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

Gray-Box Modeling for Performance Control of an HCCI Engine With Blended Fuels

[+] Author and Article Information
M. Bidarvatan

Department of Mechanical
Engineering-Engineering Mechanics,
Michigan Technological University,
Houghton, MI 49930
e-mail: mbidarva@mtu.edu

M. Shahbakhti

Department of Mechanical
Engineering-Engineering Mechanics,
Michigan Technological University,
Houghton, MI 49930
e-mail: mahdish@mtu.edu

Contributed by the Combustion and Fuels Committee of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received February 16, 2014; final manuscript received February 23, 2014; published online May 2, 2014. Editor: David Wisler.

J. Eng. Gas Turbines Power 136(10), 101510 (May 02, 2014) (7 pages) Paper No: GTP-14-1101; doi: 10.1115/1.4027278 History: Received February 16, 2014; Revised February 23, 2014

High fidelity models that balance accuracy and computation load are essential for real-time model-based control of homogeneous charge compression ignition (HCCI) engines. Gray-box modeling offers an effective technique to obtain desirable HCCI control models. In this paper, a physical HCCI engine model is combined with two feed-forward artificial neural network models to form a serial architecture gray-box model. The resulting model can predict three major HCCI engine control outputs, including combustion phasing, indicated mean effective pressure (IMEP), and exhaust gas temperature (Texh). The gray-box model is trained and validated with the steady-state and transient experimental data for a large range of HCCI operating conditions. The results indicate that the gray-box model significantly improves the predictions from the physical model. For 234 HCCI conditions tested, the gray-box model predicts combustion phasing, IMEP, and Texh with an average error of less than 1 crank angle degree, 0.2 bar, and 6 °C, respectively. The gray-box model is computationally efficient and it can be used for real-time control application of HCCI engines.

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Figures

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Fig. 4

Architecture of the HCCI gray-box model

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Fig. 3

Range of the engine combustion phasing, load, and exhaust gas temperature for the experimental data points shown in Fig. 2

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Fig. 2

Operating range for 233 experimental HCCI data points used in this study (external EGR = 0)

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Fig. 5

Feedforward neural network models used in this study

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Fig. 11

Steady-state experimental validation of the empirical Texh model

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Fig. 9

Training and validation results of Texh for the gray-box model: (a) training, and (b) validation

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Fig. 10

Validation of the gray-box models for transient fueling conditions (Pm = 110 kPa, Tm = 91 °C, external EGR = 0%, Pexh = 99 kPa, and N = 815 rpm)

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Fig. 6

Training metrics for the ANN-1. Normalized MSE in the y-axis is the average of the normalized MSEs for CA50, IMEP, and Texh.

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Fig. 7

Training MSE over the iteration history. The normalized MSE in the y-axis is the average of the normalized MSEs for the CA50, IMEP, and Texh.

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Fig. 8

Training and validation results of the CA50-IMEP for the gray-box model: (top) training, and (bottom) validation

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Fig. 1

Background of the HCCI engine control modeling in literature

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