Large Eddy Simulation of GDI Single-hole and Multi-hole Injector Sprays with Comparison of Numerical Break-up Models and Coefficients

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1 Journal of Applied Fluid Mechanics, Vol. 9, No. 2, pp , 216. Available online at ISSN , EISSN Large Eddy Simulation of GDI Single-hole and Multi-hole Injector Sprays with Comparison of Numerical Break-up Models and Coefficients H. Zamani 1, V. Hosseini 1, H. Afshin 1, L. Allocca 2 and M. Baloo 3 1 Mechanical Engineering Department, Center of Excellence in Energy Conversion, Sharif University of Technology, Tehran, Iran 2 IstitutoMotori CNR, Napoli, Italy 3 Iran-Khodro Powertrain Company IPCO, Tehran, Iran Corresponding Author vhosseini@sharif.edu (Received April 1, 214; accepted June 16, 215) ABSTRACT In the present study the fuel spray of a gasoline direct injected engine with multi-hole injector is simulated. Simulation inputs data, injection flow rate and spray cone angle are obtained from previous experimental studies. Log-normal distribution with different standard deviation is used for initial droplet size as the primary break-up model in order to reach the agreement between experimental and calculated spray tip penetration. As the first step, only one plume of spray injected into a quiescent air environment is simulated and validated by varying break-up model and standard deviation. Then, with coefficient obtained from the single jet simulation all six spray jets are simulated based on the injector nozzles geometry. The comparison between single-jet simulation and multi-jet simulation shows that validated model coefficients for the single-jet spray cannot be used for multi-jet spray simulation without significant modifications due to adjacent jet interaction and pressure drag. A set of new coefficients for the multi-jet spray is presented Keywords: Gasoline direct injection (GDI) engine; Single-hole and multi-hole spray; Spray tip penetration; Largeeddy simulation. NOMENCLATURE theoretical initial droplet size break-up length scale constant for calculating break-up time scale gasoline surface tension surrounding gas density aerodynamic time scale in wave model break-up rate relative velocity between fuel jet and surrounding sub-grid scale (SGS) stress gas tensor dimensionless wavelength of the most unstable waves of liquid-gas interface turbulent eddy viscosity gas phase Weber number rate-of-strain tensor standard deviation grid size droplet diameter constant in smagorinsky model turbulent length scale fuel injection pressure turbulent time scale fuel injected mass time chamber pressure Huh-Gosmanbreak-up model constants RMS root mean square 1. INTRODUCTION Improving mixture formation is one of the solutions to the low efficiency operation of Spark Ignited (SI) engine under part load conditions. Lean flammability limits of the air fuel mixture reduce the possibility of lean operation of the engine. Hence, throttling is used to reduce the power;

2 H. Zamani et al. /JAFM, Vol. 9, No. 2, pp , 216. consequently, a reduction of volumetric efficiency and fuel conversion efficiency are inevitable. Direct injection of fuel in SI engines enhances the combustion and engine operation through various mechanisms. Fuel evaporations cool down the mixture before ignition and allows for the higher compression ratios which leads to higher thermal efficiency (Schwarz, et al. 26; Baecker, et al. 27; Kim, et al. 28; Postrioti, et al. 212). In the case of stratified mode operation, when the mixture is not homogenous inside the combustion chamber, pumping loss is eliminated by reducing throttling (Badami, et al. 24). Avoiding the fuel loss during the valves overlap and deposition of fuel over the walls are some of the other advantages of fuel direct fuel injection. Fuel wall film results in local incomplete combustion and increase of unburned hydrocarbons emissions (Costa, et al. 29). Gasoline Direct Injection (GDI) enabled engine operations in various modes. At part load, the fuel is injected around the spark and a stratified air fuel mixture is formed. The mixture around the kernel at the spark is still stoichiometric, while the overall air fuel mixture is fuel lean. At high load and torque demand, the injection happens early during compression cycle and hence a homogenous stoichiometric mixture is formed (Costa, et al. 212). There are various types of injection strategies and fuel injectors to achieve either homogenous or stratified charges. Hollow cone, single-hole