Capturing Cyclic Variability in SI Engines with High-Fidelity LES using a New Parallel Perturbation Approach

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1 International Multidimensional Engine Modeling User s Group Meeting at the SAE Congress April 3, 2017 Detroit, Michigan Capturing Cyclic Variability in SI Engines with High-Fidelity LES using a New Parallel Perturbation Approach Muhsin M Ameen 1,*, Sibendu Som 1 1 Argonne National Laboratory, Lemont, IL, USA * Corresponding Author mameen@anl.gov 1. Introduction Cycle-to-cycle variability (CCV) is known to be detrimental to SI engine operation and can result in partial burn, knock and an overall reduction in the reliability of the engine. Prior studies [1] have shown that CCV is caused by a combination of factors including variations in in-cylinder flow pattern, mixture inhomogeneity, turbulence intensity and spark discharge characteristics. The relative importance of these factors will depend on the engine geometry and operating conditions. There have been several experimental studies [2-5] to study CCV in SI engines. However, the experimental studies face a major drawback in that it is difficult to isolate the different coupled processes and hence cannot be used to explain the causes of CCV. Numerical prediction of CCV is extremely challenging for two key reasons: (i) high-fidelity methods such as large eddy simulation (LES) are required to accurately capture the in-cylinder turbulent flow field, and (ii) CCV is experienced over long timescales and hence the simulations need to be performed for hundreds of consecutive cycles. In spite of these challenges, there have been several numerical studies in recent times where multi-cycle LES has been used to predict CCV. These include simplified geometries resembling piston and/or valve motions [6, 7], realistic engine geometries under motored conditions [8, 9] and under fired conditions [10, 11]. These numerical studies have shown the importance of the in-cylinder flowfield and its cyclic variability on CCV. However the numerical simulation of CCV using multi-cycle LES is still extremely expensive and performing these calculations on computing clusters would require a few months of computational time and thus would not be realistically used in the engine design cycle. Ameen et al. [12] recently developed the parallel perturbation model (PPM), a methodology to reduce the turnaround time in simulating CCV without appreciably sacrificing the accuracy. The strategy is to perform multiple parallel simulations, each of which encompasses 2-3 cycles, by effectively perturbing the simulation parameters such as the initial and boundary conditions. The methodology is schematically shown in Fig. 1. More details of this methodology are explained in Ameen et al. [12]. This methodology was validated for the prediction of CCV due to gas exchange in a motored transparent combustion chamber (TCC) engine by comparing the flowfield statistics with particle image velocimetry (PIV) measurements. It was shown that by perturbing the initial velocity field effectively based on the intensity of the in-cylinder turbulence, the mean and variance of the in-cylinder flow field was captured reasonably well. Recently, Zhao et al. [13] performed LES of 50 consecutive cycles for a fired port fuel injected (PFI) SI engine. They employed the G-equation approach to model the flame propagation and showed that the simulations were able to accurately predict the cyclic variability from the experiments. They also showed that, by decoupling the effects of the velocity field and the equivalence ratio field, the velocity field and not the equivalence ratio field is what dominated the CCV for this engine configuration. This motivates the use of PPM to simulate the CCV for this engine configuration, since PPM was shown to be effective in capturing the cyclic variability of the in-cylinder flowfield. In this paper, the PPM approach is extended to simulate the CCV in a fired PFI SI engine previously studied by Zhao et al. [13]. Two operating conditions are considered a medium CCV operating case corresponding to 2500 rpm and 16 bar BMEP which was studied by Zhao et al. [13] and a low CCV case corresponding to 4000 rpm and 12 bar BMEP. In the next section, the numerical setup employed will be discussed briefly. This will be followed by a 1

2 comparison of the simulated results with the experiments as well as with the consecutive cycle LES results from Zhao et al. [13]. Figure 1. Flowchart showing the Parallel Perturbation Model (PPM) approach 2. Numerical Setup The simulations were performed using the commercial CFD code CONVERGE v2.3 [14]. The numerical setup employed in the present study is exactly the same as that employed by Zhao et al. [13] and hence not shown here for brevity. Table 1 summarizes the important details of the CFD setup. The computational domain is schematically shown in Fig. 2. In the present study, two different operating conditions are employed, the details of which are summarized in Table 2. Case A corresponds to a late spark timing medium CCV condition, and Case B corresponds to a low CCV condition. For both these conditions, 100 parallel cycles were simulated using the approach shown in Fig. 1. The turnaround time for the parallel cycles is about 10 times smaller than the consecutive cycle LES. Table 1. Computational setup CFD software CONVERGE V2.3 Injection model Blob Break-up model KH-RT Collision model NTC Dray-law Dynamic Evaporation model Frossling correlation Combustion model G-Equation Turbulence model LES-dynamic structure Base mesh size 2.8 mm Minimum grid size mm Fixed embedding level 4 in spark gap; 3 in intermediate region around spark gap; 3 in intake valve angle and 2 in exhaust valve angle Time discretization PISO Fuel 95% isooctane, 5% n-heptane 2

