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THERMAL ISSUES IN EMERGING TECHNOLOGIES

Simulation on Flow and Heat Transfer in Diesel Particulate Filter

[+] Author and Article Information
Kazuhiro Yamamoto1

Department of Mechanical Science and Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi 464-8603, Japankazuhiro@mech.nagoya-u.ac.jp

Masamichi Nakamura

Department of Mechanical Science and Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi 464-8603, Japan

1

Corresponding author.

J. Heat Transfer 133(6), 060901 (Mar 04, 2011) (6 pages) doi:10.1115/1.4003448 History: Received November 02, 2009; Revised September 12, 2010; Published March 04, 2011; Online March 04, 2011

To reduce particulate matters including soot, a diesel particulate filter (DPF) has been developed for the after-treatment of exhaust gas. Since the filter is plugged with particles that would cause an increase of filter back-pressure, filter regeneration process is needed. However, there is not enough data on the phenomena in DPF because there are many difficulties in measurements. In this study, the flow in DPF is simulated by the lattice Boltzmann method. To focus on a real filter, the inner structure of the filter is scanned by a 3D X-ray computed tomography technique. By conducting tomography-assisted simulation, the local velocity and pressure distributions in the filter can be visualized, which is hardly obtained by measurements. Results show that, even in cold flow, the complex flow pattern is observed due to the nonuniformity of pore structure inside the filter. Based on the flow characteristics in the range of 0.2–20 m/s, simulation results show a good agreement with the empirical equation of Ergun equation. In the combustion simulation, the time-dependent temperature field inside the filter is visualized. As the temperature of inflow gas is increased, the filter regeneration process is promoted.

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Copyright © 2011 by American Society of Mechanical Engineers
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Figures

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Figure 1

(a) Photograph of cordierite filter and (b) PM trap inside porous filter wall. Calculation domain is shown by dotted line.

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Figure 2

D2Q9 model used in 2D simulation

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Figure 3

Upper figure shows an example of X-ray CT images. Lower figure shows digitized data of dotted area. Complex porous structure with variety of pore size is well observed.

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Figure 4

Profiles of (a) flow across filter wall, (b) mass flux in x-direction, and (c) pressure. Inflow velocity in cold flow is 1 m/s. These profiles are obtained under steady state.

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Figure 5

Distributions of pressure and porosity across filter wall. Inflow velocity in cold flow is 1 m/s. These profiles are obtained under steady state.

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Figure 6

Variations of friction factor with Reynolds number are shown, compared with the Ergun equation. Results in cold flow are shown under steady state.

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Figure 7

Time variation of temperature profile: (a) t=0.1 μs, (b) t=2 μs, and (c) t=10 μs. Inflow velocity is 1 m/s, and mass fraction of soot is 0.05. Oxygen volume concentration is 10 %, and temperature of inflow gas is 673 K. It is observed that temperature only in gas phase is increased at the beginning.

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Figure 8

Profiles of (a) temperature, (b) mass fraction of soot, (c) mass fraction of oxygen, and (d) reaction rate. Inflow velocity is 1 m/s, and mass fraction of soot is 0.05. Oxygen volume concentration is 10 %, and temperature of inflow gas is 673 K. These are obtained under steady state. Heat and mass transfer in soot oxidation is well visualized.

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Figure 9

Mass fraction of soot at filter exit is monitored. Inflow velocity is 1 m/s, and mass fraction of soot is 0.05. Oxygen volume concentration is 20 %, and temperature of inflow gas is varied from 373 K to 973 K. These are obtained under steady state.

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