full cone and multi-hole injectors are used for spray guided, air guided or wall guided strategies (Costa, et al. 29). In spray guided strategy, injector is located close to the spark plug and fuel spray is injected directly toward the spark plug (Costa, et al. 211). In the air guided strategy, fuel is injected in the moving air toward the spark plug. In the wall guided strategy, fuel spray is injected directly toward the piston head. The piston head with specific geometry deflects the fuel spray toward the spark plug (Costa, et al. 211). Experimental and numerical researches have been conducted to understand spray temporal and spatial evolution, mixture formation and combustion process. Pischke et al. (212) proposed a new approach to predict the collision probabilities while Ra and Reitz (29) described an evaporation model for multi-component fuel sprays. Cordes et al. (212) investigated the characteristics of an outwardly opening GDI spray in different ambient conditions with CCD camera. They observed that ambient pressure and needle lift has no significant influence on results, injection pressure and ambient temperature have the greatest effect on hollow cone spray string distribution: however, these effects are very small. Wall impingement of fuel spray is one of the GDI engine concerns that may result in HC and soot formation. Montanaro et al. (211, 212) studied the wall impingement of a multi-hole spray with imaging apparatus and CFD simulation. Sementa et al. (212) used 4-cylinder commercial GDI engine with optical access to assess the effect of different fuels, injection strategies and cyclic variations on combustion process and pollutants emission. Their study shows that, bio-ethanol spray is less sensible to in-cylinder pressure and air motion than gasoline spray is. Also, in case of stratified mode, flame front is faster than in homogeneous mode due to distribution of air to fuel ratio, results in more stable combustion and maximum pressure. Costa et al. 212) coupled a 3D numerical simulation with an optimization tool to validate the gasoline spray model. They also used this CFD-O tool to investigate the effects of single and double injection in part load and full load. It was shown that, optimized double injection has better energy efficiency and stratified charge quality under lean operation (Costa, et al. 212).Martin et al. (Martin, et al. 21) studied the interaction of two injected spray in double injection. The comparison of the results shows that, in-cylinder pressure and temperature as well as dwell time are the main influencing parameters. Santolaya et al. (Santolaya, et al. 213) investigated the effect of droplets collisions on the structure of a pressure swirl spray. They found that an increase in injection pressure results in higher droplet collision rate, higher inertia collisions and increase of axial droplet mean diameter. Daniel et al. (212) compared the sensitivity of different fuels to ignition timing. They showed that DMF had good performance in cold start. Zigan et al. (211) investigated the spray evaporation of different fuel using PDA, LIF and 2-D Mie scattering imaging method and introduced a three-component fuel to model the gasoline characteristics. Nishad et al. (211; 212) used the well-known large eddy simulation for more precisely simulation of hollow cone spray. Lampa and Fritsching (213) used the Large Eddy Simulation to investigate the influence of spray parameters on the large-scale turbulence of ambient gas and droplets. They showed that, within the spray, the driving force for the formation of droplet clusters is the interaction between droplets and ambient gas within the shear layer. Jones et al. (21) studied the effect of velocity fluctuations on droplet vaporization and on the structure of kerosene and acetone spray by means of Large Eddy Simulation. They found out that, in downstream area, mixing region and radial spreading rate of jet increased due to the vortices formed in the shear layer. Ronald and Grover (211) traced the each hole jet of multi-hole injector and redesigned the hole patterns to reduce the wall film while maintaining good mixture formation and combustion stability. Montanaro et al. (211) simulated and validated just one spray plume of a multi-hole injector to obtain the submodels constants for full spray simulation. This method significantly decreases the computational cost. Although initial condition and sub models constants of all holes are the same but adjacent spray jets have different evolution due to spray interaction, fluid dynamic effects and the like. In the present study, a numerical model is developed for 1-hole and 6-holes injector nozzles and spray behavior is investigated through using large eddy simulation (LES) technique. Although 114

