ANALYSES OF FLOW DUE TO RECIPROCATING PISTON - SPEED AND GEOMETRY EFFECTS

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1 11th International Conference on Fluid Control, Measurements and Visualization FLUCOME 2011 Paper No. 222 December 5-9, 2011, National Taiwan Ocean University, Keelung, Taiwan ANALYSES OF FLOW DUE TO RECIPROCATING PISTON - SPEED AND GEOMETRY EFFECTS Avishek Ranjan 1, S R Chakravarthy 2 and Pramod S Mehta 1* 1 Department of Mechanical Engineering, 2 Department of Aerospace Engineering, Indian Institute of Technology Madras, India * Corresponding Author s psmehta@iitm.ac.in ABSTRACT The engine combustion, performance and emission characteristics are known to be influenced by the incylinder mixing induced due to several factor. In this work, the role of reciprocating piston on the incylinder flow in motoring conditions is investigated on an optical test rig designed for understanding the flow development during charge compression. The flow measurements are carried out using Particle Image Velocimetry (PIV) at varying engine speeds for different piston configurations. The results at those conditions are analyzed through Computational Fluid Dynamics (CFD) and Proper Orthogonal Decompositon (POD) tools. Typical results comparing PIV measurements with CFD and the flow turbulence using POD are discussed. The agreement between the PIV measurement and the CFD simulation in a base line case of a flat piston configuration and 600 rpm speed condition is qualitatively reasonable. For this case, POD analysis on measured instantaneous velocity points to the transfer of energy from mean flow to turbulence particularly during the later part of compression. The CFD estimates concerning normalized turbulent kinetic energy obtained during parametric analysis for the three piston geometries viz. flat, cylindrical and spherical at crank speeds between rpm, reveal a linear scaling of these quantities with mean piston kinetic energy. The turbulent kinetic energy is higher in case of spherical cavity-in-piston than cylindrical and flat pistons. INTRODUCTION There is an ever-increasing impetus on the improvement of overall efficiency and reduction of emissions from an internal combustion engine. The engine incylinder air motion affects combustion and hence the engine performance and exhaust emissions, by influencing processes such as fuel-air mixing, flame propagation, combustion and heat transfer [1]. This flow is not only turbulent but also involves large cycle-to-cycle variations of the mean flow, and remains less understood. Among the various mean flows arising in an engine, tumble is an organized fluid rotation around the axis perpendicular to the vertical plane passing through the centre of inlet and exhaust valves. The complete characterization of incylinder flow field requires instantaneous planar velocity measurement which is possible using Particle Image Velocimetry (PIV) [2]. In their investigation of cyclic variations in tumble flow using PIV, Li et al. [3] reported that one third of the instantaneous velocity fields do not show any tumble motion. Also, even in the cycles with tumble, the location and intensity of tumble varies. They report minimal influence of engine speed on the ensemble averaged flow field. Muller et al. [4] investigated the temporal evolution of the flow field using vortex center location for characterizing tumble motion. They found that the location of vortex centre not only changes with engine speed but also with crank angle. In another investigation, Voisine et al. [5] observed the presence of a tumbling jet during the intake and an interaction of the jet with the cylinder geometry and the moving piston during compression. Their results show a vortex breakdown during compression indicated by the decay of the mean flow kinetic energy. This is also proven using proper orthogonal decomposition (POD), which is a powerful tool to study cycle-to-cycle variations in engine flows [6,7]. Thus, it is known that the complex flow field that results at the TDC includes the combined effects of intake and compression. Therefore, in this work, it is intended to isolate and investigate the effect of pure compression on the incylinder air motion, along with the effect of engine speed and combustion chamber geometry. The work elements include abinitio development of an optical test-rig, flow diagnostics using PIV, analyses using POD and numerical simulation using KIVA-3V. EXPERIMENTAL SET UP Test-rig design The optical engine test-rig is built around a crankshaftconnecting rod-piston assembly from a Hero Honda Splendor four-stroke motor-bike engine. Figure 1 shows a schematic of the engine test-rig with designed components shown in box. The engine design incorporates a maximum possible optical access using a fully transparent quartz cylinder liner and a transparent Perspex cylinder head. The thickness for the 1

