Evaluation of Column Breakpoint and Trajectory for a Plain Liquid Jet Injected into a Crossflow

Transcription

1 ILASS Americas, 1 st Annual Conference on Liqui Atomization an Spray Systems, Orlano, Floria, May 008 Evaluation of Column Breakpoint an Trajectory for a Plain Liqui Jet Injecte into a Crossflow S.M. Thawley, U.M. Monragon, C.T. Brown *, an V.G. McDonell Energy Research Consultants 334 South Pointe Drive, Suite E Laguna Hills, CA Abstract The injection of a plain liqui jet into a gaseous crossflow has been the subject of consierable stuy which has resulte in numerous empirical moels for the up, penetration, an ispersion of the liqui jet. In recent years, focus has been on penetration an qualitative features of the jet. Penetration is a critical value which has significant value for engineering esign applications. However, as etaile moels for the behavior of the jet evolve, eriving more quantitative information about the jet up process is becoming of greater interest to help improve CFD preiction of the jet behavior. Furthermore, as more sophisticate simulation approaches such as surface tracking methos evolve, a richer atabase for assessing accuracy is of great interest. In the present work, a etaile examination of the liqui column point is carrie out using high resolution, high spee igital imaging. Automate processing routines are evelope an applie to ientify column point an trajectory. As a result, new expressions for column point time an trajectory are obtaine base on thousans of images from a wie range of conitions. The new expressions are compare with previous expressions which were base on limite experimental methos an atasets. The results show systematic ifferences between the new expressions an the earlier expressions. The expressions evelope can be incorporate into CFD moeling approaches for the injection of plain liqui jets into high spee crossflows an may result in improve agreement with measurements. * Corresponing author (949) x 101 brown@erc-lt.com 1

2 Backgroun an Objective The moeling of liqui jet injection into a gaseous cross flow has been progressively stuie beginning with preiction of average far fiel spray characteristics an builing up to preictions of more complex phenomena relating to the near fiel an the liqui column [1,,3,4]. With increasing moeling focus on the liqui column, new measurement systems an processing are require to prouce ata sets for moel evelopment an valiation. Recently, high spee vieo has become a typical ata acquisition system for recoring the behavior of the liqui column, but often the processing of these vieos yiels only interprete or qualitative results. As a result, evelopment of an automate quantitative processing technique to etermine the liqui column trajectory an time has great value in increasing the utility of such results. The work presente herein reflects some significant steps towars this goal. The present effort buils off previous work in which a high spee vieo system was use to ocument the near fiel an liqui column behavior [4,5]. In the present work, the time of the liqui column has been chosen as the primary characteristic to work with for a number of reasons. The first reason is that liqui column time has a physical relationship with the experimental parameters treate in the current stuy incluing air velocity, liqui velocity, an orifice iameter. Secon the time is an important value for moeling purposes in the liqui jet in crossflow [4,6]. Finally, the time remains on the same scale throughout the entire range of practical operating conitions. In aition, times remain continuous throughout the three up moes: column, multimoe, an column shear. Liqui column trajectory was selecte as a seconary focus in this stuy because of both its relationship to liqui column time an its effect on important spray characteristics such as penetration an liqui mass istribution. The evelopment of moels for key physical phenomena has focuse on important imensionless quantities, such as Reynols number: Re ρ U j j j j = (1) μ j the Weber number, (in this case base on the crossflow properties): We ρ U σ an the momentum flux ratio: c c j c = () q ρ U j j = (3) ρ cuc Using these imensionless groups in the escription of features such as time provies a physical basis for interpreting the behavior. Reynols number is not feature prominently in many of the general jet behavior correlations. This parameter inicates the presence of instabilities on the jet surface as a transition from laminar to turbulent behavior occurs. Recent stuies suggest that once the jet is turbulent, the effect of other parameters such as the crossflow velocity on the nature of the atomization becomes less important. [7] The Weber number represents the ratio of the aeroynamic forces to the surface tension forces. The crossflow velocity use is typically that of the freestream etermine from volumetric flow an test section cross sectional area. The momentum flux ratio represents the relative strength of the liqui jet momentum in the injection irection versus the air momentum in the crossflow irection. For the liqui flow, the volumetric flow was measure an ivie by the orifice area, assuming a flow coefficient of unity. This approach was use for comparison to existing stuies as oppose to using calculate flow coefficient in orer to keep the efinition of important experimental parameters compatible with existing stuies. For the air flow, the mass flow an temperature were measure. The velocity was then calculate by iviing by both the ensity an test section cross-sectional area. In aition to the continuous effects of experimental parameters on liqui column up, the ominant moe of column up changes as the air an liqui flows vary. Typically the up process is split up into three regimes: column instability up, multimoe up, an column shear up.[7] The objectives of the current work are to (1) escribe a methoology to extract liqui column information from high spee vieo of liqui jet in crossflow experiments in an automate an statistically significant way an () apply this methoology to evelop correlation of the liqui column time an trajectory base on non-imensional characteristics. Approach The examination of the liqui column point analysis resulte in the preiction of liqui column time an liqui column trajectory base on experimental parameters incluing injector orifice iameter, aeroynamic Weber number, an momentum flux ratio. High spee vieo shaowgraphs were acquire to capture the time resolve behavior of the liqui jet injecte at 90-eg into a subsonic crossflow. These