3 Figure 2 Engine configuration Table 2. Operating conditions Case A Case B r/min IMEP (bar) Fueling (mg/cycle/cylinder) Spark timing (degrees) Injection timing (degrees) Results and Discussion The principal advantage of the PPM approach is the fast turnaround in predicting the cyclic variability of the incylinder flowfield. Swirl ratio is one of the important indicators of the flowfield and have shown to play a strong role on the burning velocity and CCV [15]. Figure 3 compares the mean and standard deviation in the swirl ratio predicted by the PPM and the consecutive cycle approaches for Case A. It can be seen that both the approaches show good agreement with each other. This is similar to the observation by Ameen et al. [12] for the motored TCC engine. Figure 3. Comparison of the mean and standard deviation of swirl between the consecutive and parallel LES for Case A Figure 4 compares the experimentally measured pressure traces with the mean in-cylinder pressures from the LES cycles. Figure 4(a) shows the pressure traces from the consecutive cycles [13] and Fig. 4(b) shows the pressure traces from the current study. For the experiments, 1000 cycles are considered while the consecutive and parallel LES uses 50 and 100 cycles respectively. It can be seen that both the consecutive and parallel LES approaches show good 3

4 agreement with the experimental measurements. The simulations show a slight discrepancy during the expansion stroke. One of the possible reasons for this could be due to the inaccuracies in the heat transfer models. More importantly, it can be seen that both the consecutive and parallel LES approaches show remarkable similarity with each other although the parallel approach is almost 10-times faster than the consecutive cycle approach. Figure 4. In-cylinder pressure traces for (a) 50 consecutive cycles and (b) 100 parallel cycles for Case A. Also shown are the pressure traces from 1000 experimental cycles. The cyclic variability can be quantified in terms of coefficient of variance (COV), which is the standard deviation divided by the mean value. Figure 5 compares the COV of maximum pressure (Pmax) and burn-rate parameters (CA10, CA50, CA10-75) between the experimental measurements and the simulations. It can be noted that the parallel LES underpredicts the COV as compared the consecutive LES by 1-5%. There are two possible reasons for this behavior: (a) the parallel LES only considers perturbations in the flowfield and ignores variabilities in mixture composition, temperature distributions and so on, and (b) only 50 cycles were performed with consecutive LES while experimental results have shown that at least 100 cycles are required to predict accurate values for COV. The second observation is that the discrepancy between parallel and consecutive LES is the largest for COV of CA10, which is an indicator of the variability in the early stage of combustion, while the difference is less than 3% for the other quantities. This signifies that the cyclic variability in the early stage of combustion could be affected more strongly by the cyclic variabilities of mixture composition and temperature distributions, while the variability in the later stages are driven by the flowfield variabilities. Overall, both the LES approaches show a relatively good quantitative agreement for the COV with the experimental observations. Figure 5. COV of maximum pressure, CA10, CA50, and CA10-75 for experimental, consecutive LES and parallel LES results for Case A. 4

5 All the results in Figs. 3-5 were for Case A, which is a medium CCV operating condition. Parallel LES was also performed for the lower CCV Case B (refer to Table 2). Consecutive cycle LES was not performed for this condition. The objective of this simulation was to determine whether LES was able to correctly predict the trend in CCV with changes in operating conditions. Figure 6 compares the pressure traces from the experimental and parallel LES results for Case B. It can be seen that the LES results agree well with the experimental measurements at this operating condition as well. Figure 7 compares the COV of Pmax between the experiments and parallel LES for Cases A and B. The COV of Pmax predicted by the experiment are 7.6% and 4.1% whereas the parallel LES predicts 9.1% and 6% respectively for Cases A and B. Thus, the parallel LES approach can accurately predict the trends in COV with changing operating conditions. More analysis is needed to compare the COV in other parameters such as IMEP, burnrate parameters and so on for Case B. Figure 6. Comparison of the pressure traces between experimental (Blue) and parallel LES (Red) results for Case B. (a) (b) Figure 7. Variation of COV of Pmax with increasing number of cycles for (a) Experiment and (b) Parallel LES 4. Conclusions In this work, the parallel perturbation model (PPM) approach introduced by Ameen et al. [12] for speeding up CCV simulations was extended to a fired PFI engine. The idea is to launch multiple parallel simulations simultaneously by effectively perturbing the initial flowfield of each of these simulations. It is shown that the PPM approach is able to accurately predict the cyclic variability in the in-cylinder flowfield, pressure and burn rates. It is also shown that the parallel LES approach is able to correctly predict the trends in the CCV with changing operating conditions. It is also shown that the results from the parallel LES match the results from consecutive cycle approach reasonably well. The turnaround time for the parallel LES approach is about 10 times shorter than the consecutive cycle approach. This implies that these CCV calculations can be completed in a few days and can thus be realistically employed in the engine design cycle. 5

