LES Modeling Study on Cycle-to-Cycle Variations in a DISI Engine

SAE technical papers on CD-ROM/SAE technical paper series, vol. 1

Abstract

The reduction of cycle-to-cycle variations (CCV) is a prerequisite for the development and control of spark-ignition engines with increased efficiency and reduced engine-out emissions. To this end, Large-Eddy Simulations (LES) can improve the understanding of stochastic in-cylinder phenomena during the engine design process, if the employed modeling approach is sufficiently accurate. In this work, an inhouse code has been used to investigate CCV in a direct-injected spark ignition (DISI) engine under fuel-lean conditions with respect to a stoichiometric baseline operating point. It is shown that the crank angle when a characteristic fuel mass fraction is burned, e.g. MFB50, correlates with the equivalence ratio computed as a local average in the vicinity of the spark plug. The lean operating point exhibits significant CCV, which are shown to be correlated also with the in-cylinder subfilter-scale (SFS) kinetic energy. To assess the predictive capabilities of the turbulent combustion modeling approach, an engine-relevant Direct Numerical Simulation (DNS) dataset of lean premixed flame propagation in homogeneous isotropic turbulence is considered for a-posteriori investigations. LES predictions using the Flame Front/Progress Variable Equation Model are demonstrated to be in good agreement with the DNS results. Integral flame propagation results are shown to be unaffected by the choice of two eddy viscosity models, although noticeable differences in the SFS velocity distributions near the flame front exist between the Dynamic Smagorinsky Model (DSM) and the Coherent Structure Model (CSM).

Authors 8

1. Tobias Falkenstein Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

2. Marco Davidovic Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

3. Antonio Attili Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

4. Mathis Bode Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

5. Hongchao Chu Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

6. Seongwon Kang

   Sogang University

   Affiliation as printed

   Sogang University

7. Heinz G. Pitsch Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

8. Hiroyoshi Taniguchi

   Honda (Japan) · Honda R&D Co., Ltd. (Japan)

   Affiliation as printed

   Honda R&D Co., Ltd

   Honda R&D Co.,Ltd