Microscopic Visualization of PM Trapping and Regeneration in Micro-Structural Pores of a DPF Wall

Karin, Preechar; Cui, Liyan; Rubio, Pedro; Tsuruta, Teppei; Hanamura, Katsunori

doi:10.4271/2009-01-1476

Cited by 37 publications

(13 citation statements)

References 10 publications

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“…Karin et al . () observed that the PM penetration depth is strongly dependent on the porosity distribution. Comparison of the PM distribution and the porosity distribution of SiC DPF, shown in Figure , shows that the mean PM penetration depth is approximately two times larger than that in the location of porosity distribution.…”

Section: Resultsmentioning

confidence: 99%

“…In this calculation, the flow velocity depending on the porosity distribution was taken into account until a depth of 10 µm. The averaged depth of the surface pore was 20 µm, which is approximately two times of the depth for the depth of porosity distribution according to the previous study (Karin et al ., ). However, further investigation regarding the depth of the surface pore in terms of the SiC porous wall structure is required in the future.…”

Section: Resultsmentioning

confidence: 99%

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Electron microscopic time‐lapse visualization of surface pore filtration on particulate matter trapping process

Sanui

Hanamura

2016

Journal of Microscopy

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A scanning electron microscope (SEM) was used to dynamically visualize the particulate matter (PM) trapping process on diesel particulate filter (DPF) walls at a micro scale as 'time-lapse' images corresponding to the increase in pressure drop simultaneously measured through the DPF. This visualization and pressure drop measurement led to the conclusion that the PM trapping in surface pores was driven by PM bridging and stacking at constricted areas in porous channels. This caused a drastic increase in the pressure drop during PM accumulation at the beginning of the PM trapping process. The relationship between the porous structure of the DPF and the depth of the surface pore was investigated in terms of the porosity distribution and PM penetration depth near the wall surface with respect to depth. The pressure drop calculated with an assumed surface pore depth showed a good correspondence to the measured pressure drop.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Electron microscopic time‐lapse visualization of surface pore filtration on particulate matter trapping process

Sanui

Hanamura

2016

Journal of Microscopy

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“…Recently, a probability density function (PDF)-based heterogeneous multiscale filtration (HMF) model was developed to calculate the filtration efficiency of clean GPFs by considering the heterogeneous microstructure of the substrate . Rather than using the mean pore size and mean porosity, a pore size PDF established from experimental observations , was introduced to represent the heterogeneous multiscale porous substrate. The HMF model was validated on three particulate filters with distinct mean pore sizes at various flow rates .…”

Section: Introductionmentioning

confidence: 99%

Dynamic Heterogeneous Multiscale Filtration Model: Probing Micro- and Macroscopic Filtration Characteristics of Gasoline Particulate Filters

Gong

Viswanathan

Rothamer

et al. 2017

Environ. Sci. Technol.

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Motivated by high filtration efficiency (mass- and number-based) and low pressure drop requirements for gasoline particulate filters (GPFs), a previously developed heterogeneous multiscale filtration (HMF) model is extended to simulate dynamic filtration characteristics of GPFs. This dynamic HMF model is based on a probability density function (PDF) description of the pore size distribution and classical filtration theory. The microstructure of the porous substrate in a GPF is resolved and included in the model. Fundamental particulate filtration experiments were conducted using an exhaust filtration analysis (EFA) system for model validation. The particulate in the filtration experiments was sampled from a spark-ignition direct-injection (SIDI) gasoline engine. With the dynamic HMF model, evolution of the microscopic characteristics of the substrate (pore size distribution, porosity, permeability, and deposited particulate inside the porous substrate) during filtration can be probed. Also, predicted macroscopic filtration characteristics including particle number concentration and normalized pressure drop show good agreement with the experimental data. The resulting dynamic HMF model can be used to study the dynamic particulate filtration process in GPFs with distinct microstructures, serving as a powerful tool for GPF design and optimization.

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“…When the substrate wall is fresh, soot particles penetrate deep into the substrate on the average depth of 30 mm and are trapped. 10 Although the particle sizes are significantly smaller than the pore size in the substrate, particles are trapped at high efficiencies by diffusion, interception, and impaction depending on the particle size and flow velocity. 11,12 The mass-based filtration efficiency under the early phase deposition is below 95%.…”

Section: Trapping Particlesmentioning

confidence: 99%

A review of soot sensors considered for on-board diagnostics application

Kamimoto

2016

International Journal of Engine Research

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If emissions control systems and engine control devices experience a failure, the engine system may malfunction causing exhaust emissions to exceed the standard levels of the regulated emissions. Thus, the functional status of each of all primary control devices needs to be monitored for detecting possible failures, so that the proper action can be taken to warn the vehicle operator. This article reviews a soot sensor currently developed for on-board diagnostics to monitor a diesel particulate filter and detect the failure. Original equipment manufacturers have selected a resistive soot sensor for the on-board diagnostics application because of its commercial feasibility in terms of functionality and cost. The sensor accumulates soot particles in exhaust gas on the sensing element. Structure, design, and function of the cumulative sensor including the signal processing techniques are described. Since the sensor signal contains noises and fluctuations, the post-diesel particulate filter soot concentration is repeatedly measured over 100 times and averaged in order to ensure the measurement accuracy, so that the diesel particulate filter failures can be detected. In addition to the resistive soot sensor, this article reviews the status of currently developed continuous soot sensors such as an electrostatic sensor, an electrical charging sensor, and a radio frequency-based sensor. These sensors have major issues of packaging and cost to be resolved prior to application to production vehicles. This review article is based primarily on scientific literatures most of which were published a few years ago, but the development of new soot sensor concepts has been continuing in the industry. Three new soot sensor concepts all of which are under development are also reported.

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Microscopic Visualization of PM Trapping and Regeneration in Micro-Structural Pores of a DPF Wall

Cited by 37 publications

References 10 publications

Electron microscopic time‐lapse visualization of surface pore filtration on particulate matter trapping process

Electron microscopic time‐lapse visualization of surface pore filtration on particulate matter trapping process

Dynamic Heterogeneous Multiscale Filtration Model: Probing Micro- and Macroscopic Filtration Characteristics of Gasoline Particulate Filters

A review of soot sensors considered for on-board diagnostics application

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