3 H. Zamani et al. /JAFM, Vol. 9, No. 2, pp , 216. Reynolds averaged turbulence models such as k-e, k-zeta-f and etc. have been shown to result in acceptable macroscopic agreement between numerical and experimental data, in order to ensure reliable simulation of microscopic phenomena i.e. fuel vaporization, species transport and mixture formation whichh are vital for further combustion simulation, it is preferred to utilize more realistic time dependent Large Eddy Simulation turbulence model in present study. The objective of the current study was to understand and to validate the possibility of using 1-hole injector model coefficients for the 6-hole injector model. 2. EXPERIMENTAL SETUP Cone angle (deg) Pinj=2 MPa, mf=5 mg/str - mean= Fig. 2. Spray cone angle of a jet emerged from just one nozzle (Costa, Iorio et al. 29). The experimental results of a recent work by Costa et al. (29) of Istituto Motori were used for the current numerical simulation study. Mass flow rate of fuel and cone angle are input data for simulation of the spray. In the present study, the injector is the 6-hole Bosch HDEV 5.1,.193 mm in diameter. The mass flow rate and spray cone angle for this injector are shown in Fig. 1 and Fig. 2, respectively. The injection was carried out in an optically accessible quiescent vessel filled with nitrogen at temperature of 298 K and pressure of 1 kpa. In addition, spray images were captured by a CCD camera,.5 µs shutter, pixels and 12 bit resolution, synchronized with the injection event at different instant from the Start Of Injection (SOI). The experimental set up was set to measure a single jet penetration and cone angle without parallax indetermination. The footprint of the 6-hole injector at 3 mm away the nozzle tip and perpendicularly located with respect to its axis, is shown in Fig Pinj=2 Mpa, mf= 5mg/str Fig. 1. Injected Fuel mass rate from all 6 nozzles during one stroke(costa, Iorio et al. 29) ). Injected mass rate (kg/s) Fig. 3. Spray footprint on a plane 3 mm from the holes (Costa, Iorio et al. 29). Cyclicc variations on experimental data i.e. tip penetration of jet #4 and corresponding cone angle were taken into account by repeating it five times. 95% confidence intervals were calculated based on Normal probability distribution assumption for test replication results population. As each test is repeated 5 times, student-t distribution was used for confidence interval calculations (Table 1). 3. NUMERICAL SIMULATION In this paper well known Eulerian-Lagrangian numerical method is used to simulate development of disperse spray particle into continuous gas phase. Using Navier-Stokes coupled with Large Eddy Simulation turbulence model, gas phase is described. Velocity components, temperature, pressure and density for all cells in each time step are defined accordingly. In each time step, the boundary conditions for a droplet are obtained by 115

4 F. Kaya/JAFM, Vol. 9, No. 2, pp. x-x, 216. time [µs] penetration [mm] Table 1 Uncertaiaty analysis of experimental data Qinj 5mg/str Pinj 2 MPa - Pch,1 MPa σ Confidence interval (95%) cone angle [deg] Σ Confidence interval (95%) ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± calculating the effect of gas phase on that droplet crossing a specific cell. Break-up is the most complex phenomenon that a droplet is exposed to as a consequence of competition between drag force that tend to split up a single drop into several smaller drops on one hand and surface tension which tries to keep the droplet integrated on the other hand. Several sub-models were developed in order to describe exchange properties (mass, energy, momentum) between continuous and disperse phase which some of them were used in present study(baumgarten 26). 