J Vis (2014) 17:197–209 DOI 10.1007/s12650-014-0203-8
R E G UL A R P A P E R
Hao Chen • David L. S. Hung • Min Xu Jie Zhong
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A dynamic thresholding technique for extracting the automotive spark-ignition direct-injection pulsing spray characteristics Received: 12 December 2013 / Revised: 4 April 2014 / Accepted: 10 April 2014 / Published online: 10 June 2014 Ó The Visualization Society of Japan 2014
Abstract The macroscopic characteristics of fuel injection spray are crucial for spark-ignition directinjection (SIDI) engines. To precisely quantify the macroscopic spray characteristics such as spray penetration length and spray angle, it is essential to determine the boundary of the spray structure from the images. Thresholding method is widely used for the spray boundary detection. It is normally achieved by selecting a single global thresholding value to distinguish the spray boundary from the image background. In this paper, a novel technique of dynamic thresholding, in which the threshold value is dynamically determined by the image local pixel intensity property in the vicinity of the spray boundary, is proposed. To evaluate the accuracy of the technique, a symmetric spray with well-defined geometry was used. The dynamic threshold method effectively reduced the difference between the right and left half-angles of the spray to only 0.5°, which is much smaller than the difference of as large as 3–4° obtained using global thresholding method. Similar results have been obtained for the spray penetration length measurements. Therefore, the proposed thresholding algorithm provides more accurate evaluation of macroscopic spray characteristics than the single-value global thresholding technique. Keywords Fuel spray Spark-ignition direct-injection engines Dynamic thresholding Global thresholding
1 Introduction In spark-ignition direct-injection (SIDI) engines, the macroscopic characteristics of fuel spray play a vital role in the combustion process, and thus influence the combustion efficiency and production of harmful emissions (Zhao et al. 1999). For the homogeneous-charge SIDI engine, the spray is injected directly into the engine cylinder during the intake stroke to create fuel homogeneity inside the cylinder. Therefore, it is important to minimize any wall wetting caused by fuel impinging on the cylinder wall or the piston top because fuel impingement on cylinder wall could dilute the engine oil lubrication, while the fuel splashing on the piston increases the formation of soot and unburnt hydrocarbon (Stojkovic et al. 2005; Mittal et al.
H. Chen D. L. S. Hung (&) M. Xu National Engineering Laboratory for Automotive Electronic Control Technology, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China E-mail:
[email protected] H. Chen E-mail:
[email protected] D. L. S. Hung J. Zhong University of Michigan-Shanghai Jiao Tong University Joint Institute, Shanghai Jiao Tong University, Shanghai 200240, China
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2011; Hung et al. 2007). For spray-guided SIDI engine, the fuel is injected late during the compression stroke to produce a more compact and richer fuel air structure, so that an ignitable fuel–air mixture could be formed in the vicinity of the spark plug (Peterson et al. 2011; Chen et al. 2011). In both SIDI combustion modes, the spray parameters including spray penetration length and spray angle are crucial to achieve the aforementioned goals. The in-cylinder fuel–air mixture formation processes are very complex (Sick et al. 2010). Even under the quiescent environment, cyclic variation of fuel spray was observed (Chen et al. 2013). The breakup process of liquid fuel into spray is perplexingly affected by many factors. Zigan et al. (2010) identified the significant effects of fuel properties on spray breakup, evaporation and stability. It was demonstrated that the highly unsteady, cavitating in-nozzle flow could be the origin for spray cyclic variation (Andriotis et al. 2007; Zigan et al. 2013). Under realistic engine running conditions, the cycle-to-cycle variation of spray behavior was augmented (Hung et al. 2003) due to the turbulent nature of in-cylinder flow (Chen et al. 2012). Marchi et al. (2010) compared the spray stability of three prototype piezoelectric pintle-type injectors. Their results showed that the spray formation of positive-step inwardly seal band injector was most reproducible, and it was ideal for SIDI engine applications. These processes considerably affected the subsequent combustion characteristics (Aleiferis et al. 2004). To achieve high combustion efficiency and low emission levels, both advanced simulation and experimental techniques have been widely utilized to investigate these intrinsic instabilities. Since the simulation results had to be validated by the