Transported PDF Calculations of Combustion in Compression- Ignition Engines

Transcription

1 International Multidimensional Engine Modeling User s Group Meeting at the SAE Congress Detroit, MI 15 April 2013 Transported PDF Calculations of Combustion in Compression- Ignition Engines V. Raj Mohan 1, D. C. Haworth 1 and J. Li 2 1 Department of Mechanical and Nuclear Engineering, The Pennsylvania State University, University Park, Pennsylvania 16802, USA 2 Group Trucks Technology, Volvo Group, Hagerstown, Maryland 21742, USA A transported probability density function (PDF) method is used to model the in-cylinder combustion processes in a compression-ignition heavy-duty engine. In chemically reacting turbulent flows such as in-cylinder flows, turbulent fluctuations are coupled with the reaction-rate chemistry in highly non-linear ways. Hence, the combustion models should be able to capture the interactions between the reaction-rate chemistry and the unresolved turbulent fluctuations in composition and temperature. The transported PDF model has the unique advantage of capturing these turbulent-chemistry interactions (TCI) in a direct manner with minimal approximations. In this paper, recent results from in-cylinder combustion simulations using the transported PDF model for two different operating conditions for a heavy duty engine are presented. The computed pressure traces agree reasonably well with experimental data. More importantly, significant differences are found between the results obtained using the transported PDF model that explicitly accounts for TCI and those obtained using a model that does not account for TCI. In addition, sensitivities to variations in gas-phase chemistry models are studied using different chemical mechanisms. These results indicate that the choice of gas-phase chemical mechanism is critical in predicting the in-cylinder combustion characteristics accurately. 1. Introduction Heavy-duty vehicles are substantial users of petroleum-based fuels and significant contributors to green-house gases. With increasing concern about rising fuel prices, limited petroleum supplies and greenhouse-gas emissions, the need for more advanced, efficient and cleaner compression-ignition engines for heavy-duty vehicles is now greater than ever. Several advanced combustion strategies have been explored recently that have the potential to decrease the fuel consumption in compression-ignition engines for heavy-duty vehicles. Reference [1] gives an overview of the recent advances in the area of internal combustion engines research and the importance of the need to develop next-generation clean and efficient engines. Predictive computational fluid dynamics (CFD)-based models are needed to explore these 1

2 modern combustion strategies, and turbulent combustion models play a vital role in predicting the in-cylinder combustion processes based on these advanced combustion modes. Many of these strategies are based on largely varying thermochemical conditions (extremely high pressures, low temperatures, and/or high EGR rates) and involve multiple fuel injections per engine cycle, and in some cases, more than one fuel. The turbulent combustion model must clearly separate the effects of reaction-rate chemistry from the effects of unresolved turbulent fluctuations in composition and temperature. Several combustion models have been used to study the in-cylinder combustion processes in compression-ignition engines. Many of these models either neglect the influence of unresolved turbulent fluctuations on mean chemical reaction rates, or treat those effects in a highly simplified way. For instance, a locally well-stirred reactor (WSR) model uses the cell-mean values of composition and temperature directly in the chemical mechanism, thereby ignoring the effects of turbulence-chemistry interactions (TCI). This paper emphasizes the importance of these TCI in predicting in-cylinder combustion processes by using a transported probability density function (PDF) model. The transported PDF model has the distinct benefit of capturing the complex interactions among hydrodynamic turbulence, gas-phase chemistry, soot, radiation and fuel-sprays in a natural and direct manner [2]. 2. Physical Models and Numerical Methods The mean momentum, pressure and turbulence model equations are solved using a segregated, pressure-based, time-implicit finite-volume method. The discretizations are second-order in space and first-order in time. A modeled transport equation for the joint PDF of species mass fractions and mixture specific enthalpy is solved using a hybrid Lagrangian particle/eulerian mesh (LPEM) method. The turbulence is modeled using a standard two-equation model. The turbulent transport terms in the PDF equation are modeled using the gradient transport model, and the scalar mixing is modeled using interaction-by-exchange-with-the-mean (IEM). To model gas-phase chemistry of diesel fuel used in experiments, skeletal n-heptane chemical kinetic mechanisms (29- and 40-species) are used. The liquid fuel-spray is modeled using a stochastic Lagrangian parcel method, where the liquid mass is represented by a finite number of statistical parcels. Each parcel represents a group of droplets having the same properties. These models are implemented in a commercial CFD code, STAR-CD [3]. A 1/6 th (60 degrees) wedge sector mesh represents a bowl-in-piston compression-ignition engine. Intake and exhaust ports are not modeled. Simulations begin at 60 btdc and continue until 120 atdc. Two different engine operating conditions one at full-load and one at part-load are simulated to examine the response of the models at different load conditions for a heavy-duty engine. The fuel injector is mounted near the top of the chamber on the centerline, with the spray directed along the bisecting plane of the sector mesh and angled down into the bowl. 2