3 shaowgraphs were then analyze to fin a massaverage trajectory curve an ientify a spatial location of the liqui column point. After the spatial characteristics for each image were ientifie, the results over entire vieos were average an transforme into physical quantities. Finally, correlation an regression analyses were conucte on the average quantities to associate the time an trajectory to the pertinent, non-imensional parameters of the various experimental conitions. Variations of air velocity, liqui velocity, an orifice iameters were taken into account while all non-varying properties were assume to be those of Mil-PRF-704 Type II. High spee vieos containing at least 00 images were recore for each of the experimental conitions escribe in Table 1. Image processing an point etection were performe on each image, over 5,000 ata points, an the correlation an regression analyses were performe on an average of the values in each of the 5 cases liste. Experimental Apparatus Test Section. A test section with inner imensions of mm x mm was use for the experimental stuies. A 150 mm roun to square transition was accomplishe over a length of 300 mm. Aitional etails of the geometry are shown in Figure 1. INJECTOR AIR FLOW TRANSITION SECTION HONEY COMB mm ia mm THICK QUARTZ WINDOWS AIR DUMP Table 1. List of Experimental Conitions Case Diameter mm Weber Number Momentum Ratio EXHAUST Z X Figure 1. Test Section. Y EXHAUST The cross flow air velocity (nominally 60 m/s) was establishe using a blower (Spencer Turbine) capable of elivering about 0.7 kg/sec at about 0.7 kpa (at the blower outlet). The air flow was metere with a calibrate mass flow meter (Sierra Instruments). The actual test section velocity was checke using a Pitot probe. The test liqui was supplie by a gear pump capable of elivering about 3.8 l/m at about,00 kpa. The liqui flow was measure with a calibrate turbine meter (EG&G Moel FTO-1 w/extene range) that has a range of to 0.3 l/m with accuracy of 0.05% of reaing. A pressure transucer (Omega Moel PX1-15G) with a kpa range w/ 0.5% accuracy was use to log pressure upstream of the injector, but ownstream of the shutoff an control valve. A glycerin fille pressure gauge (0-15psig) was use to check the pressure reaings. Further etails about the experimental apparatus an measurement methoology can be foun elsewhere. [8]. Injector. Figure shows a sketch of the injector geometry an orifice iameters use: 0.48, 0.94, an 1.30 mm. 3

