A Multi –plane PIV Analysis of In-Cylinder Flow Structures...

14th Int Symp on Applications of Laser Techniques to Fluid Mechanics Lisbon, Portugal, 07-10 July, 2008

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A Multi –plane PIV Analysis of In-Cylinder Flow Structures in an Optical

Engine under Part Load Conditions at 3500 rpm

Graham Pitcher1, Phil Stansfield2, Graham Wigley2 and Dave Hollis3

1: Powertrain Research, Lotus Engineering Ltd, Hethel, Norwich, UK, [email protected]

2: Aeronautical and Automotive Engineering, Loughborough University, Leics, UK, [email protected] 3: LaVision UK Ltd, Grove Technology Park, OX12 9FF, UK, [email protected]

Abstract Within the framework of an investigation into valve operating strategies for homogeneous charge direct injection spark ignition engines under part load conditions this paper discusses the in-cylinder flow fields developed by running an optical engine with an early inlet valve closing strategy with a lift of 3.9 mm that produces 3bar IMEP at 3500 rpm under firing conditions. The experimental facilities are similar to previous work but measurement technique has been optimized with the significant aspect of this work being that the flow fields were determined in three vertical planes and one horizontal plane to highlight the three dimensional and the highly cyclic nature of the flow. These characteristics have significant impact on the application of the PIV technique and data processing to ensure a high degree of confidence in the flow fields generated. The time varying nature of this three dimensional flow field is discussed and how it impacts on the PIV measurement technique particularly as regards data acquisition and data processing. The true extent of the complex three dimensional flow field is readily seen and that, even though the combustion chamber geometry is symmetrical, the swirl flow field is not. The main features of the vertical flow structure are described over a crankangle period from 90oCA to 120oCA which highlight the dominant feature as being a wall jet flow which interacts with the piston crown to generate a strong reverse tumble flow. 1. Introduction In-cylinder PIV measurements have been previously reported in an unthrottled 4-valve single cylinder optical gasoline direct injection engine motored at speeds of 750, 2000 and 3500 rpm [1]. Mean vector flow fields were produced during the latter half of the intake stroke in the symmetry plane between the valve pairs and tumble ratios presented. Within the framework of an investigation into valve operating strategies for homogeneous charge direct injection spark ignition engines under part load conditions this paper discusses the in-cylinder flow fields developed by running the engine un-throttled with an early inlet valve closing strategy and valve lift of 3.9 mm that produces 3bar IMEP at 3500 rpm under firing conditions. Unthrottled engine operation with variable intake valve lift, duration and timing is seen as a potential candidate for improving part load engine efficiency [2]. The experimental facilities are similar to those reported in [1] but the measurement technique as applied here has been optimised with a significant benefit for this work in the determination of the flow fields in three vertical planes and one horizontal plane to highlight the cyclic variability and the three dimensional nature of the flow. The time varying nature of these high speed, three dimensional flow fields with strong cyclic variations is well known, [3, 4 and 5], and will be discussed here in terms of its impact on the PIV measurement technique and data processing. This will be followed by a description of the engine flows as a function of crankangle.


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2. Experimental 2.1 Optical Engine The optical engine, Fig. 1, incorporated a full length fused silica liner and a sapphire window in the piston crown to provide optical access to the cylinder and combustion chamber. A carbon fibre piston ring maintains compression pressures, producing 22 bar at 2000 rpm with unthrottled port and standard valve timing. The four overhead poppet valves were actuated by a fully variable electro-hydraulic valve system, the Lotus AVT, which allowed complete control of the individual valve profiles. The engine has both primary and secondary balance shafts to allow operation at speeds of up to 5000 rpm. Engine timing data were provided by two optical encoders, one with 0.2° resolution mounted on a 2:1 drive which represented the camshaft timing and provided the maximum resolution of the laser diagnostic system. The second encoder provided a TDC pulse from the crankshaft and a 1.0° resolution pulse for the AVT timing electronics and the engine diagnostic system. The engine was operated at a standard test point replicating 3500 rpm and 3.0 bar IMEP where the IMEP was calculated over a 720° cycle to include pumping losses. The test point was achieved using an un-throttled EIVC inlet valve strategy.