3 3. Results and Discussion Comparisons are made between the two models, one which accounts for TCI (PDF model) and one which neglects TCI (WSR model), and also for each model with experimental data. Initially, a 29-species skeletal n-heptane mechanism [4] was used to model the gas-phase chemistry. Figure 1 shows the computed pressure traces from both the models and their comparison with measured data. Figure 1. Computed (using 29-species mechanism) and measured pressure traces versus crankangle degrees for the two operating conditions. Left: Part-load; Right: Full-load. Two computed profiles are shown: one for PDF model and one for WSR model. Figure 2. Computed pressure traces versus crankangle degrees for the two gas-phase chemical mechanisms. Both computed profiles use the PDF model. Two observations can be made from Fig.1. First, the computed curve from the PDF model is in closer agreement to the experimental data compared to the WSR model for both operating 3

4 conditions. Second, both models deviate from the measured data at the start of ignition, and also near the peak cylinder pressure, especially for the part-load case. The disagreement in the ignition delay is likely to be due to the gas-phase chemistry, and a more detailed 40-species skeletal n-heptane mechanism [5] was tried to explore this. Figure 2 shows the comparison of the computed pressure traces using the two gas-phase chemical mechanisms. It can be observed that the 40-species mechanism better captures the ignition timing compared to the 29-species mechanism. Moreover, the computed peak cylinder pressure has come down slightly with the 40- species mechanism. Differences between the WSR and PDF models are still evident using the 40-species n-heptane mechanism, especially in the peak pressure, for both operating conditions, as shown in Fig. 3. For the PDF model, a notional particle representation is used where the evolution of these particles represents the behavior of the real fluid system. Therefore, there is a distribution of properties about the cell-level mean value for the PDF model, and this tends to broaden the heat release in time and to lower the computed peak pressure compared to the WSR model. Remaining differences between model and experiment may be due to the simplified representation of the geometry and the difference in fuels between experiments (diesel) and simulations (n-heptane). Figure 3. Computed (using 40-species mechanism) and measured pressure traces versus crankangle degrees for the two operating conditions. Left: Part-load; Right: Full-load. Two computed profiles are shown: one for PDF model and one for WSR model. The importance of TCI is further evident on comparing the computed mean temperature and mean OH contours at 5 atdc for the part-load operating condition between the two models, as shown in Figs. 4 and 5 respectively. It can be observed that the peak temperature is lower for the PDF model compared to the WSR model. Similarly, the peak OH level is lower and the profile is more diffuse with consideration of the unresolved turbulent fluctuations in composition and temperature, i.e. for the PDF model. This is consistent with the observations made in an earlier work for diesel-engine-like conditions [6]. 4

5 Figure 4. Computed mean temperature contours at 5 atdc for part-load case for the two models. Left half: WSR model. Right half: PDF model. Figure 5. Computed mean OH mass fraction contours at 5 atdc for part-load case for the two models. Left half: WSR model. Right half: PDF model. 4. Concluding Remarks Simulations have been performed for two different load conditions in a heavy-duty engine using two combustion models to study the importance of turbulence-chemistry interactions in predicting the in-cylinder combustion processes accurately. The PDF model, which accounts for turbulence-chemistry interactions, predicts pressure traces that are in closer agreement to the measured data compared to the WSR model. Marked differences are also observed in the computed turbulent flame structure between the two models. These differences indicate the extent to which unresolved turbulent fluctuations in composition and temperature influence the mean chemical reaction rates in a compression-ignition heavy-duty engine. Furthermore, comparisons have been made for the part-load operating condition between two gas-phase chemical mechanisms. The computed pressure trace using the 40-species n-heptane mechanism is in better agreement with the measured data than the 29-species n-heptane mechanism. This highlights the importance of choosing an appropriate gas-phase chemical mechanism to predict the combustion physics accurately. 5

6 Acknowledgements This research has been supported by the U.S. Department of Energy under award no. DE- EE , and by the Volvo Group. The authors are grateful to CD-adapco for making available their STAR-CD CFD code for this research. References [1] Reitz R.D., Directions in internal combustion engine research, Combust. Flame 160 (2013) 1 8. [2] Haworth D.C., Progress in probability density function methods for turbulent reacting flows, Prog. Energy Combust. Sci. 36 (2010) [3] CD-adapco, See (2012). [4] Patel A., Kong S.C., Reitz R.D., Development and validation of a reduced reaction mechanism for HCCI engine simulations, SAE Technical Paper No (2004). [5] Golovitchev V.I., Chalmers University of Technology, Gothenburg, Sweden, 2000; [6] Bhattacharjee S., PDF modeling of high-pressure turbulent spray combustion for dieselengine-like conditions, Ph.D. thesis, The Pennsylvania State University, University Park, PA (2012). 6