3.1 Droplet Break-up Models Since in GDI spray models the nozzle is modeled virtually, initial droplet size is another unknown inputdatum for spray simulation. A semi empirical correlation is used to calculate the initial droplet size. = 2 (1) where τ, ρ, u and λ are the gasoline surface tension, the surrounding gas density, relative velocity between fuel jet and surrounding gas and the dimensionless wavelength of the most unstable waves of liquid-gas interface, respectively. Also, C =1 is a constant (Costa, Sorge et al. 211). In Fig. 4, a log-normal distribution ofd h is generated as initial droplet size probability density in order to model the primary break-up. For this injection rate and nozzle diameter gas phase Weber number (We =ρ u d τ, d is droplet diameter) at nozzle exit is lower than 1. In this condition, Kelvin-Helmoltz (WAVE) model is suggested for secondary break-up modeling (Liu and Reitz 1993). Huh-Gosman model (Huh and Gosman 1991; Huh, Lee et al. 1998) is also said to be suitable for GDI multi-hole break-up model. In Huh-Gosman break-up model, nozzle flow turbulence is responsible for break-up mechanism. The turbulent length and time scalesl,τ are defined as Eq. 2 and 3:. ( ) = 1 + (2) ( ) = + (3) Probability density E+ 1E-5 2E-5 3E-5 4E-5 5E-5 Diameter (m) Fig. 4.Log-normal droplet size distribution. C is optimized for GDI multi hole injection conditions and always kept C =.92. C andc are initial values for time and length scales. Break-up length L A and time τ A scales are defined by turbulent length and time scales, Eq. 4 and 5. = (4) = + (5) is aerodynamic time scale in WAVE model. The ratio of break-up length and time scales determines the break-up rate as shown in Eq. 6 = 2 (6) 3.2 Droplet Collision Model D th = 1.31 E-5 m σ =.5 σ =.6 Droplet size is determined by the competition between break-up and collision models specially in non-vaporizing sprays (Montanaro, Allocca et al. 211). In dense spray region, the differences in 116

5 H. Zamani et al. /JAFM, Vol. 9, No. 2, pp , 216. droplet velocities caused by differences in injection velocities (e.g. multiple injections), in trajectories, turbulence, wall impingement, break-up and etc. lead to droplet collision. The outcome of collision can be break-up, pure reflection or coalescence depend on the energy of impact, drop sizes ratio and ambient conditions (Baumgarten 26). In this research, Nordin model (Nordin 21) is used to account for changing in droplet s mass, momentum and energy after collision. 3.3 LES Turbulence Model Large Eddy Simulation with simple Smagorinsky model was used to consider the turbulence effects in this study. In this model by standard WAVE break-up model overestimates the experimental results. Substituting the WAVE break-up model by standard Huh-Gosman break-up model and increasing the C1value from 2 to 4, give a good agreement between simulation and experimental results, Fig = (7) Where is sub-grid scale (SGS) stress tensor. is turbulent eddy viscosity and is the rate-of-strain tensor. = (8) In Smagorinsky model is defined as follows. = 2 = (9) Where is the grid size and C =.1 is a constant. Turbulent flow contains a lot of eddies with different scale as secondary flows. These eddies lead to larger motion between flow layers compared to laminar flow. Thus in turbulent flow, transfer parameters such as mass transfer and heat transfer have bigger intensity(sheikholeslami, Jafaryar et al. 215). On the other hand, small droplets moving through the surrounding air create a nanofluid which affects resulting mixture properties (Mosayebidorcheh, Sheikholeslami et al. 214). 4. RESULT AND DISCUSSION Existing experimental data are related to penetration of one spray plume (of #4 nozzle) of 6-hole injector (Costa, et al. 29). Thus, fuel spray emerged from just one hole of the injector is simulated as proposed in (Montanaro, Allocca et al. 211). A mm box initially filled with nitrogen at pressure of.1 MPa and 3 K temperature is considered as computational domain with total number of 48 cells to simulate just one spray plume. For simultaneous simulation of all 6 jets according to Fig. 3, the computational domain is a cylinder with diameter of 8 mm and height of 9 mm. In this case, the total number of 252 cells is created. In addition to break-up and collision models, turbulent dispersion and Dukowicz droplet evaporation model (Dukowicz 198) are applied. Initial droplet size distribution with σ =.5 and σ =.6 (Costa et al. 29) and Large Eddy Simulation solver using classical Smagorinsky turbulent model (Smagorinsky 1963) are also used. Fig. 5 shows that the spray tip penetration simulated Fig. 5.Single spray plume penetration simulated by standard WAVE model and =.. Penetration (m) EXP. (Costa et al, 29) Present study, C1 = 2 Present study, C1 = Fig. 6.Single spray plume penetration simulated by Huh_Gosman model and droplet distribution with =.. Fig. 7 shows the tip penetration for all 6 spray plumes emerging