experiments, the image-based experimental techniques played a critical role in engine research (Koh et al. 2006; Hargrave et al. 2000; Parrish and Zink 2012). The fuel spray characterizations, such as spray penetration length and spray angle, are usually obtained through imaging methods (Mitroglou et al. 2006; Soid and Zainal 2011). The quantification of spray geometry from images requires the determination of the spray boundaries which are often ambiguous (Macian et al. 2012). Therefore, distinguishing the spray region from the background by setting a threshold value is a very important step towards the quantification of spray characteristics (Sahoo et al. 1988). Many thresholding methods were utilized in the digital image processing field. Sezgin and Sankur (2004) provided an extensive literature review on this topic. However, in fuel injection spray research community, a fixed threshold value is generally selected to obtain the spray parameters. This simple global thresholding technique, mostly done in a subjective manner, has been widely utilized to identify the spray region by separating the background and its intrinsic noise. As expected, the threshold value has a direct effect on quantifying the spray characteristics. On one hand, a very low threshold value might not be able to differentiate the spray from the background. In this case, the penetration or spray angle cannot be obtained. On the other hand, an unnecessarily high threshold value could lead to the loss of useful information on the edge of the sprays. Under this circumstance, the image processing may give misleading information, such as shorter penetration than what the actual value should be. Moreover, most spray imaging techniques, such as light sheet imaging, illuminate the spray from a light source which is positioned perpendicular to the camera. This leads to an uneven distribution of light intensity across the spray structure (Berrocal et al. 2012). In addition, the light source is not always homogeneous, resulting in inhomogeneous spray image intensity distribution. Therefore, a single-value global thresholding scheme could lead to erroneous spray characteristics. A thresholding technique taking into account the local intensity properties and variations within the image should be adopted. In this study, a dynamic thresholding method utilizing the local information of spatial pixel intensity property is developed and demonstrated. Based on this dynamic thresholding method, the spray angle and penetration length are computed, and their values are compared with the results calculated from the singlevalue global thresholding method. The comparison illustrates that dynamic thresholding technique provides more accurate results than that of global threshold method. 2 Experimental setup and imaging test conditions Figure 1 depicts the experimental setup of the spray imaging test. Gasoline fuel spray was formed using an eight-hole SIDI injector at an injection pressure of 10 MPa. The spray was injected into a constant-volume chamber where the ambient pressure was set to reach an atmospheric condition. A piston accumulator combined with a pressurized nitrogen system was utilized to supply the required injection pressure. The eight-hole SIDI injector was installed vertically at the top of the chamber. Four quartz windows surrounding the chamber provided a full optical access inside of the chamber. Fuel spray was illuminated using a strobe
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light. The spray images were recorded by the imaging system which included a CCD camera (LaVision ImageIntense, 12 bit dynamic range) and a Nikon lens (f = 105 mm, f# = 4.5). The camera was located at an angle of 150° from the light source. Due to the nature of the multi-plume spray pattern emanating from the multi-hole injector, the light source was positioned in such orientation to maximize the illumination of the entire spray structure. A programmable timing unit (LaVision, PTU) synchronized the strobe light and imaging system with the fuel injection timing logic signal. The injection duration was fixed at 1,000 ls which corresponded to a nominal injection quantity for light to medium load of engine condition. Fifty spray images (1,376 9 1,024 pixels, with 18.44 pixel/mm) were recorded at each imaging time, namely 800, 900, 1,000, 1,100, 1,200 and 1,300 ls after start of injection (aSOI). Figure 2 depicts a spray image and the corresponding grey level distribution at a line of 25 mm below the injector tip. Five peaks shown in Fig. 2b correspond to the five individual spray plumes shown in the image. In Fig. 2b, the grey level peak at the spray right side is about 1.6 times of that from the left side. This is primarily due to the uneven light illumination on the spray structure, i.e., the spray is illuminated more from the right side. 3 Results and discussions 3.1 Development of the dynamic thresholding technique As pointed out by other researchers (Parrish and Zink 2012; Zigan et al. 2013), the light source (flashlight, laser sheet, etc.) illuminating the spray from the side is commonly adopted in fuel spray imaging experiments. As the light goes through the spray, its light intensity will be attenuated owing to the scattering or absorption by the spray, resulting in inhomogeneous grey level distribution of spray structure. Under this circumstance, applying the same global threshold value to the entire spray image could lead to erroneous
