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
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
Walter Putz, Senior VP
Engineering,
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.
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.
Mansour Masoudi,
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:
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.
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.
Prof.
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
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!
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.
Greg Merkel,
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
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.
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.
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.
Tariq Shamin,
U
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
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.
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.
Suresh Gulati,
Research Fellow and Consultant,
DPF requirements:
Durability:
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:
Physical properties
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
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.
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.
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.
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.
Tony Triano,
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?
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.
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
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.
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.
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:
The map must
This maybe a task for the
A map might include
The LNT team has made
progress on maps; can the DPF team do so too?
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.
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