4 4 (mm) (mm) eg mm Figure. Sketch of injector geometry. Methoology Image Preprocessing. To streamline the core of the calculations performe on the vieos an to ensure the program is insensitive to changes in experimental conitions or acquisition proceure, some preprocessing of the images was require. First, the vieos are converte from proprietary formats into a set of iniviual tiff images allowing for manipulation by image eiting software packages or programming languages. Once the vieos have been converte to a usable form, the region upstream of the injection an any other region that oes not contain the liqui phase are croppe out. In aition, the images were oriente an ajuste to ensure that cases with iffering acquisition an environments are irectly comparable. Figure 3 shows an example an image before an after preprocessing. liqui column trajectory using all of the pixels illuminate by the liqui column an assigning the point to be an on the trajectory path, the point location can be assigne in a way that is insensitive to the local liqui column istortions an the nonsymmetric behavior of the liqui column typical at up. The process of fitting a continuous trajectory to the image of the liqui column is mae up of three parts: (1) isolation of liqui column, () selection of a fit equation, an (3) regression of the fit coefficients using the Hough transform metho. Pixels signifying the liqui column were selecte base on the assumption that the image backgroun, which illuminates most of the pixels can be efine as a ranom range of illumination centere aroun the noise floor. This noise floor an the range about the noise floor were esignate as the meian intensity of the entire image an two stanar eviations accoringly. The image was then threshol at the intensity corresponing to the meian plus two stanar eviations. a) linear b) log log c) semi-log Figure 3. Example of Image Before an After PreProcessing. Liqui Column Trajectory Mapping. The liqui column trajectory serves as a path along which to search for the liqui column point an serves as a metho of locating the point that is consistent over the range of images in each high spee vieo an images from one vieo to the next. Also, by fitting the 4 Figure 4. Comparison of Liqui Column Shape of Varying Axis. The secon step, selecting a fit equation was accomplishe by first looking to existing stuies. Base on these stuies [5,9,10,11] the possibilities were re-

5 uce to linear, exponential, an log fit. In orer to select one fit, the liqui column points were plotte on each scale. Figure 4 shows an example image plotte on the three scales. Figure 4 emonstrates that an equation of the form in Equation 4 is the best choice of a fit equation for the liqui column because of the apparent linear tren of the liqui column. y = m ln( x) + (4) y o where m an y o are fitting coefficients. Fit Process. The stanar fitting metho for a linearize fit equation such as Equation (4) is the Least Squares metho which minimizes the error for all of the ata points compare to the fit equation. Although this metho is fast an efficient, it is only capable of fitting a single curve to all of the ata. Using this metho in the current set of ata prouces a bias towars the wall since a large number of pixels to be fit are intensities generate by the surface reflection an not signal ue to the liqui column. Instea the Hough transform metho was use to fit the liqui column [1]. The Hough transform is commonly use when ientification of more than one object or curve is esire. This feature is exploite to locate both the wall an the liqui column. Then eliminate the wall as a whole rather then eliminating each pixel in the wall reflection. Equations (5) an (6) were use to parameterize an linearize the spatial location of pixels ientifie to be in the liqui column in the previous step. where r = x' cos( ϕ) + y sin( ϕ) (5) x ' = ln( x) (6) In the general case for each x an y location r woul be calculate using Equation (5) for a range of φ s creating a curve of values in Hough space representing that single point in Cartesian space. In Equation (5) points that are collinear in Cartesian space will occupy the same point in Hough space. For example a line that follows the +X axis will all yiel a value for r of 0 when φ is equal to 90 egrees because y an sin(φ) will equal 0. For any set of collinear points in Cartesian space there is a value of φ that will yiel a single value of r. This process is more computationally intensive than the Least Squares metho but the range of r an φ can be limite by applying constraints to the possible location an orientation of the result. For example, in the current stuy, the liqui column in assume to have a positive slope an one point on the fitte line, an the center of the injection orifice is known. These two characteristics of the shape inuce the following constraints on possible values for φ, 70 to 360 egrees, an r, less than 0. Precise fitting coefficients are locate by binning in Hough space an then counting the number of x-y points that prouce values within each r-φ bin. The bin fit line is then etermine using Equation (5) to solve for the equation that correspons to the bin with the greatest count. Briefly, the Hough transform metho is compose of the following steps: parameterizing linear fit equation such that collinear points yiel equal parameters collect all possible parameter possibility for each ata point to be fitte bin points by parameter location select bin that matches possible parameter value for the maximum number of ata points transform parameter location of maximum bin back into ata variables Trajectory Error Analysis. The liqui column trajectory mapping process has three sources of error. The first is iscretization of the measurement region into pixel sensors on the camera giving an uncertainty of 1 pixel in any irection of the position of any feature capture. Discretization also causes the location of the orifice center to have an uncertainty of 1 pixel because the ege of the liqui column at injection can only be ientifie to an accuracy of 1 pixel. Finally, the binning of the Hough transform causes the fit slope, m, to have an uncertainty up to 1.9 pixels. The maximum uncertainty was applie to the maximum height yieling a value of 1.65 pixels. The position of any point on the liqui column trajectory then has a value of. pixels arrive at using Equation 7. 5 ε trajectory = ε (7) Break Point Assessment. One feature that istinguishes the liqui column an large clumps from rops in a high spee vieo is a sharp intensity graient. For both shaowgraph an laser illuminate surface reflections, the ege of the liqui column typically varies from near saturate to the noise floor within on or two pixels. A rop, on the other han, oes not typically prouce enough signal to reach intensity values near saturation an therefore oes not prouce the sharp bounaries that are inicative of the liqui column an large clumps. In aition, for the current set of ata, the illumination profile is an integrate value in the z, span-wise, irection. Therefore, regions of rops have out-of-focus effects that ten to blur the ata an soften i

