catalyzed filters
  • Have a simplified form
    • Normalized dP
    • Normalized flow rate or T
    • Normalized soot loading
  • Include things like heat capacity, dP vs soot loading, kinetic rates…

 

This maybe a task for the DPF Focus Group to coordinate!

 

A map might include

  • Soot loading versus time for various temperature and flow
  • Normalized dP versus soot loading for various exhaust flow rate
  • Kinetics of NO to NO2 oxidation, NO2+C reaction, C+O2 reaction, etc. – or enough data so we can calibrate a kinetic model

 

The LNT team has made progress on maps; can the DPF team do so too?

 

Discussion

Prof. K: In systems that are path dependents, you can’t describe by a simple map like a turbo map.  You have to have an intrinsically dynamic model, not a static map.  Perhaps what is meant is a minimum set of performance data, from which true dynamic models can be calibrated.  It might better be called a protocol rather than a map.

 

We don’t have reliable handbook values of things like permeability.  Further, since models are not standardized, the same permeability in different models will give different results.

 

Prof K: you need to get more of the basics nailed down so there is less to be calibrated from data.  As you do so, you will need less and less calibration data.

 

You need to develop a very detailed test protocol.  The whole thermodynamics is involved: eat and mass transfer, chemistry, …

 

George Muntean (PNNL): the “map” is intended to be a simplification that lets you bypass detail consideration of some variables.  For instance, a turbo could be calculated from geometry – but not easily.  At this point, we haven’t even decided what we want in a DPF model.  Is it just pressure-flow?  Do you need to know detailed soot cake characteristic?  How do you short cut the data process to make it economic and timely?  Because DPFs have a history effect things get more complex.  Perhaps the map should be rate of change rather than current value based.  The discussion is still very open.

 

 

Global Kinetic LNT Model

Louissa Olsson, Chalmers

 

With GM R&D

 

Model is a storage and shrinking core type model.  There are rates for BaCO3 to Ba(NO3)2 and vice versa.  The model includes recognition that some Ba sites never participate.

 

Reaction set: (see slides for better detail)

Storage

BaCO3 + NO2

BaCO3 + NO – forms nitrites

Regeneration

BaNO3 + c3h6

NOx + CO

 

PGM:

NO + O2 è NO2

c3h6 + O2 è CO2 + H2O

NO + c3h6 è N2, CO2, H2O

 

Experiment sequence: 

NO to NO2 over Pt/alumina.  T ramp 25-500C, 8% O2 and 600 ppm NO

NOx storage on Pt/Rh/Ba/alumina – 320C with long lean period. 8% O2 (0 rich), 900 ppm c3h6, varying [NO].

 

The NO oxidation rate kinetics were calculated from