Fig. 1 Experimental setup
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Fig. 2 An instantaneous spray image at 1300 ls aSOI, and the grey level distribution on a horizontal line (the spray was illuminated from the right side)
values in spray penetration length and spray angle. On the contrary, a dynamic thresholding method which determines the threshold values according to the local pixel intensity properties offers much greater accuracy. In the following section, the mathematical procedure of a dynamic thresholding method is first described. Then, the spray angle and spray penetration length from dynamic threshold method are compared with the global thresholding method. The dynamic thresholding technique in this study takes into account the local brightness gradient as the indicator of the spray boundary. As demonstrated in Fig. 3, the brightness ratio between the neighboring pixels (the intensity of the right pixel over that of the left pixel) is computed along a horizontal line perpendicular to the spray axis. First, it is reasonable to deduce that the ratio between spray and background should be a maximum value, and the ratio between background and spray is a minimum value. Therefore, the maximum and minimum ratio points (i.e., points a, a0 , b, b0 , c, c0 in Fig. 3) that separate the spray from the background are identified. The brightness values of these 6 points are included in Fig. 3. For each of these 6 points, the average brightness value of its neighboring pixels could be a suitable choice of the threshold value at that location. As expected, the threshold values of these 6 locations are different. For instance, for all three lines at 5, 15, and 25 mm below the injector tip, their threshold values on the right side of the spray edge are about twice of that on the left side. It is brighter in the right side since the light source also enters the spray structure from the right side. In addition, the threshold values at the same side but different locations are different as well. For instance, the threshold value at point c’ is 4.2 times larger than that of point a’. The point light source creating an inhomogeneous pixel intensity distribution is the main reason for this phenomenon. For pulsing spray, the spray structure is transient due to the unsteady in-nozzle flow and complex interaction with the entrainment of air (Lefebrve 1989). Since the spray is temporally diluted owing to the liquid spray breakup and evaporation, the use of the same global thresholding values for different imaging times of the spray structure is not appropriate. Time-dependent threshold, where the thresholds for different instants are obtained from dynamic thresholding, could effectively resolve this issue. Therefore, it is necessary to access whether the dynamic thresholding approach works for other imaging time snapshots. The spray images at 900 and 1,100 ls aSOI are employed for this purpose. In the same manner, the dynamic threshold values at different horizontal lines for the time instants of 900 and 1,100 ls aSOI are demonstrated in Figs. 4 and 5, respectively. Due to the limited spray penetration length at 900 ls, only the horizontal line of 5 mm below the injector tip is shown. Similar to 1,300 ls (Fig. 3), the points separating the spray with background can be identified for all other instants. The pixel intensity used to calculate the intensity ratio is also included in Figs. 4 and 5. Again, the average intensity of the neighboring pixels is used as the threshold value at that location. The threshold values are different not only in the same spray image, but also for the different instants. It is worth mentioning that the dynamic threshold works even better for the early instants. It is because the spray and background are easier to be
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Fig. 3 Brightness ratio between neighboring pixels for spray image at 1,300 ls aSOI
