Predicting Cycle-to-Cycle Variation With Concurrent Cycles in a Gasoline Direct Injected Engine With Large Eddy Simulations

High cycle-to-cycle variation (CCV) is detrimental to engine performance, as it leads to poor combustion and high noise and vibration. In this work, CCV in a gasoline engine is studied using large eddy simulation (LES). The engine chosen as the basis of this work is a single-cylinder gasoline direct injection (GDI) research engine. Two stoichiometric part-load engine operating points (6 brake mean effective pressure (BMEP) and 2000 revolutions per minute) were evaluated: a nondilute (0% exhaust gas recirculation (EGR)) case and a dilute (18% EGR) case. The experimental data for both operating conditions had 500 cycles. The measured CCV in indicated mean effective pressure (IMEP) was 1.40% for the nondilute case and 7.78% for the dilute case. To estimate CCV from simulation, perturbed concurrent cycles of engine simulations were compared with consecutively obtained engine cycles. The motivation behind this is that running consecutive cycles to estimate CCV is quite time consuming. For example, running 100 consecutive cycles requires 2–3 months (on a typical cluster); however, by running concurrently, one can potentially run all 100 cycles at the same time and reduce the overall turnaround time for 100 cycles to the time taken for a single cycle (2 days). The goal of this paper is to statistically determine if concurrent cycles, with a perturbation applied to each individual cycle at the start, can be representative of consecutively obtained cycles and accurately estimate CCV. 100 cycles were run for each case to obtain statistically valid results. The concurrent cycles began at different timings before the combustion event, with the motivation to identify the closest time before spark to minimize the run time. Only a single combustion cycle was run for each concurrent case. The calculated standard deviation of peak pressure and coefficient of variance (COV) of IMEP were compared between the consecutive and concurrent methods to quantify CCV. It was found that the concurrent method could be used to predict CCV with either a velocity or numerical perturbation. Both a large and small velocity perturbations were compared, and both produced correct predictions, implying that the type of perturbation is not important to yield a valid realization. Starting the simulation too close to the combustion event, at intake valve close (IVC) or at spark timing, underpredicted the CCV. When concurrent simulations were initiated during or before the intake even, at start of injection (SOI) or earlier, distinct and valid realizations were obtained to accurately predict CCV for both operating points. By simulating CCV with concurrent cycles, the required wall clock time can be reduced from 2–3 months to 1–2 days. In addition, the required core-hours can be reduced up to 41%, since only a portion of each cycle needs to be simulated.

[1]  Xiaofeng Yang,et al.  Parallel methodology to capture cyclic variability in motored engines , 2017 .

[2]  P. Senecal,et al.  Capturing Cyclic Variability in Exhaust Gas Recirculation Dilute Spark-Ignition Combustion Using Multicycle RANS , 2016 .

[3]  J. Naber,et al.  Numerical Investigation of Spark Ignition Events in Lean and Dilute Methane/Air Mixtures Using a Detailed Energy Deposition Model , 2016 .

[4]  P. K. Senecal,et al.  Cycle-to-Cycle Variations in Multi-Cycle Engine RANS Simulations , 2016 .

[5]  Sebastiano Breda,et al.  LES Modelling of Spark-Ignition Cycle-to-Cycle Variability on a Highly Downsized DISI Engine , 2015 .

[6]  Stephen Ciatti,et al.  Computational Fluid Dynamics Simulation of Gasoline Compression Ignition , 2015 .

[7]  Stephen Ciatti,et al.  Achieving Stable Engine Operation of Gasoline Compression Ignition Using 87 AKI Gasoline Down to Idle , 2015 .

[8]  Christopher J. Rutland,et al.  Large-Eddy simulation analysis of spark configuration effect on cycle-to-cycle variability of combustion and knock , 2015 .

[9]  Xiaofeng Yang,et al.  RANS and LES of IC Engine Flows: A Comparative Study , 2013 .

[10]  T. Poinsot,et al.  Large-Eddy Simulation and experimental study of cycle-to-cycle variations of stable and unstable operating points in a spark ignition engine , 2012 .

[11]  A. Benkenida,et al.  Towards the understanding of cyclic variability in a spark ignited engine using multi-cycle LES , 2009 .

[12]  Shu-jin Cao A Novel Hybrid Scheme for Large Eddy Simulation of Turbulent Combustion Based on the One-Dimensional Turbulence Model , 2006 .

[13]  John Abraham,et al.  Modeling the outcome of drop–drop collisions in Diesel sprays , 2002 .

[14]  Christopher J. Rutland,et al.  A new droplet collision algorithm , 2000 .

[15]  A. A. Amsden,et al.  KIVA-II: A Computer Program for Chemically Reactive Flows with Sprays , 1989 .

[16]  F. Millo,et al.  Multi-cycle Large Eddy Simulation to Capture Cycle-to-Cycle Variation (CCV) in Spark-Ignited (Si) Engines , 2017 .

[17]  Thierry Poinsot,et al.  LES study of cycle-to-cycle variations in a spark ignition engine , 2011 .

[18]  F. A. Williams,et al.  3. Turbulent Combustion , 1985, The Mathematics of Combustion.

[19]  Forman A. Williams,et al.  The Mathematics of Combustion , 1985, Frontiers in applied mathematics.

[20]  Narendra J. Sheth,et al.  Statistical Design and Analysis of Engineering Experiments , 1973 .