2 cylinder liner is 8 mm calculated using circumferential stress design calculation. The cylinder head includes inclined holes that act as intake/exhaust ports, and side holes for gas exchange. The intake arrangement is provided using a solenoid actuated pneumatic valve which controls the movement of the plunger in order to open/ close the intake port. The exhaust port is kept closed during measurements. Three Graphite-filled Teflon piston rings are used with a rubber O-ring on the inside circumference of the piston ring. The transparent engine portion is fastened to the support block using four. Adequate sealing between the cylinder head and the liner is ensured using an O- ring. After the assembly is complete, a hydrostatic test is performed on the test-rig to i) ascertain the ability of the test-rig to withstand pressure ii) check for leakages. Table 1 shows the final specifications for the designed optical engine test-rig. Table 1: Specifications of the optical engine test-rig. Engine Single cylinder, 4-stroke optical engine motored using a 2 HP, VFD controlled induction motor Bore / mm Speed upto 1420 rpm Stroke 49.5 mm Connecting Rod length 94.0 mm Compression Ratio 8.0 Piston Ring: Material ID x width x thickness Graphite-filled Teflon 47.0 x 1.5 x 2.0 mm Instrumentation and measurement procedure Figure 2 shows a schematic of the fully instrumented testrig. The instruments and apparatus used in the test-rig can be described as i) light source and sheet optics ii) camera and imaging optics iii) seeding unit iv) timing units. The source of illumination is a Nd:YAG double pulse laser (Quantel, Twins Ultra), frequency doubled to emit 532 nm i.e. green wavelength light at a 30 mj/pulse. A combination of three lenses concave, convex and cylindrical is used to create a light of thickness ~ 1.5 mm. A charge coupled device (CCD) double shutter camera (Pixelfly, PCO Imaging) with resolution 1360 x 1024 pixels is used for imaging. A Nikon lens of aperture 52 mm with f ratio kept as 5.6 is used with the CCD camera. A camera hardware card and Camware software are used to visualize the images on a computer. Compressed nitrogen, at a pressure between 3-4 bar, is mixed with an aerosol of fine olive oil droplets of size ~ 1 µm generated by a Laskin nozzle-based oil seeding generator [8]. An optical type encoder is used to generate a 5V TTL pulse signal at every top dead centre (TDC). One out of the two signals obtained in every engine cycle is skipped using a divider circuit with a J-K flip flop. This signal (of width 50 ms) goes as an external trigger for the laser and is also used to actuate the solenoid-controlled pneumatic valve. A sequencer unit (PIVTec, Germany) comprising of a hardware card and software (seco2004), is used to synchronize the laser flash lamp trigger with the camera shutter exposure. A phase lock delay is added to the signal depending on the engine speed (rpm) and the crank angle location at which the laser is required to fire. The camera magnification, M (pixel/mm) is 18, and the pulse separation is kept as 20 µs. Inlet pressure of 1.5 bar is maintained upstream of the engine. The engine is run so as to capture a maximum of 100 images in one shot. PIV measurements are conducted in the vertical plane passing through ports, at 400 and 600 rpm for three geometries flat piston, cylindrical cavity-in-piston and spherical cavity inpiston (shown in Figure 3), starting from BDC to 150 degrees after BDC (abdc) in steps of 30 degrees crank angle. Figure 1: Optical engine test-rig Post-processing and data analysis PivView software (PivTech) is used for statistical crosscorrelation of the captured images. The size of the interrogation window used in this work is 32 x 32 pixels with an overlap of 50%. The displacement is obtained with sub-pixel accuracy by fitting a 3-point-Gaussian function to the correlation peak, and finding the exact location of the peak. Multi-pass interrogation algorithm with three iterations is used for improving the signalto-noise ratio of the correlation peak [8]. The number of outliers is less than 5% of the total vectors, and they are replaced by interpolation. The uncertainty in instantaneous velocity is 0.2 m/s (0.1 pixel displacement). The correlation coefficient is greater than 0.4 in the region of interest. Figure 2: Experimental setup for PIV measurements. 2