distinguished at the early instants where the spray is optically denser. In the following analysis, only the results obtained from the instant of 1,300 ls aSOI are presented. It must be emphasized that the spray image has to be pre-processed to reduce the influence of the random background noise. Here, a moving average filter, which is the most common filter in signal processing (Smith 1997), is employed. Figure 6 shows the comparison between a spray image and its filtered spray image. From visual inspection, the main spray structures are quite similar, but the filtered image shows smoother along the spray boundary than the original image. To demonstrate this in a more convincing way, the grey values of the horizontal line (at 25 mm below the injector tip, highlighted in Fig. 6) in the spray
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Fig. 4 Brightness ratio between neighboring pixels for spray image at 900 ls aSOI
images are compared in Fig. 7. Figure 7a demonstrates that the major trend has not changed, while the Fig. 7b illustrates that the random low-order fluctuations of the grey value are removed. In essence, the dynamic thresholding technique is performed using the following two steps. First, the moving average filter is used to reduce the random noise and retains the major spray pattern information. Secondly, by scanning each horizontal line along the axis of spray on the pre-processed spray image as shown in Fig. 3, reliable thresholding values at different locations can be obtained. 3.2 Thresholding effect on spray angle In this section, the spray angle computed from the dynamic thresholding method is compared with that from the global thresholding method. As shown in Fig. 8, the spray angle is determined according to the SAE (Society of Automotive Engineers) J2715 Recommended Practice on Gasoline Fuel Injector Spray Measurement and Characterization (Hung et al. 2009). Basically, four points (M, M0 , N, N0 ), which separate the spray from the background, are identified to calculate the spray angle. According to the experimental results from the injector manufacturer, the injector used in this study has a design-intent spray angle of 70° and it is symmetrical with respect to its injector axis. In other words, the half-angle on the left side of the spray should equal the half-angle of the right side of the spray. However, any slight tilting of injector axis could result in difference between the left and right half-angles. Therefore, it is also critical to carefully align the injector axis with respect to the vertical camera axis to minimize any relative tilting between these two axes. The alignment was carried out as follows. First, a digital level gauge was used to confirm the horizontal orientation of optical table in which the imaging system and constant-volume chamber were mounted on. Then, a special injector fixture was utilized to position the injector axis vertical to the top surface of optical table. Finally, in the imaging setup, the vertical camera axis is adjusted to be perpendicular to the same surface of the optical table. Using this alignment procedure, it was estimated that the angle between the vertical camera axis and injector axis was within ±0.1°.
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Fig. 5 Brightness ratio between neighboring pixels for spray image at 1,100 ls aSOI
Figure 9 shows the comparisons of the left and right spray half-angles computed from global and dynamic thresholding techniques. Spray angle is computed from each of 50 spray images using both methods, and their average and standard deviation are plotted in Fig. 9. Six global threshold values, namely 75, 148, 164, 247, 259, 616, are utilized. They are the threshold values obtained from points a, a0 , b, b0 , c, c0 in Fig. 3. Three important observations could be found in Fig. 9. First, for all six global threshold values, the half-angle on the right side of spray is larger than that on the left side by 3–4°. As this spray is symmetrical, this difference is not reasonable. The same threshold value but different brightness levels for left and right sides (because the light illuminates the spray from right side) are the major reasons for this discrepancy. Secondly, after the dynamic thresholding method is adopted, the angle difference between left-half and
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Fig. 6 An instantaneous spray image (a) and filtered image using moving average filter (b)
right-half is only about 0.5°. In addition, both the left and right spray half-angle is about 35°. This is reasonable as it is symmetric spray with a design-intent spray angle of 70°. Thirdly, the standard deviation of the spray angle obtained from global threshold method is nearly 1°, while the deviation from dynamic threshold method is reduced to\0.5°. This suggests that global thresholding method might overestimate the spray angle variation between spray injection cycles. An explanation to the aforementioned observation is as follows: It is inevitable that an imaging system (such as the light source) has a certain level of shot-to-shot variations. In this situation, the use of the same global thresholding value for all spray images may falsely include this hardware variation as part of the spray variation. However, the proposed dynamic thresholding method in this study effectively eliminates this problem. In summary, Fig. 9 quantitatively demonstrates that the dynamic thresholding method provides more accurate spray angle characterization than that of global thresholding method. 