Engine Geometry Bore 88.0 mm Stroke 82.1 mm Capacity 0.50 L Speed 3500 rpm Compression Ratio 10:1 Pressure 25.0 bar Valve Timing Data - relative to valve overlap Inlet Opening 15° BTDC Exhaust Opening 258° BTDC Max. Inlet Lift 3.9mm @ 75° Inlet Closing 172° Exhaust Closing 45° Max. Exhaust Lift 9.35 mm

Fig. 1: Single Cylinder Optical Research Engine 2.2 PIV System The PIV light sheet was generated by a New Wave “Solo 120” Nd:YAG double pulsed laser producing co-linear beams at 532 nm wavelength and approximately 50 mJ per pulse. The delay between pulses was varied to suit the velocity field as a function of crank-angle, and is discussed more fully in the data processing section, 3.1. The laser beams were expanded by a cylindrical lens and reflected up through the sapphire window in the piston crown, into the engine cylinder, as shown in Fig. 2. The aim of the experiment was to measure the airflow corresponding to the injection and mixing period over three vertical planes and one horizontal measurement plane. The vertical measurement planes were parallel to the symmetry plane but were referenced to the mid point between the spark plug and injector to avoid reflections from them, Plane 2, and 17 mm either side, Planes 1 and 3, as shown in Fig. 3.


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A LaVision Flowmaster 3 fast shutter CCD camera with a Nikon 60 mm Macro lens was focused on a region of the light sheet approximately 55 mm by 45 mm with a resolution of 1280 by 1024 pixels which was centered axially on a point 52.5 mm above the position of the piston crown when at BDC. The field of view of the vertical image plane is shown in Fig. 4. The reduced field of view, compared with the cylinder bore diameter, is beneficial as regards minimizing liner aberrations and low light levels which would cause the accuracy of piv data to decrease [6]. The camera and laser system were controlled remotely through LaVision’s ‘DaVis 7.0’ software. For the measurements in the horizontal plane the laser and camera were interchanged with the light sheet entering through the fused silica liner and the camera viewing up via the 45 degree mirror. The location of the horizontal light sheet in the vertical plane was set to intersect with the piston crown at 900 ATDC, i.e. 35 mm above the piston crown at BDC as shown in Fig. 4. With planes 1 and 3 offset from the cylinder axis the field of view is limited by the cylinder curvature while the field of view for the horizontal plane is limited by the 55 mm diameter window in the piston crown as well as any masks applied during the PIV processing.

Fig. 2 Schematic of optical engine and PIV system The intake airflow was seeded by silicon oil droplets of approximately 1 μm diameter from a Scitek seeding unit operating at 2 bar. The droplets were introduced into the intake port through four radial equally spaced fittings. The number of Laskin nozzles in the seeder was adjusted to control the seeding density, until 5-10 seeding particles could be seen in a typical 64 px by 64 px interrogation area. Fifty one image pairs were captured at each crank angle to ensure sufficient images for a reliable mean flow field without requiring excessively long experimental runs. La Vision’s “DaVis 7.0” PIV software was used to cross-correlate the image pairs. An image calibration was performed, to reduce the aberration caused by imaging through the cylindrical liner and background noise on each image was minimized by subtracting a mean background intensity calculated using the first five images of the dataset.


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Fig. 3 PIV vertical measurement planes Valves 1 and 3 are the inlet valves

Fig. 4 PIV horizontal measurement planes

A multi-pass cross-correlation algorithm was used to compute the vector fields from the remaining 46 images recorded on each sequential cycle. The first correlation employed a 128 px by 128 px interrogation area size with 50% overlap. A second, higher resolution, correlation then utilized a 64 px by 64 px interrogation area size also with 50% overlap. Two passes of the particle image were made for each interrogation area size. This technique yielded 46 crank angle resolved vector fields with a 32 by 32 px resolution, i.e. approximately one vector every millimetre. Mean vector fields were then calculated for each crank angle position. Tumble flow field data were obtained for a crankangle range from 500 ATDC on the inlet stroke to 500 BTDC compression, while the swirl flow field data are restricted from 1000 ATDC to 1000 BTDC due to the light sheet being below the piston crown. 3. Results and Discussion The PIV flow fields chosen for presentation here are restricted to the crank-angles where the piston crown is below the swirl measurement plane, where the axial velocities are up to 100 m/s and the instantaneous piston speed is above 10 m/s. 3.1 PIV Data Processing There are several aspects of image quality and data processing that need to be considered when processing the images collected from high speed in-cylinder measurements. One decision to be taken is made before the data collection even starts, and that is the time between laser pulses required to capture the velocity field. In an engine, measurements will be typically made at several engine speeds, and this variation will have an impact on the magnitude of the vectors to be measured. Therefore, it is necessary to change the time between pulses for the different engine speeds. Although the work presented here is only at one speed, to accommodate the changes observed in the velocity field versus crank-angle, it was necessary to use different delay times with crank-angle to accurately capture these velocity fields. During the first part of the inlet stroke,


