Fundamental Studies of Diesel Particulate Filters: Transient Loading, Regeneration and Aging

Konstandopoulos, Athanasios G.; Kostoglou, Margaritis; Skaperdas, Evangelos; Papaioannou, Eleni; Zarvalis, Dimitrios; Kladopoulou, Evdoxia

doi:10.4271/2000-01-1016

Cited by 391 publications

(275 citation statements)

References 23 publications

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“…This has been expanded here based on the unit collector model pioneered by Konstandopoulos and Johnson [6] and later updated by Konstandopoulos et al [7,8,9,10]. This effort slightly modifies this model for inclusion in the lumped zone approach.…”

Section: Pm Mass Equationsmentioning

confidence: 99%

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Combining the Classical and Lumped Diesel Particulate Filter Models

Depcik¹

2015

SAE Int. J. Engines

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The control of PM that emanates from SIDI and CI engines is of significant concern for the automotive industry. This is because PM can infiltrate the alveoli in humans and transport toxic chemicals absorbed on the surface of the particle, subsequently leading to lung related illnesses [1]. As a result, the automotive industry now widely implements PM filtration in the exhaust. In particular, DPFs are used for CI engine exhaust with GPFs currently being considered for SIDI engines.The operation of a DPF (or analogous GPF) includes the storage of PM in and on top of a porous wall with its eventual regeneration in an oxidative environment. This can be accomplished actively utilizing O 2 or passively by employing NO 2 ; often through synchronization with an upstream Oxidation Catalyst. This need to modulate device operation has led to the development of simulation tools in order to reduce the number of experiments needed by predicting the pressure drop through the device and its temperature evolution. Broadly speaking, the models typically fall into one of two categories; those designated to operate within an Engine Control Unit (ECU) [2] and the classical two-channel approach [3].The ECU approach is designed for computational speed and is still often based on the two-channel model originally posed by Bissett [4]. In a previous effort by the author, the research in this area was reviewed and a derived model was generated that used dynamically incompressible flow to simulate the flow through the device while creating lumped zones in order to predict the temperature evolution. Utilizing a high-level coding language that runs significantly slower than traditional programming languages [5], the filter temperature evolution could be predicted relatively accurately in a faster than real time manner. This was because only one term in the governing energy equations was influenced by compressible flow. However, decoupling temperature and pressure by using dynamically incompressible flow (effectively negating the ideal gas law), the model was unable to simulate the pressure drop during warm-up and PM regeneration experiments.As a result, this effort seeks to merge the two models by simulating temperature using the ECU approach while estimating the pressure drop using the two-channel methodology. In specific, the temperature profile through the device is generated by the ECU version that is then utilized as a constant within the two-channel model removing the need to solve for the channel temperatures. Moreover, deep bed filtration is included in the model formulation in order to simulate the pressure drop evolution in the device more accurately. Finally, model run times are given in order to document the computational expense of the combined model. DPF MODEL DERIVATIONIn a previous effort, instead of simulating both the inlet and outlet channels along with a porous wall, model derivation followed a lumped approach through the creation of a variable number of zones Combining the Classical and Lumped Diesel Particulate Filter M...

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Section: Pm Mass Equationsmentioning

confidence: 99%

“…Therefore, they must be translated to the boundary temperatures of each zone: (5) (6) (7) with the last assumption indicating that the exit temperature change over the half-cell is negligible. Of note, for the temperature exiting the DPF, this is the same boundary condition utilized in the previous effort.…”

Section: Filter and Gas Energy Equationsmentioning

confidence: 99%

Combining the Classical and Lumped Diesel Particulate Filter Models

Depcik¹

2015

SAE Int. J. Engines

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“…The radial temperature distribution at the inlet of the filter is due to the thermal boundary layer development as explained in references [12,[19][20][21]. In order to account for the thermal boundary layer development, the empirical temperature factor profile is determined by analyzing experimental data from the 18 runs.…”

Section: Temperature Distribution At Filter Inletmentioning

confidence: 99%

Development of a Catalyzed Diesel Particulate Filter Multi-zone Model for Simulation of Axial and Radial Substrate Temperature and Particulate Matter Distribution

Mahadevan

Johnson

Shahbakhti

2015

Emiss. Control Sci. Technol.

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The catalyzed particulate filter (CPF) is an important exhaust aftertreatment subsystem that is managed by the electronic control unit (ECU) of an engine. CPFs need periodic regeneration to avoid temperature exotherms and excess engine back pressure. To this end, a multi-zone particulate filter (MPF) model was developed in this research to serve as a simulation tool to provide on-board diagnostics (OBD) data for managing CPF active regeneration (AR). The MPF model runs in real time within the ECU to provide feedback on temperature and particulate matter (PM) loading distribution within each axial and radial zone of the filter substrate. The MPF model accounts for the internal and external heat transfer mechanisms, inlet temperature distribution using the fully developed boundary layer concept, and PM oxidation by thermal (O 2 )-and NO 2 -assisted oxidation mechanisms. A calibration procedure was developed to calibrate the PM kinetics and heat transfer coefficients of the MPF model. The model shows the good capability to predict temperature and PM loading distribution within the filter.

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“…The technology of DPF was first introduced in the early 1980's and tested for its performance on reducing PM [4] since then. With the current design of DPF, the reduction of PM from the diesel engine can be more than 95%conversion efficiency [5].…”

Section: Introductionmentioning

confidence: 99%

“…Their model was based on Kuwabara, Lee and Gieseke [9,10]. The model was later modified and outlined in Konstandopoulos [5,11]. Currently it has been used in most of DPF simulation software packages.…”

Section: Introductionmentioning

confidence: 99%

Sensitivities Of Diesel Particulate Filter Clean And Loading Models

Thiengkaew

Khongsup

Wirojsakunchai

2015

MATEC Web of Conferences

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ABSTRACT.A comparison of two mathematical models from two cycle simulation software packages predicting a cold flow and filtration process of a Diesel Particulate Filter (DPF) is studied extensively. Three Cordierite Diesel Particulate Filters (DPF) models, with variations in washcoats (bare, washcoat-only, and catalyzed washcoat), are built and validated to experimental data. The goal is to estimate the values of wall permeability ( ) and friction loss coefficient ( ) from the flow bench test and the values of soot permeability ( ) from the filtration process. Good agreements are found comparing between experimental data and simulation. The values of those parameters obtained from each software packages show the same trend among all tested DPF samples. However, they are different when comparing cases by cases because of the methodology lies in each mathematical model.

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Fundamental Studies of Diesel Particulate Filters: Transient Loading, Regeneration and Aging

Cited by 391 publications

References 23 publications

Combining the Classical and Lumped Diesel Particulate Filter Models

Combining the Classical and Lumped Diesel Particulate Filter Models

Development of a Catalyzed Diesel Particulate Filter Multi-zone Model for Simulation of Axial and Radial Substrate Temperature and Particulate Matter Distribution

Sensitivities Of Diesel Particulate Filter Clean And Loading Models

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