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

A Quasi-dimensional Model of the Ignition Delay for Combustion Modeling in Spark-Ignition Engines

[+] Author and Article Information
Sebastian Grasreiner

Powertrain Development,
BMW Group,
München 80788, Germany
e-mail: sebastian.sg.grasreiner@bmw.de

Jens Neumann

Powertrain Development,
BMW Group,
München 80788, Germany
e-mail: jens.je.neumann@bmw.de

Michael Wensing

Professor
Institute of Engineering Thermodynamics,
Friedrich-Alexander-Universität
Erlangen-Nürnberg,
Erlangen 91058, Germany
e-mail: michael.wensing@ltt.uni-erlangen.de

Christian Hasse

Professor
Chair of Numerical Thermo-Fluid Dynamics,
Department of Energy Process Engineering
and Chemical Engineering,
Technische Universität Bergakademie Freiberg,
Freiberg 09599, Germany
e-mail: christian.hasse@iec.tu-freiberg.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 August 14, 2014; final manuscript received October 9, 2014; published online December 17, 2014. Editor: David Wisler.

J. Eng. Gas Turbines Power 137(7), 071502 (Jul 01, 2015) (7 pages) Paper No: GTP-14-1486; doi: 10.1115/1.4029100 History: Received August 14, 2014; Revised October 09, 2014; Online December 17, 2014

Quasi-dimensional (QD) modeling of combustion in spark-ignition (SI) engines allows to describe the most relevant processes of heat release. Here, a submodel for the ignition delay is introduced and applied. The start of combustion is considered from ignition to the crank angle of 5% burned gas fraction. The introduced physical approach identifies the turbulent propagation velocity of the initiated kernel by taking into account early flame expansion and geometric restrictions of the flame propagation. The model is applied to stationary operation within an entire engine map of a turbocharged direct injection SI engine with fully variable valvetrain. Based on provided cycle-averaged input data, the model delivers good results within the margins of measured cycle-to-cycle fluctuations. Thus, it contributes to the assessment of the interplay between engine, engine control unit, drivetrain, and vehicle dynamics, hence making a step toward optimization and virtual engine calibration.

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References

Figures

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Fig. 1

Flow chart of the QD combustion model with submodel requirements for ignition delay marked (gray)

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Fig. 2

Measured relation between α5 and crank angle of 50% mass burned α50 (left). Measured relation between α50 and indicated mean effective pressure imep of the high-pressure part of the working cycle (right).

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Fig. 3

Fitted probability distribution of flame development angles α5 from indicated measurements of 256 consecutive cycles: symmetric (left) and asymmetric (right)

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Fig. 4

Expansion factor Ex at different stages of combustion (ignition, 5%, 50%, and 90% burned fraction) for a variation of engine load

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Fig. 5

Validation results for ignition delay prediction (predicted—model) as absolute deviation (degCA) and compared to targeted accuracies of ±3 degCA (bold contours)

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Fig. 6

Definition of the margins of fluctuation according to the average of the 50 most extreme cycles out of 256 consecutive cycles (dashed lines) and according to the absolute fluctuation of 256 consecutive cycles (solid lines)

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Fig. 7

Deviation of predicted and measured flame development angles α5 with respect to the defined margins of fluctuation for the critical operating points of Fig. 5 with the highest absolute deviations

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Fig. 8

Modeled reaction of ignition delay for a variation of cylinder air charge at 2000 rpm and constant ignition timings

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Fig. 9

Modeled reaction of ignition delay for a variation of residual gas fractions at 2000 rpm and constant ignition timings

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