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where there was a fast incoming jet through the inlet valves, the delay time was set to 3μs. This was increased to 5μs for the end part of the induction stroke and the beginning of the compression stroke, as the flow field decayed, and was increased even further, to 10μs, during the remainder of the stroke as the velocity field continued to decay in magnitude. These times utilized for the axial measurements had to be changed again to reflect the different magnitudes observed in the horizontal plane, where the delay times used were 10μs, 15μs and 20μs for the corresponding parts of the engine cycle. The use of a variable delay time proved to be an enabling technique for these measurements at high engine speeds. The axial/radial components of velocity have been measured through the cylinder liner, and so suffer from a distortion effect due to the curved surfaces of the liner which obviously increases with radius. To correct for this a calibration mask with known marker positions is placed in the liner and an image collected. The software can then calculate a de-warping function to correct for the distortion caused by the curved surfaces. The second pre-processing technique involved subtracting the background image. This increased the signal to noise ratio during the correlation. As subtraction of a reference image would not account for the seeding deposits accumulating on the liner, DaVis’ dynamic background subtraction, known as ‘subtract sliding minimum over time’ was used instead. This took a specified number of images, typically five, from the start of the dataset, and calculated the minimum intensity at each pixel location. This produced a new image, Imin which was then subtracted from the middle image of the five images. A new Imin was then calculated for images 2 to 6 and this was subtracted from image four. The process continued for all images of the dataset. Although this technique reduced the number of images available for analysis, (by four in this example), sufficient images from the engine were taken (typically 50), for this to be insignificant. With a running engine, even under motored conditions, there is the problem of window fouling over time, which can have a detrimental effect on the image quality, leading to a smearing in the images, Fig. 5. The upper and lower left hand plots show images collected through a dirty window and a clean window respectively, while the right hand plots show the corresponding vector plots, using only standard processing, obtained from these images. Despite the top image having seemingly considerable smearing effects, it has still been possible for the software to extract an acceptable vector field. However, it is considered that the window integrity is close to the limit where acceptable image quality can be captured. The upper image in Fig.5 also shows the importance of accurate masking of the image before processing, to ensure that only those parts of the image containing real data are processed. In the magnified part of the image a reflection of particles can be seen in the sapphire piston crown, where the laser light is input. Additionally, there are some bright flare spots where the laser beam is impinging on deposits on the window. All of these parts of the image have to be masked out to obtain good quality and reliable velocity vector information. The left hand, raw data, images also shows the difference in seeding density that occurs in an engine cylinder, where the in-cylinder volume is changing with time. This gives a different number of particle pairs in the interrogation region at different times in the engine cycle. For this work, it was attempted to have a minimum of 5 particle pairs per interrogation area, and this had to be set at the point in the engine cycle with the lowest seeding density, typically bottom dead centre, but possibly later if a significant amount of seeding vaporization was taking place on the compression stroke.