simultaneously based on the structure of Fig. 3 that represents the actual injection condition. All penetrations are lower than experimental data. Even jet #4 with least plume interference has slightly lower penetration than single jet simulation. As indicated in Fig. 7, spray plumes behave similarly till approximately 26 μs after start of injection. During this period, wide angle between plumes direction cause them to develop independently just like single plume injection so that no interaction is observed between plumes as shown in Fig. 8. After this time, the plumes (especially # 1, 3 and 5) begin to interact gradually and form a complex structure of droplets. This interaction is one probable reason for reduction of plumes penetration. Simulating just one spray plume has low computational cost; however, this approach does not consider the plumes interaction effects such as 117

6 F. Kaya/JAFM, Vol. 9, No. 2, pp. x-x, 216. Penetration (m) EXP. (Costa et al, 29) Present study #2, #4 & #6 Present study #3 & #5 Present study # Fig. 7. Spray penetration of actual injection condition (6 nozzle) by Huh-Gosman C1=4 & =.. Time after SOI (μs) Top view Side view Fig. 8. Spatial and temporal evolution of spray till 26 after SOI. adjacent plumes droplets collision, pressure difference between inner and outer side of plumes and air entrainment (Nishida, et al. 29). On the other hand, simulation of all plumes significantly increases the computational cost especially due to calculation of lots of droplet collisions. Generally, initial jet velocity, droplets size and pressure difference affect the penetration. Since initial jet velocity is the same for all 6 jets due to similar injection rate; therefore, initial jet velocity has no effect on differences in jets penetration. But, collision may change the droplet size distribution in simultaneous 6-jet simulation. Collision may lead to coalescence and make larger droplets that increase the penetration or may lead to different type of break-up and make smaller droplets that decrease the penetration. 6-jet spray has larger frontal area than single jet spray has. Negative pressure can decreases the penetration especially in the case of multi-jet injection because of larger frontal area; however, air entrainment can reduce the negative pressure (i.e. increase the penetration). Fig. 9 illustrates the comparison for droplet size in single jet and multi-jet simulation 1 μsafter SOI. As shown, droplets size of 6-jet injection are larger than single jet one tend to increase the penetration in turn. But, negative pressure depicted in Fig. 1 suppresses this tendency and decreases the penetration. As shown in Fig. 11, even standard WAVE breakup model presents an acceptable penetration in the 118

7 F. Kaya/JAFM, Vol. 9, No. 2, pp. x-x, 216. (a) (b) Fig. 9. Comparison for droplet size [m] 1 after SOI in (a) single jet and (b) 6-jet simulation. (a) (b) Fig. 1. Comparison for relative pressure (Pa), 1 after SOI in (a) single jet and (b) 6-jet simulation. case of simultaneous 6 nozzle simulation compared to single nozzle simulation (Fig. 5). More negative pressure in simultaneous 6-jet simulation (Fig. 1 (b)) compared to single jet simulation (Fig. 1 (a)), exerts more pressure drag on all 6 jets. Hence, the penetration of all jets decreases compared to penetration of single jet simulation. For improvement of the slight decrease in the plume #4 penetration, C1 is increased to 6 so the break-up rate and the size of child droplets are increased. The increase in child droplets size, increases the penetration. Also standard deviation of initial droplet size distribution is increased from σ =.5 to σ =.6.Fig. 12 illustrates the improved jet #4 penetration in simultaneous simulation by using C1=6 and σ =

8 F. Kaya/JAFM, Vol. 9, No. 2, pp. x-x, 216. Penetration (m) EXP. (Costa et al, 29) Present study #1, #3 & #5 Present study #2, #4 & #6 Penetration (m) EXP. (Costa et al, 29) Present study #4, C1=6 & σ=.6 Present study #4, C1=4 & σ= Fig. 11.Simulation of the 6 hole penetration by standard WAVE model and = Fig. 12.Comparison for nozzle #4 penetration using Huh-Gosman model and different coefficients. 