3.3 Thresholding effect on spray penetration length As shown in Fig. 8, the spray penetration length is defined according to the SAE J2715 Recommended Practice. Essentially, the single point which has the longest axial (or vertical as seen in the picture) distance from the injector tip needs to be identified. Due to the spray pulse-to-pulse variation (Chen et al. 2013), this single point for spray penetration calculation could vary between different spray plumes. Figure 10 depicts two typical spray images. For pulse #40, the spray penetration should be computed by detecting point P within the right spray plume. For pulse #47, point Q on the left plume should be utilized. Therefore, if the same global threshold is used for both pulses #40 and #47, it means that the identification of points P and Q will utilize the same threshold value. However, as shown in Fig. 2, the brightness level in right plume is about 1.6 times stronger
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Fig. 7 The grey value along the horizontal line (25 mm below the injector tip) in spray image (Original image was filtered using a moving average filter)
Fig. 8 Definitions of spray angle and penetration length according to the SAE J2715 (Hung et al. 2009)
than that of right plume. Under this scenario, global thresholding method could introduce errors in penetration length calculation, and therefore dynamic thresholding method should be adopted. Using the same manner, the dynamic thresholding method is performed to compute the spray penetration length. The brightness ratio of neighboring pixels (the ratio of upper pixel intensity to the lower pixel intensity) for each vertical line in the spray image is calculated. Figure 11 depicts 12 typical locations of these vertical lines which are scattered in the vicinity of the end points of different plumes. It must be emphasized that the locations of these lines are primarily selected based on the locations of the spray plumes. The lines are labeled as ‘‘Line #1’’ to ‘‘Line #12’’, and six of their brightness ratios are depicted in Fig. 12. The brightness levels for the peak ratio are included in Fig. 12. In the same way as shown in Fig. 3, the vertical farthest point away from the injector tip for each vertical line could be identified as the peak brightness ratio (Fig. 12). In this way, the maximum one among all vertical lines can be regarded as the spray tip penetration length.
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Fig. 9 Left and right spray half-angle comparison for global and dynamic threshold methods
Fig. 10 Variation of spray structures from two different pulses
Fig. 11 Dynamic thresholding method for penetration length computation
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Fig. 12 Brightness level ratio between neighboring pixels for the vertical lines in Fig. 11
To demonstrate the advantage of dynamic thresholding over global thresholding method, the penetration length from both techniques is compared as follows. First, the spray penetration length of left spray plumes and right plumes is defined, as shown in Fig. 13 (the center plumes are not discussed here, because they are not in the focal plane of the camera, and this is why they appear to be shorter than the penetration of side plumes). Then, the penetration length for left and right plumes is computed for all 50 spray images using both global and dynamic thresholds. The average and standard deviation over the 50 obtained penetration lengths are plotted in Fig. 14. Two important observations can be drawn from Fig. 14. First, for global thresholding method, the right penetration length is 1.1 mm longer than the left side. However, using the dynamic thresholding method, the right penetration length is only 0.5 mm longer than the left one. The global thresholding method employs the same threshold value for the both left and right sides, but the right side is about 1.6 times brighter than that of left side. This is responsible for the larger difference between left and right penetrations. The dynamic thresholding method reduces this effect by 0.6 mm. Secondly, the standard deviation is slightly smaller for the dynamic thresholding method, suggesting that the global thresholding method could slightly overestimate the cycle-to-cycle variation of the spray penetration length. Therefore, Fig. 14 quantitatively illustrates that the dynamic thresholding method provides more accurate spray penetration computation than that of global threshold method.
