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Research Papers: Fuel Combustion

Numerical Prediction of Cyclic Variability in a Spark Ignition Engine Using a Parallel Large Eddy Simulation Approach

[+] Author and Article Information
Muhsin M. Ameen

Argonne National Laboratory,
9700 S Cass Avenue,
Lemont, IL 60439
e-mail: mameen@anl.gov

Mohsen Mirzaeian

Politecnico di Torino,
c.so Duca degli Abruzzi, 24,
Torino 10129, Italy
e-mail: mohsen.mirzaeian@polito.it

Federico Millo

Politecnico di Torino,
c.so Duca degli Abruzzi, 24,
Torino 10129, Italy
e-mail: federico.millo@polito.it

Sibendu Som

Argonne National Laboratory,
9700 S Cass Avenue,
Lemont, IL 60439
e-mail: ssom@anl.gov

1Corresponding author.

Contributed by the Internal Combustion Engine Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received February 16, 2018; final manuscript received February 19, 2018; published online March 29, 2018. Editor: Hameed Metghalchi.The United States Government retains, and by accepting the article for publication, the publisher acknowledges that the United States Government retains, a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for United States government purposes.

J. Energy Resour. Technol 140(5), 052203 (Mar 29, 2018) (10 pages) Paper No: JERT-18-1137; doi: 10.1115/1.4039549 History: Received February 16, 2018; Revised February 19, 2018

Cycle-to-cycle variability (CCV) is detrimental to IC engine operation and can lead to partial burn, misfire, and knock. Predicting CCV numerically is extremely challenging due to two key reasons. First, high-fidelity methods such as large eddy simulation (LES) are required to accurately resolve the in-cylinder turbulent flow field both spatially and temporally. Second, CCV is experienced over long timescales and hence the simulations need to be performed for hundreds of consecutive cycles. Ameen et al. (2017, “Parallel Methodology to Capture Cyclic Variability in Motored Engines,” Int. J. Engine Res., 18(4), pp. 366–377.) developed a parallel perturbation model (PPM) approach to dissociate this long time-scale problem into several shorter time-scale problems. This strategy was demonstrated for motored engine and it was shown that the mean and variance of the in-cylinder flow field was captured reasonably well by this approach. In the present study, this PPM approach is extended to simulate the CCV in a fired port-fuel injected (PFI) spark ignition (SI) engine. The predictions from this approach are also shown to be similar to the consecutive LES cycles. It is shown that the parallel approach is able to predict the coefficient of variation (COV) of the in-cylinder pressure and burn rate-related parameters with sufficient accuracy, and is also able to predict the qualitative trends in CCV with changing operating conditions. It is shown that this new approach is able to give accurate predictions of the CCV in fired engines in less than one-tenth of the time required for the conventional approach of simulating consecutive engine cycles.

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Figures

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Fig. 2

(a) Engine configuration showing the location of the valves, intake and exhaust ports, spark plug and the piston and (b) location of the window chosen for calculating the statistics of flow field and equivalence ratio at 1 deg. Before spark timing in Figs. 58.

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Fig. 3

Mean velocity magnitude (m/s) and velocity vectors for case A at 10 deg before TDC of compression along the Y = 0 plane based on (a) 50 consecutive LES cycles and (b) 50 PPM LES cycles

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Fig. 4

RMS velocity magnitude (m/s) for case A at 10 deg before TDC of compression along the Y = 0 plane based on (a) 50 consecutive LES cycles and (b) 50 PPM LES cycles

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Fig. 5

Mean equivalence ratio magnitude for case A at 10 deg before TDC of compression along the Y = 0 plane based on (a) 50 consecutive LES cycles and (b) 50 PPM LES cycles

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Fig. 6

RMS equivalence ratio magnitude for case A at 10 deg before TDC of compression along the Y = 0 plane based on (a) 50 Consecutive LES cycles and (b) 50 PPM LES cycles

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Fig. 7

Comparison of (a) mean Y-tumble ratio and (b) RMS of the Y-tumble ratio between the consecutive and PPM LES cycles for case A

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Fig. 1

Flowchart showing the PPM approach to branch out multiple parallel simulations by perturbing the initial velocity field to compute CCV

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Fig. 14

Comparison of COV of Pmax, IMEP and burn rate-related quantities between experiment and PPM LES

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Fig. 15

Comparison of the COV of (a) Pmax and (b) CA10-75 computed from cycles 2 and 3 of the PPM LES calculations for case B

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Fig. 8

Comparison of the indicated works of consecutive cycles for (a) case A and (b) case B

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Fig. 9

Variability of the trapped mass among the different cycles for consecutive LES and PPM LES for case A

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Fig. 10

Comparison of the experimental pressure traces with (a) 50 consecutive LES cycles and (b) 100 PPM LES cycles for case A. The blue lines correspond to the 1000 experimental cycles and the red lines correspond to the LES cycles. (Refer to the web version for color)

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Fig. 11

Comparison of COV of Pmax, IMEP and burn rate related quantities between experiment, consecutive LES and PPM LES

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Fig. 12

Comparison of the experimental pressure traces with 100 PPM LES cycles for case B. The blue lines correspond to the 1000 experimental cycles and the red lines correspond to the 100 LES cycles.

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Fig. 13

Variation of COV of Pmax with increasing number of cycles for cases A and B from (a) experimental measurements and (b) PPM LES

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