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Research Papers: Gas Turbines: Combustion, Fuels, and Emissions

Propensity of Soot Deposition in a Rectangular Exhaust Gas Recirculation Cooler Using Kalman Filter

[+] Author and Article Information
Alireza Mirsadraee

Institute for Thermodynamics and
Thermal Engineering,
University of Stuttgart,
Pfaffenwaldring 6,
Stuttgart 70550, Germany
e-mail: mirsadraee@itw.uni-stuttgart.de

M. Reza Malayeri

School of Chemical,
Gas and Petroleum Engineering,
Shiraz University,
Shiraz 50278, Iran;
Institute for Thermodynamics and
Thermal Engineering,
University of Stuttgart,
Pfaffenwaldring 6,
Stuttgart 70550, Germany
e-mail: m.malayeri@itw.uni-stuttgart.de

1Corresponding author.

Contributed by the Combustion and Fuels Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received March 24, 2015; final manuscript received April 25, 2015; published online June 2, 2015. Editor: David Wisler.

J. Eng. Gas Turbines Power 137(12), 121503 (Jun 02, 2015) (9 pages) Paper No: GTP-15-1106; doi: 10.1115/1.4030519 History: Received March 24, 2015

The detection of fouling in exhaust gas recirculation (EGR) coolers of diesel engines should be fast and accurate. This would facilitate deciding an effective strategy to combat fouling and to prolong the lifetime of EGR coolers. In the present study, the propensity of soot deposition in a rectangular EGR cooler is modeled using Kalman filters. Noises, coherent feature of many deposition processes which can be resulted from measurement sensors such as thermocouples or incidental deposit flake-off, are also considered in the model. The Kalman filter minimizes the estimation error covariance by considering the measurement and process noise covariance matrices while it can simultaneously handle the noisy data. The results are characterized with measurement process noise covariance. The relation between these two defines the smoothness and shape of the estimated trend of fouling resistance. Comparisons of the experimental data and the resultant model confirmed the usefulness of the applied method for various operating conditions of an EGR cooler prone to particulate deposition of soot particles. The paper proceeds with the impact of such models in monitoring fouling and taking an appropriate mitigation approach in diesel engines.

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References

Abd-Elhady, M. S. , Malayeri, M. R. , and Müller-Steinhagen, H. , 2011, “Fouling Problems in Exhaust Gas Recirculation Coolers in the Automotive Industry,” Heat Transfer Eng., 32(3–4), pp. 248–257. [CrossRef]
Abd-Elhady, M. S. , and Malayeri, M. R. , 2014, “Mitigation of Soot Deposition in Exhaust Gas Recirculation Coolers Using a Spiral Insert,” Aerosol Sci. Technol., 48(2), pp.184–192. [CrossRef]
Hoard, J. , Abarham, M. , Styles, D. , Giuliano, J. , Sluder, C. , and Storey, J. M. , 2009, “Diesel EGR Cooler Fouling,” SAE Int. J. Engines, 1(1), pp. 1234–1250 .
Park, S. , Choi, K. , Kim, H. , and Lee, K. , 2010, “Influence of PM Fouling on Effectiveness of Heat Exchanges in a Diesel Engine With Fin-Type EGR Coolers of Different Sizes,” Heat Mass Transfer, 46(11–12), pp. 1221–1227. [CrossRef]
Kennedy, I. M. , 1997, “Models of Soot Formation and Oxidation,” Prog. Energy Combust. Sci., 23(2), pp. 95–132. [CrossRef]
Kalman, R. E. , 1960, “A New Approach to Linear Filtering and Prediction Problems,” ASME J. Basic Eng., 82(1), pp. 35–45. [CrossRef]
Welch, G. , and Bishop, G. , 2006, “An Introduction to the Kalman Filter,” University of North Carolina at Chapel Hill, Chapel Hill, NC, technical report.
Abd-Elhady, M. S. , Zornek, T. , Malayeri, M. R. , Balestrino, S. , Szymkowicz, P. G. , and Müller-Steinhagen, H. , 2011, “Influence of Gas Velocity on Particulate Fouling of Exhaust Gas Recirculation Coolers,” Int. J. Heat Mass Transfer, 54(4), pp. 838–846. [CrossRef]
Malayeri, M. R. , Zornek, T. , Balestrino, S. , Warey, A. , and Szymkowicz, P. G. , 2013, “Deposition of Nano-Sized Soot Particles in Various EGR Coolers Under Thermophoretic and Isothermal Conditions,” Heat Transfer Eng., 34(8–9), pp. 665–673. [CrossRef]

Figures

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Fig. 1

Sketch of the rectangular EGR cooler

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Fig. 2

Flowchart of estimating fouling resistance using the Kalman filter (tend was the time where the experiment was ended)

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Fig. 3

Estimation of normalized fouling resistance using Kalman filter for various Q=0.001-100 and R=0.001-100 (left: experiment number 1 (Tin=400 °C, Uin=30 m/s, and Cin=105 mg/m3) and right: experiment number 2 (Tin=250 °C, Uin=40 m/s, and Cin=70 mg/m3))

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Fig. 4

Estimation of normalized fouling resistance using Kalman filter for various Q=0.001-100 and R=0.001-100 (left: experiment number 3 (Tin=400 °C, Uin=30 m/s, and Cin=100 mg/m3) and right: experiment number 4 (Tin=400 °C, Uin=10 m/s, and Cin=360 mg/m3))

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Fig. 5

Estimation of fouling resistance using Kalman filter with R=0.001 and Q=100 for Tin=400 °C, Uin=30 m/s, and Cin=105 mg/m3

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Fig. 6

Estimation of fouling resistance using Kalman filter with R=0.001 and Q=100 for Tin=250 °C, Uin=40 m/s, and Cin=70 mg/m3

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Fig. 7

Estimation of fouling resistance using Kalman filter with R=0.001 and Q=100 for Tin=400 °C, Uin=30 m/s, and Cin=100 mg/m3

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Fig. 8

Estimation of fouling resistance using Kalman filter with R=0.001 and Q=100 for Tin=400 °C, Uin=10 m/s, and Cin=360 mg/m3

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