6 5. Acknowledgements The research was funded by DOEs Office of Vehicle Technologies, Office of Energy Efficiency and Renewable Energy under Contract No. DE-AC02-06CH The authors wish to acknowledge the computational resources of Fusion and Blues clusters at Argonne National Laboratory. The authors would also like to thank Dr. Janardhan Kodavasal from Argonne National Laboratory for the help with the numerical setup. The authors would also like to acknowledge Mohsen Mirzaeian and Prof. Frederico Millo from Politecnico di Torino for sharing the experimental data for comparison. 6. References 1. Heywood, J. B., 1988, Internal combustion engine fundamentals, Mcgraw-hill New York. 2. Funk, C., Sick, V., Reuss, D. L., and Dahm, W. J., 2002, "Turbulence properties of high and low swirl in-cylinder flows," No , SAE Technical Paper. 3. Schiffmann, P., Gupta, S., Reuss, D., Sick, V., Yang, X., and Kuo, T.-W., 2016, "TCC-III Engine Benchmark for Large-Eddy Simulation of IC Engine Flows," Oil & Gas Science and Technology Revue d IFP Energies nouvelles, 71(1), p Baum, E., Peterson, B., Surmann, C., Michaelis, D., Böhm, B., and Dreizler, A., 2013, "Investigation of the 3D flow field in an IC engine using tomographic PIV," Proceedings of the Combustion Institute, 34(2), pp Buhl, S., Gleiss, F., Köhler, M., Hartmann, F., Messig, D., Brücker, C., and Hasse, C., 2016, "A combined numerical and experimental study of the 3D tumble structure and piston boundary layer development during the intake stroke of a gasoline engine," Flow, Turbulence and Combustion, pp Moureau, V., Barton, I., Angelberger, C., and Poinsot, T., 2004, "Towards large eddy simulation in internalcombustion engines: simulation of a compressed tumble flow," No , SAE Technical Paper. 7. Naitoh, K., Ono, M., Kuwahara, K., and Krause, E., 2002, "Cycle-resolved computations of compressible flow in engine," No , SAE Technical Paper. 8. Yang, X., Gupta, S., Kuo, T.-W., and Gopalakrishnan, V., 2014, "RANS and Large Eddy Simulation of Internal Combustion Engine Flows A Comparative Study," Journal of Engineering for Gas Turbines and Power, 136(5), p Enaux, B., Granet, V., Vermorel, O., Lacour, C., Thobois, L., Dugué, V., and Poinsot, T., 2011, "Large eddy simulation of a motored single-cylinder piston engine: numerical strategies and validation," Flow, turbulence and combustion, 86(2), pp Vermorel, O., Richard, S., Colin, O., Angelberger, C., Benkenida, A., and Veynante, D., 2009, "Towards the understanding of cyclic variability in a spark ignited engine using multi-cycle LES," Combustion and Flame, 156(8), pp Schmitt, M., Hu, R., Wright, Y. M., Soltic, P., and Boulouchos, K., 2015, "Multiple cycle LES simulations of a direct injection natural gas engine," Flow, Turbulence and Combustion, 95(4), pp Ameen, M. M., Yang, X., Kuo, T.-W., and Som, S., 2016, "Parallel methodology to capture cyclic variability in motored engines," International Journal of Engine Research, p Zhao, E., Moiz, A. A., Som, S., Fogla, N., Bybee, M., Wahiduzzaman, S., Mirzaeian, M., Millo, F., and Kodavasal, J., 2017, "Multi-cycle large eddy simulation to capture cycle-to-cycle variation (CCV) in spark-ignited (SI) engines," Publication in Preparation. 14. Richards, K. J., Senecal, P. K., and Pomraning, E., 2016, "CONVERGE (v2.3)," Convergent Science, Inc., Madison, WI. 15. Nagayama, I., Araki, Y., and Iioka, Y., 1977, "Effects of swirl and squish on SI engine combustion and emission," Society of Automotive Engineers, Warrendale, PA. 6