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Fig. 13 Spray penetration definition
Fig. 14 Spray penetration comparison between dynamic and global threshold methods
4 Conclusions A dynamic thresholding method has been developed for determining the SIDI spray image macroscopic parameters. By focusing on the boundary of the spray edge, this method computes the intensity ratio of adjacent pixels, and the local threshold value is identified based on the peak value between the intensity ratio of spray and background. The important spray characteristics namely spray tip penetration and spray angle, have been compared with those obtained from the widely used single-value global thresholding technique. For the symmetric spray analyzed in this study, the dynamic thresholding method reduces the difference of the spray half-angle between the right and left side to only 0.5°, which is much smaller than the difference of as large as 3–4° using the global thresholding methods. Moreover, the cycle-to-cycle variation of spray induced by variations of imaging system (such as the shot-to-shot variations of the light source) could be effectively eliminated using the dynamic thresholding method. Similar results have also been obtained for the spray penetration length measurement. This quantitatively illustrates that dynamic thresholding method provides more accurate spray penetration length and spray angle measurements than the global thresholding method. In addition, the dynamic thresholding method also reduces the bias of a single threshold value which solely relies on the visual inspection of spray images, thus offering a practical way to define a more appropriate threshold value based on values from different critical locations along the spray boundary.
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Acknowledgments This research is sponsored by GM R&D and NSFC under grants No. 51076093/E060702 and 51176115/E060404. It is carried out at the National Engineering Laboratory for Automotive Electronic Control Technology of the Shanghai Jiao Tong University.
References Aleiferis PG, Taylor AMKP, Ishii K, Urata Y (2004) The nature of early flame development in a lean-burn stratified-charge spark-ignition engine. Combust Flame 136(3):283–302. doi:10.1016/j.combustflame.2003.08.011 Andriotis A, Spathopoulou M, Gavaises M (2007) Effect of nozzle flow and cavitation structures on spray development in lowspeed two-stroke diesel engines. In: Proceedings of international council on combustion engines (CIMAC) Berrocal E, Kristensson E, Hottenbach P, Alde´n M, Gru¨nefeld G (2012) Quantitative imaging of a non-combusting diesel spray using structured laser illumination planar imaging. Appl Phys B 109(4):683–694. doi:10.1007/s00340-012-5237-9 Chen H, Reuss D, Sick V (2011) Analysis of misfires in a direct injection engine using proper orthogonal decomposition. Exp Fluids 51(4):1139–1151. doi:10.1007/s00348-011-1133-z Chen H, Reuss DL, Sick V (2012) On the use and interpretation of proper orthogonal decomposition of in-cylinder engine flows. Meas Sci Technol 23(8):085302. doi:10.1088/0957-0233/23/8/085302 Chen H, Hung DLS, Xu M, Zhong J (2013) Analyzing the cycle-to-cycle variations of pulsing spray characteristics by means of the proper orthogonal decomposition. At Sprays 23(7):623–641. doi:10.1615/AtomizSpr007851 Hargrave GK, Wigley G, Allen J, Bacon A (2000) Optical diagnostics and direct injection of liquid fuel sprays. J Vis 2(3–4):293–300. doi:10.1007/bf03181445 Hung DLS, Chmiel DM, Markle LE (2003) Application of an imaging-based diagnostic technique to quantify the fuel spray variations in a direct-injection spark-ignition engine. SAE Technical Paper 2003-01-0062 