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Raw image at 90 CA showing smearing Vector field using standard processing

Raw image at 130 CA after cleaning Vector field using standard processing Fig. 5. The effects of smeared images

Careful setup and alignment of the PIV system gave between 90% to 95% first choice vectors for each image pair collected, i.e., for these vectors the main peak of the cross correlation function was substantially above the next highest peak. However, to further improve the consistency across the whole vector field, a search for rogue vectors was performed using two different filters. The first filter used was a median filter, where the median and RMS values of the surrounding 8 vectors was found, and the central vector was only accepted if it fell between the median minus the RMS and the median plus the RMS. The results of this filter are shown for the single cycle flow fields, (a), (b) and (c) in the left hand column of Fig. 6. In regions of high turbulence, where the RMS value for the surrounding vectors can be expected to be high, this filter fails to remove all the rogue vectors. To help overcome these limitations a second filter was tried, termed a de-noise filter. This fits a polynomial function through each vector and its surrounding vectors, ignoring specified outliers to ensure rogue vectors are not used to calculate the curve. This curve is then used to de-noise the vector field. This is a stronger filter than the median filter but can be more successful in identifying rogue vectors. The disadvantage of this filter is that it can introduce smoothing and so erode steep velocity gradients. However, for regions without any rogue vectors these velocity gradients would be preserved. The results of this filter, for the same set of data, are shown in (d), (e) and (f) in the right hand column of Fig. 6. It can clearly be seen that the flow fields have a smoother and more consistent look to them. The advantage of this filter is for when single cycle images are being studied, the cycle to cycle variation in a spark ignition engine is well known and can be readily identified in Fig. 6. The means from 46 image pairs are shown in Fig. 7, where the left hand plot has used the median filter and the right hand plot has used the de-noise filter. For these mean plots it can be seen that the choice of filter has no visible effect on the final processed vector field.


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(a) (d)

(b) (e)

(c) (f) Median filter 110 CA De-noise filter 110 CA Fig. 6. The effects of median and de-noise filters on single cycle data


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Mean from 46 image pairs with median filter

Mean from 46 image pairs with de-noise filter

110 CA 110 CA Fig. 7. Mean velocity field with two choices of data filter

3.2 Discussion of Results The highly cyclic nature of the generated flow fields obtained with the de-noise filter has been seen in Fig. 6. One of the most noticeable features of this cyclic variation is the change in positions of individual vortex centres, i.e. it is the structure of the flow itself that changes while the magnitude of the velocity vectors remains fairly constant. The main cause for this can be seen in the high speed flows in the upper right and then the lower right of the flow fields in (c) and (d) of Fig. 6. These flows originate through the inlet valves 3 and 1 respectively and shows that in some cycles one valve dominates the flow and can then switch for the next cycle, whereas Fig. 7 shows that this behaviour averages out over time. Previous experiments have shown that a minimum of 30 cycles of data are required to enable a stable mean flow to be calculated, however, due to these fluctuating flow patterns in the swirl plane this work suggests that the absolute minimum should be 50 image pairs. The plots in Fig. 8 show the axial/radial components of mean velocity in the left hand column at three different crank-angles, 100, 110 and 120 degrees, for the mid plane measurements. The right hand column shows the corresponding plots in the horizontal plane which transects the vertical plane at the 35 mm level. Over this time period, the vortex in the bottom right hand part of the vertical vector flow field passes through the swirl plane. This causes a complete change in the flow structure through the swirl plane with time and the direction of the principal vectors in the horizontal plane. This moving vortex is an example of the transient nature of these flows which in turn leads to the highly three dimensional nature of the flows. In the vertical measurement planes, it can also be seen that apart from the mid plane, there is a substantial third component of velocity and several horizontal vortices. This of course has an additional impact on the time between laser pulses that can be utilized to ensure that sufficient particles are captured in both images for the cross correlation. The conclusion from these horizontal velocity components is that there is a two dimensional flow in the mid plane region, with a substantial third component of velocity being evident in the other two measured vertical planes.


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1000CA Middle Plane 2



Fig. 8. Example of the transient and 3D nature of the flow fields


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To illustrate the tumble flow regime a series of plots from 90° to 120° crankangle are shown in Figs. 9 and 10, for the three vertical measurement planes. The corresponding valve lifts and instantaneous piston speed are also provided at each crankangle. In the mid plane, the dominant features are the two opposing vortices, one under the exhaust valves in the top left, which stays in the same position over crank-angle, and the second on top of the piston in the lower right, which moves down trying to track the the piston motion. This second vortex is generated by the high speed jet flow, up to 100 m/s, down the wall of the liner on the right hand side and impacting on the piston.