1 µs 2 µs 5 µs 7 µs (a) (b) (c) (d) (e) (f) (g) (h) Fig. 13.Comparison between experimental (Costa, Iorio et al. 29)and numerical spray structure for =, =. Fig. 13 shows the spatial and temporal evolution of simulated spray compared to experimental images from side view (a-d) and front view (e-h). Although underestimation in spray penetration could be seen in early time after start of injection, good agreement was achieved after this period of time. Negative pressure described in Fig. 1 caused the ambient air to enter into the spray structure. The air entrainment diluted the spray cloud as it can be seen in Fig. 1 (c) and (d). This phenomenon enhances the air-fuel mixing. Uniform length velocity vectors illustrated in Fig. 14 shows the air entrainment more clearly. 3. CONCLUSION The objective of the present study was to understand the physics and spray penetration tips of two cases of single-hole and multi-hole injector sprays under the conditions similar to those of Gasoline Direct Injection (GDI). While the computational cost for simulation of multi-hole injector spray is much more than that of a singlehole injector spray, it is shown here that due to the competition between droplet collisions and relative pressure differences between the two cases, it is not 12

9 H. Zamani et al. /JAFM, Vol. 9, No. 2, pp , 216. possible to use the numerical results of a single-hole injector spray for multi-hole injector spray. The interactions between sprays in the multi-hole injector spray results in coalescence between droplets which leads to larger droplet size. Fig. 14.air entrainment shown by uniform length velocity vectors. However, multi-hole injector sprays are exposed to larger negative pressure drops. The latter two conditions interactions make the problem so complex that the results of single-hole injector spray cannot be used directly. ACKNOWLEDGEMENTS The support of Iran Khodro Engine Design Center (IPCO) on providing data and guidance during the present study is acknowledged. REFERENCES AVL Fire v28 Users Guide - ICE Physics and Chemistry. Badami, M., V. Bevilacqua, F. Millo, M. Chiodi, and M. Bargende (24). GDI Swirl Injector Spray Simulation: A Combined Phenomenological-CFD Approach. SAE Baecker, H., A. Kaufmann and M. Tichy (27).Experimental and Simulative Investigation of Spray-Guided GDI Combustion Systems.SAE Baumgarten, C. (26). Mixture Formation in Internal Combustion Engines: Springer. Cordes, D., P. Pischke and R. Kneer (212).Influence of Injection and Ambient Conditions on the Nozzle Exit Spray of an Outwardly Opening GDI Injector.SAE Costa, M., B. Iorio, U. Sorge and S. Alfuso. (29). Assessment of a Numerical Model for Multi- Hole Gasoline Sprays to be Employed in the Simulation of Spark Ignition GDI Engines with a Jet-Guided Combustion Mode. SAE Costa, M., U. Sorge and L. Allocca (211).Numerical study of the mixture formation process in a four-stroke GDI engine for two-wheel applications.simulation Modelling Practice and Theory 19, Costa, M., U. Sorge and L. Allocca (212). CFD optimization for GDI spray model tuning and enhancement of engine performance. Advances in Engineering Software 49, Costa, M., U. Sorge and L. Allocca (212).Increasing energy efficiency of a gasoline direct injection engine through optimal synchronization of single or double injection strategies.energy Conversion and Management 6, Daniel, R., G. Tian, H. Xu and S. Shuai (212). Ignition timing sensitivities of oxygenated biofuels compared to gasoline in a directinjection SI engine. Fuel 99, Dukowicz, J. K. (198). A Particle-Fluid Numerical Model for Liquid Sprays.J. Comp. Physics 35, Huh, K. Y. and A. D. Gosman (1991). A Phenomenological Model of Diesel Spray Atomisation.Proceedings of the International Conference on Multiphase Flows,Tsukuba, Japan. Huh, K. Y., E. Lee and J. Y. Koo (1998). Diesel Spray Atomization Model Considering Nozzle Exit Turbulence Conditions.Atomization and Sprays 8, Jones, W. P., S. Lyra and A. J. Marquis (21). Large Eddy Simulation of evaporating kerosene and acetone sprays. International Journal of Heat and Mass Transfer 53, Kim, S.-J., Y. N. and J. H. Lee (28). Analysis of the In-Cylinder Flow, Mixture Formation and Combustion Processes in a Spray Guided GDI Engine. SAE Lampa, A. and U. Fritsching (213). Large Eddy Simulation of the spray formation in confinements. International Journal of Heat and Fluid Flow 43, Liu, A. B. andr. D. Reitz (1993). Modeling the Effects of Drop Drag and Break-up on Fuel Sprays. SAE x Martin, D., J. Stratmann, P. Pischke, R. Kneer and M. C. Lai (21).Experimental Investigation of the Interaction of MultipleGDI Injections using Laser Diagnostics.SAE Montanaro, A., L. Allocca, D. Ettorre, T. Lucchini, F. Brusiani and G. Cazzoli (211).Experimental Characterization of High- 121

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