Hung DLS, Zhu GG, Winkelman JR, Stuecken T, Schock H, Fedewa A (2007) A high speed flow visualization study of fuel spray pattern effect on mixture formation in a low pressure direct injection gasoline engine. SAE Technical Paper 2007-01-1411 Hung DLS, Harrington DL, Gandhi AH, Markle LE, Parrish SE, Shakal JS, Sayar H, Cummings SD, Kramer JL (2009) Gasoline fuel injector spray measurement and characterization: a new SAE J2715 Recommended Practice. SAE Int J Fuels and Lubr 1(1):534–548. doi:10.4271/2008-01-1068 Koh H, Jung K, Yoon Y, Lee K, Jeong KS (2006) Development of quantitative measurement of fuel mass distribution using planar imaging technique. J Vis 9(2):161–170. doi:10.1007/bf03181759 Lefebrve AH (1989) Atomization and sprays. Combustion: an international series. Taylor & Francis, New York Macian V, Payri R, Garcia A, Bardi M (2012) Experimental evaluation of the best approach for diesel spray images segmentation. Exp Tech 36(6):26–34. doi:10.1111/j.1747-1567.2011.00730.x Marchi A, Nouri J, Yan Y, Arcoumanis C (2010) Spray stability of outwards opening pintle injectors for stratified direct injection spark ignition engine operation. Int J Engine Res 11(6):413–437. doi:10.1243/14680874jer605 Mitroglou N, Nouri JM, Gavaises M, Arcoumanis C (2006) Spray characteristics of a multi-hole injector for direct-injection gasoline engines. Int J Engine Res 7(3):255–270. doi:10.1243/146808705x62922 Mittal M, Hung DS, Zhu G, Schock H (2011) Fuel spray visualization and its impingement analysis on in-cylinder surfaces in a direct-injection spark-ignition engine. J Vis 14(2):149–160. doi:10.1007/s12650-011-0083-0 Parrish SE, Zink RJ (2012) Development and application of imaging system to evaluate liquid and vapor envelopes of multihole gasoline fuel injector sprays under engine-like conditions. At Sprays 22(8):647–661. doi:10.1615/AtomizSpr006215 Peterson B, Reuss DL, Sick V (2011) High-speed imaging analysis of misfires in a spray-guided direct injection engine. Proc Combust Inst 33(2):3089–3096. doi:10.1016/j.proci.2010.07.079 Sahoo PK, Soltani S, Wong AKC (1988) A survey of thresholding techniques. Comput Vis Gr Image Process 41(2):233–260. doi:10.1016/0734-189X(88)90022-9 Sezgin M, Bl Sankur (2004) Survey over image thresholding techniques and quantitative performance evaluation. ELECTIM 13(1):146–168. doi:10.1117/1.1631315 Sick V, Drake MC, Fansler TD (2010) High-speed imaging for direct-injection gasoline engine research and development. Exp Fluids 49(4):937–947. doi:10.1007/s00348-010-0891-3 Smith SW (1997) The scientist and engineer’s guide to digital signal processing. California Technical Publishing, San Diego Soid SN, Zainal ZA (2011) Spray and combustion characterization for internal combustion engines using optical measuring techniques: a review. Energy 36(2):724–741. doi:10.1016/j.energy.2010.11.022 Stojkovic BD, Fansler TD, Drake MC, Sick V (2005) High-Speed Imaging of OH* and Soot Temperature and Concentration in a Stratified-Charge Direct-Injection Gasoline Engine. Proc Combust Inst 30(2):2657–2665 Zhao F, Lai MC, Harrington DL (1999) Automotive spark-ignited direct-injection gasoline engines. Prog Energy Combust Sci 25(5):437–562 Zigan L, Schmitz I, Flu¨gel A, Knorsch T, Wensing M, Leipertz A (2010) Effect of fuel properties on spray breakup and evaporation studied for a multihole direct injection spark ignition injector. Energy Fuels 24(8):4341–4350 Zigan L, Shi J-M, Krotow I, Schmitz I, Wensing M, Leipertz A (2013) Fuel property and fuel temperature effects on internal nozzle flow, atomization and cyclic spray fluctuations of a direct injection spark ignition–injector. Int J Engine Res 14(6):543–556. doi:10.1177/1468087413482320