Plane 3

Middle Plane 2

Plane 1 90oCA valve lift-3.6 mm piston speed–15m/s 100oCA valve lift-3.3 mm piston speed–14m/s

Fig. 9. Tumble flows for 90 and 100 degrees crank-angle


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This jet flow originates from the lower edges of the inlet valves. For the low lift EIVC valve strategy employed here the valve lifts range from 3.6 to 2.2 mm over the four crankangle windows while the piston velocity remains high, from 15 to 11 m/s. Although the small valve lifts generate a more uniform pressure distribution around the valve periphery in the inlet port the high piston speed will produce very high shear gradients in the flow through the inlet valve curtains when compared with standard valve lifts. It is considered that the interaction of the strong shear flows between the two inlet valves and the cylinder is responsible for the high cyclic fluctuations seen in the swirl flow

Plane 3

Middle Plane 2

Plane 1 110oCA valve lift-2.8 mm piston speed–12m/s 120oCA valve lift-2.2 mm piston speed–11m/s

Fig. 10. Tumble flows for 110 and 120 degrees crank-angle


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patterns of Fig. 7. As the inlet valves close the wall jet flow decays rapidly such that the maximum velocity at 120oCA is down to 70 m/s and the penetration down the wall is significantly reduced. In planes 1 and 3, the vortex under the exhaust valve is only just visible as it sits much higher in the cylinder and there does not appear to be much movement of the vortex centre over the four crankangle windows. The second vortex on the other side of the cylinder is much weaker and there is less axial movement than for the vortex in the mid plane. This may be due to the low pressure region that would form under the inlet valves during the inlet stroke being a more significant and stabilizing force than that generated by the downward movement of the piston. The main feature of these flow patterns is of a strong reverse tumble flow generated by the interaction of the wall jet flow with the piston crown. 4. Conclusions A multi –plane PIV analysis of the in-cylinder flow structures generated during the intake stroke of an engine operating with a early inlet valve closing strategy at an engine speed of 3500 rpm. The flow patterns in three vertical planes and a one horizontal plane show that the flows structures are highly transient and three dimensional with a high cyclic variability. These characteristics have had significant impact on the application of the PIV technique and data processing to ensure a high degree of confidence in the flow fields generated. The effects of image smearing, due to seeding deposition, and the need for careful masking have been identified. The high speed transient nature of the flow requires the inter-frame delay time to be varied as a function of engine crankangle. To ensure a high degree of consistency for an analysis of the flow patterns on a cycle by cycle basis the PIV data have been filtered using a local polynomial filter rather than a linear median filter to remove inappropriate vectors. The main features of the flow structure has been described over a crankangle period from 90oCA to 120oCA which highlight the dominant feature as being a wall jet flow which interacts with the piston crown to generate a strong reverse tumble flow. 5. References [1] Stansfield, P., Wigley, G., Justham, T., Catto, J. and Pitcher, G., (2007), PIV analysis of in-

cylinder flow structures over a range of realistic engine speeds, Experiments in Fluids, Vol. 43, No. 1, pp 135-146

[2] Cleary, D. and Silvas, G., (2007), Unthrottled Engine operation with Variable Intake Valve Lift, Duration and Timing, SAE Paper: 2007-01-1282

[3] Towers, D.P. and Towers C. E., (2004), Cyclic variability measurements of in-cylinder engine flows using high-speed particle image velocimetry, Meas. Sci. Technol. 15, pp.1917–1925

[4] Li, Y., Zhao, H., Peng, Z. and Ladommatos, N., (2002) Tumbling flow analysis in a four valve spark ignition engine using particle image velocimetry Int. J. Eng. Res., Volume 3, Number 3, 1, pp.139-155(17)

[5] Jarvis, S., Justham, T., Clarke, A., Garner, C.P., Hargrave, G.K. and Richardson D., (2006), Motored SI IC engine in-cylinder flow field measurement using Time resolved Digital PIV for Characterisation of Cyclic Variation, Society of Automotive Engineers: 2006–01-1044 [6] Reuss, D. L., Megerle, M. and Sick, V. (2002), Particle-image velocimetry measurement errors when imaging through a transparent engine cylinder, Meas. Sci. Technol. 13 p.1029–1035

A Multi –plane PIV Analysis of In-Cylinder Flow Structures...

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