Transcript Temperature contours from two different view points the

University of Thessaly
Mechanical Engineering Department
Laboratory of Thermodynamics
& Thermal Engines
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Brief Presentation of CATFOAM:
LTTE Foam Particulate Filter
Modeling Approach and Software
Volos, December 2000
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LTTE Foam Particulate Filter Model Specifications
Category
Items
Calculation Domain
cylindrical filter with given diameter and length
Boundary Conditions
•Engine operation condition (exhaust gas mass flow
rate and exhaust gas temperature at filter inlet)
•Possibility to assign radial velocity and temperature
profile at inlet face
•Heat loss from the canning surface
Initial Condition
•Initial filter temperature
•Initial accumulated soot mass (including clean filter)
Mode of Regeneration
Thermal Regeneration, Catalytic Regeneration
Expandability
Possible to consider various foam structures, materials
and sizes
Future activity to cover geometric design optimization
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Input Parameters and Data List (1/3)
Filter Design Parameter
Category
Items
12-hedral cell structure
Pore size (mean, STD)
Strut thickness (mean, STD)
Irregularities’ coefficient
Active volume fraction for filtration
Fibers’ volume fraction
Irregularities’ volume fraction
Tuning parameters for diffusion filtration/SBA
Filter size
Length
Diameter
Canning
Outside diameter
Thickness
Insulation material - thickness
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Input Parameters and Data List (2/3):
Operating Point Data
Category
Items
Boundary condition
Inlet gas velocity or flow rate with radial gradient
Inlet gas temperature with radial gradient
Heat loss from outer filter shell
Initial Condition
Initial filter temperature
Initial soot mass in filter / bulk mass gradient
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Input Parameters and Data List (3/3):
Material Properties
Category
Items
Ceramic Foam
Bulk density
Specific heat capacity
Thermal conductivity
Soot deposit
Mean Porosity
Mean Density
Indicative size distribution
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Output Data List
Category
Items
Spatial and Temporal
Profiles during
Regeneration
Filter temperature and temperature gradient
Species : O2, CO, CO2, NO, HC, H2O
Exhaust gas temperature evolution with time
Pressure drop evolution with time
Particulate mass evolution with time
Output Files and
Graphics
Exported to MS Excel Spreadsheets with graphs
updated by means of MS Excel macros
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Foam Filter Modeling:
Published Journal Papers
1.
2.
A Mathematical Model for the Dynamic Particulate
Filtration in Diesel Foam Filters. Particulate Science
& Technology, 17: 179-200, 1999
Dynamic Filtration Modeling in Foam Filters for
Diesel Exhaust Chem. Eng. Com., 188: 21-46, 2001
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Modeling and validation of the filtration, loading
and regeneration characteristics of foam filters
• The core of the model accounts for the pressure drop,
filtration efficiency and soot accumulation of a foam filter.
• It also includes a basic submodel for the regeneration
process of the foam filter.
• Testing procedures for the assessment of filtration, loading
& regeneration characteristics are defined.
• As regards the backpressure and filtration efficiency
prediction, the model has been validated against the results
of filtration and loading tests on specific foam filter types.
• A preliminary computational assessment of the effect of
filter geometry has been attempted with the aid of
commercial CFD code (CFX).
• The catalytic regeneration model is not yet validated.
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Modeling Assumptions (1/2)
• This is an application oriented engineering model for the
prediction of diesel foam filter operation. The following
phenomena are taken into account by the model:
• the actual size distribution of the emitted particulate
(usually approximated by a log-normal distribution)
• the geometric structure properties of the foam filter
• variation of the filtration efficiency with time, as the filter
is being loaded
• the axial distribution of the accumulated particulate along
the filter
• induced backpressure as function of filter geometry and
loading
• heat transfer between exhaust gas and foam filter
• thermal soot oxidation by exhaust gas oxygen
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Modeling Assumptions (2/2)
The filter pore structure is considered to consist of 12hedral elements (cells). The
specific geometry is described by the number of pores per linear inch (ppi) and
the filter porosity. In practice, the 12hedral structure is reproduced with
significant inaccuracies, resulting in numerous "blocked" passages. It may be
assumed that the perfectly reproduced 12hedral cells filter the particulate in a
"deep-bed" mode, whereas the "struts" act as fiber elements. In the blocked
passages, the assumption of a "cake" filtration is reasonable to employ. Thus,
the filtration of the foam is modeled by two parallel mechanisms, namely
deep-bed and cake filtration. In order to simulate the cell structure with
equivalent "fiber" filtering elements, the dimensions of the cell structure (pore
size and strut thickness) must be known. In real filters these parameters are not
uniform for the entire filter. Actually, a normal distribution around a mean
value of the strut thickness may approximate the real conditions. The mean
value and the standard deviation of the strut thickness for a specific foam
structure can be estimated from photographs. A third mechanism accounts for
the filtration due to accumulated soot. As filtration proceeds, a soot particle
layer develops around the struts. Accumulated particles, forming irregularly
shaped dendrites, act as very efficient collectors, enhancing filtration.
The blocking of some passages due to manufacturing inaccuracies is quantified
with a "specific blocked area" (SBA), that is, the total area of blocked
passages, projected in the direction of the flow per unit volume of the filter.
This is a tunable parameter, varying between filters of different pore density
and different material or manufacturing technology. Tuning is performed
against test results of filtration efficiency.
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LTTE approach in the development of
CAE Methodologies and Tools
• Development of models and software packages (apparent
kinetics – systems approach)
• Development of kinetic parameter estimation methodologies and
tools
• Development of emissions measurements quality assurance
methodologies and tools
• Design and implementation of critical experiments to improve
understanding and modeling of exhaust after-treatment systems’
components
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