3 Figure 3: Cylindrical and spherical cavity-in-piston Batch-processing of the captured image pairs is carried out to find the ensemble-averaged and the root-mean-square velocities. A convergence test carried out on mean velocity field shows that a minimum of 175 images must be captured for accurate results. Proper Orthogonal Decomposition (POD) is a technique which can be used to determine a set of spatial structures, which represent the most energetic structures present in a turbulent flow field [9]. The eigenmodes in POD are chosen by the flow itself making it physical and they have all properties of the original field such as incompressibility or boundary conditions. The convergence in capturing the energy of the flow with minimum number of modes is the fastest with POD making it more efficient than any other mathematical decomposition. CFD METHODOLOGY Numerical simulations are performed using multidimensional code KIVA-3V [10] wherein the mass, momentum (Reynolds-averaged) and energy conservation equations discretized by the arbitrary Lagrangian-Eulerian (ALE) scheme for structured meshes. Three dimensional full sector (360 deg) computational meshes are generated for flat piston, cylindrical and spherical cavity-in-piston geometries using the pre-processor code K3PREP. A grid independence study shows that 60,000 cells are sufficient for accurate computation of mean and turbulence quantities. According to Micklow and Gong [11], the tumble flow field can be accurately computed by using a full sector mesh without intake arrangement and the flow simulation can be started after the intake valve closure (IVC), provided appropriate initial conditions are supplied at the start of simulation. Initial velocity field is computed using the method described in ref [11] and has been incorporated in the KIVA-3V code. According to this method, the tumble velocities increase as a Bessel function of the distance from the geometric center. The RNG k-ε turbulence model is used for the simulations as it is reported [12] to give good agreement with experimental results. Table 2 shows the initial conditions used in the numerical simulation. Table 2: Initial conditions used for simulation Simulation period Intake BDC to 150 deg after TDC Engine speeds (rpm) 400, 600, 800, 1200 Initial pressure (atm) 1.0 Initial temperature (K) 298 Initial swirl ratio 0.0 Initial tumble ratio 1.0 # Initial turbulent kinetic energy (% 0.75 * of mean piston kinetic energy) *- estimated using suggestion by Heywood, 1998 [1] # - obtained from typical PIV measurements in this work RESULTS AND DISCUSSION 1. PIV vs. CFD The typical experimental results obtained from PIV at 600 rpm, flat piston case, are compared with those obtained from numerical simulation using KIVA-3V. Figure 4 shows a development of flow from 60 degrees crank angle after BDC (abdc) to 150 degrees crank angle abdc, where the contour indicates the in-plane velocity magnitude, V i.e. (u 2 +v 2 ) 1/2, u and v being mean velocities in x and y directions respectively. The plots obtained from PIV, Figure 4 (a), a tumble motion is observed before and at mid-stroke, viz. 60 and 90 degree crank angles and breaks down later into turbulence as can be inferred from POD analysis of the data. A decrease in mean velocity during compression is attributed to the energy dissipation and the transfer of mean energy to turbulence. In PIV measurements, the mean velocity magnitudes are observed to be somewhat higher than predicted from numerical simulation and is possibly due to the dissipation model used. A spherical cavity shows presence of radially inward squish motion induced by piston near TDC. This fact corroborates well both in PIV and CFD analysis as shown in Figure POD Analysis The proper orthogonal decomposition (POD) analysis is carried out on the fluctuating component of the measured instantaneous velocity for the flat piston case at 600 rpm. A careful look at the first four most dominant modes at various piston positions reveals interesting details about the behavior of the incylinder flow apart from the mean flow. In case of 60 degrees abdc, the first and second modes show an upward fluid motion towards intake and exhaust ports, respectively as evident in Figure 6. In the third mode, a counter-clockwise tumble vortex is observed. In case of 90 deg abdc case (Figure 7), the second mode shows a clockwise-tumble vortex, while the first and third modes show a leftward and upward fluid motions respectively. The fourth mode shows a pair of counterrotating vortices. In both 60 and 90 degree piston positions, the fourth mode has a pair of counter-rotating vortices. Thus, the POD analysis provides information regarding the structures other than the mean flow for understanding flow fluctuations. 3

4 Figure 4: Development of flow from deg abdc obtained from a) PIV b) CFD 4

5 1 st mode Figure 5: Mean velocity at 150 degrees abdc for spherical cavity-in-piston from a) PIV b) CFD The plots of non-dimensional energy content in first ten POD modes at different piston positions btained from the analysis are shown in Figure 8. From these results, it is observed that the energy in the tenth mode is 10 percent of the maximum energy contained in the first mode. A sharp fall in energy of the first mode in the early part of compression i.e. BDC and 30 deg abdc, indicates dominance of an organized rotation in the flow. The energy decay curve gets flatter as piston approaches TDC, indicating a greater fluctuating energy content of lower order modes. Thus, the POD analysis suggests an increase in turbulence. 2 nd mode 3. Effects of engine speed and piston geometry On validation of the numerical simulations with the measured PIV data, the effects of engine speed and piston geometry are investigated using CFD. Figures 9 (a) and (b) show the turbulent kinetic energy normalized with the mean piston kinetic energy for these parameters respectively. From the figure, it is observed that the turbulent kinetic energy peaks at around 30 degrees before TDC, the instant when the tumble flow is observed to break down. The turbulent kinetic energy is found to scale linearly with engine speed which corroborates with the observations reported in reference [13]. Among the piston geometries investigated, the effect of spherical cavity on turbulent kinetic energy near TDC is seen to be the most dominant, as observed in Figures 9 (b) and rd mode 4 th mode Figure 6: First four POD modes at 60 deg abdc 5

