Research Papers

Cylinder Specific Pressure Predictions for Advanced Dual Fuel Compression Ignition Engines Utilizing a Two-Stage Functional Data Analysis

[+] Author and Article Information
Xiao Huang

Department of Applied Mathematics,
Illinois Institute of Technology,
Chicago, IL 60616
e-mail: xhuang23@hawk.iit.edu

Lulu Kang

Department of Applied Mathematics,
Illinois Institute of Technology,
Chicago, IL 60616
e-mail: lkang2@iit.edu

Mateos Kassa

Department of Mechanical,
Materials, and Aerospace Engineering,
Illinois Institute of Technology,
Chicago, IL 60616
e-mail: mkassa@hawk.iit.edu

Carrie Hall

Department of Mechanical,
Materials, and Aerospace Engineering,
Illinois Institute of Technology,
Chicago, IL 60616
e-mail: chall9@iit.edu

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received February 2, 2018; final manuscript received December 6, 2018; published online January 18, 2019. Assoc. Editor: Junmin Wang.

J. Dyn. Sys., Meas., Control 141(5), 051006 (Jan 18, 2019) (11 pages) Paper No: DS-18-1053; doi: 10.1115/1.4042252 History: Received February 02, 2018; Revised December 06, 2018

In-cylinder pressure is a critical metric that is used to characterize the combustion process of engines. While this variable is measured on many laboratory test beds, in-cylinder pressure transducers are not common on production engines. As such, accurate methods of predicting the cylinder pressure have been developed both for modeling and control efforts. This work examines a cylinder-specific pressure model for a dual fuel compression ignition engine. This model links the key engine input variables to the critical engine outputs including indicated mean effective pressure (IMEP) and peak pressure. To identify the specific impact of each operating parameter on the pressure trace, a surrogate model was produced based on a functional Gaussian process (GP) regression approach. The pressure trace is modeled as a function of the operating parameters, and a two-stage estimation procedure is introduced to overcome various computational challenges. This modeling method is compared to a commercial dual fuel combustion model and shown to be more accurate and less computationally intensive.

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Fig. 1

Example of in-cylinder pressure variations. The pressure near top dead center is zoomed in to show detail.

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Fig. 2

Dual fuel engine schematic

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Fig. 3

QQ plot of the residuals from the training dataset

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Fig. 4

Calculated average pressure curve e(dj) for j=1,…,m and the least square fitting just using k(d)

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Fig. 5

Cross-validation error in each step of the lasso procedure

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Fig. 6

Pressure versus crank angle for 1200 rpm, 8.2 bar BMEP on each cylinder: cylinders 1–3

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Fig. 7

Pressure versus crank angle for 1200 rpm, 8.2 bar BMEP on each cylinder: cylinders 4–6

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Fig. 8

Pressure versus crank angle for 1200 rpm, 8.2 bar BMEP on each cylinder: peak area

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Fig. 9

Root-mean-squared prediction error values of the 108 operational settings in the test dataset

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Fig. 10

Prediction error of IMEP of the 108 operational settings in the test dataset

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Fig. 11

Prediction for cylinder 1 using gtpower at 1200 rpm and 14.3 bar IMEP

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Fig. 12

Sensitivity analysis of the peak pressure and IMEP based on the fitted model



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