6 1 st mode Figure 8: Energy decay of the POD modes 2 nd mode (a) 3 rd mode 4 th mode (b) Figure 9: Effect of (a) engine speed and (b) engine geometry on turbulent kinetic energy Figure 7: First four POD modes at 90 deg abdc 6

7 Figure 10: Turbulent kinetic energy for three different geometries at 30 deg before TDC CONCLUSION The effect of reciprocating piston on the incylinder flow in motoring conditions is investigated on an optical test rig designed for understanding the flow development during charge compression. The PIV measurements and CFD simulation results, for a flat piston at 600 rpm engine speed condition are compared and found to be in reasonable qualitative agreement. The POD analysis provides useful information regarding the dominant flow structures and suggests a transfer of energy from mean flow to turbulence in later part of compression. The CFD estimates concerning normalized turbulent kinetic energy obtained during parametric analysis for the three piston geometries viz. flat, cylindrical and spherical at crank speeds between rpm, reveal a linear scaling of these quantities with mean piston kinetic energy. The turbulent kinetic energy is higher in case of spherical cavity-in-piston than cylindrical and flat pistons. A spherical cavity shows presence of squish near TDC both in measurements and simulation. More details on the flow structure are being pursued. ACKNOWLEDGMENTS The authors are grateful for useful discussions with Dr T N C Anand during the course of this work. REFERENCES [1] Heywood, J. B. 1998, Internal Combustion Engine Fundamentals, Mc-Graw-Hill Publications, New- York, 2 nd edition. [2] Fansler, T. D. 1993, Turbulence Production and Relaxation in Bowl-in-Piston Engines, SAE Transactions [3] Li, Y., Zhao, H., Peng, Z., Ladommatos, N. 2002, Tumbling flow analysis in a four-valve spark ignition engine using particle image velocimetry, Int J Engine Research, 3(3), [4] Muller, S. H. R., Bohm, B., Gleibner, M., Grzeszik, R., Arndt, S., Dreizler, A., 2010, Flow field measurements in an optically accessible direct injection spray guided internal combustion engine using high speed PIV, Experiments in Fluids, 48(2), 1-11 [5] Voisine, M., Thomas, L., Borée, J., Rey, P. 2010, Structure and in-cycle evolution of an in-cylinder tumbling flow, 15th Int Symp on Applications of Laser Techniques to Fluid Mechanics, Lisbon, Portugal. [6] Baby, X., Dupont, A. Ahmed, A., Deslandes, W., Charnay, G. Michard, M. 2002, A New Methodology to Analyze Cycle-to-Cycle Aerodynamic Variations, SAE Transactions, [7] Fogleman, M., Lumley, J., Rempfer, D., Haworth, D. 2004, Application of the proper orthogonal decomposition to datasets of internal combustion engine flows, Journal of Turbulence, 5 (23), [8] Raffel, M., Willert, C. E., Kompenhans, J. 2007, Particle image velocimetry: a practical guide, Springer Verlag, Berlin. [9] Berkooz., G., Holmes, P., Lumley, J. L. 1993, The Proper orthogonal decomposition in the analysis of turbulent flows, Ann. Rev. Fluid Mech., 25, [10] Amsden, A. A. 1997, KIVA-3V: A blockstructured KIVA program for engines with vertical or canted valves, LA MS, Los Alamos National Lab., US. [11] Micklow, M. J., Gong, W. D. 2008, Investigation of the grid and intake-generated tumble on the incylinder flow in a compression ignition direct injection engine, Proc. I MechE Part D: J. Automobile Engineering, 222, [12] Han, Z., Reitz, R. D. 1995, Turbulence Modeling of Internal Combustion Engines Using RNG κ-ε Models, Comb. Sc. and Tech., 106 (4 & 6), [13] Hill, P. G.., Zhang, D., 1994 The effects of swirl and tumble on combustion in spark-ignition engines, Prog. Energy. Comb. Sci., 20,