6 the bounaries of iniviual rops. This effect was exploite to create efinition of point that is robust to noise an varying acquisition settings. The point for this stuy was efine as the first slice of space perpenicular to an centere on the liqui column trajectory that i not contain a significant number of pixels containing the liqui column bounary. Ientifying the point base on the efinition above was complete using a process of 5 steps: 1. Detecting the liqui column an clump bounaries.. Stepping along the liqui column trajectory. 3. Defining a slice in space that is normal to the trajectory at that point. 4. Counting the number of bounary pixels within the efine slice. 5. Designating the first point not containing a significant number of bounary pixels. Bounary Detection. Creating a bounary image was accomplishe by scanning through the image at each pixel location an assigning a new value base on the sum of the ifference between matching neighboring pixels as shown in Figure 5. Then a binary threshol was performe on the bounary values an save as a separate image. Figure 6 shows an example of a bounary image. (i-1,j+1) I = 80 (i-1,j) I = 100 (i-1,j-1) I = 10 i+1 j+1 (i,j+1) I = 110 (i,j) (i,j-1) I = 180 (i+1,j+1) I = 10 (i+1,j) I = 180 (i+1,j-1) I = 0 I = ( - ) j+1 i-1 + ( - ) + j-1 i-1 i+1 ( - ) + j I j-1 j - i-1 ( j+1 ) i+1 j-1 Figure 5. Illustration of Bounary Algorithm. Processe Image Bounary Image Figure 6. Original an Result of Bounary Image Algorithm. Step Along Liqui Column Trajectory. To ensure that each slice was evenly space from the beginning of the liqui column where the trajectory is primarily vertical to primarily horizontal near the point, the position of each ensuing step was calculate using the arc length. The change in the horizontal irection, x, was estimate using a line tangent to the liqui column trajectory. Then the change in x corresponing to the istance of 1 pixel along the tangent line was calculate as follows in Equation (8). Once the new x-location was foun, the y value was solve using the liqui column trajectory. m x = 1 + (8) x Define Normal Slice. After the start point on the liqui column trajectory was establishe, a slice perpenicular to the trajectory at that point was create. A slice is then constructe of each pixel that falls on a line normal to the trajectory of length one iameter an all of the neighboring pixels. The neighboring pixels were inclue to ensure that bounaries were not skippe because of rouning from the transformation between the continuous line an iscrete pixel locations. Figure 7 shows an exaggerate illustration of the steps along the liqui column an the perpenicular slices constructe. 6

7 Figure 7. Counting Bounary Pixels. The point location correspons to the first empty slice as oppose to the last slice containing bounary pixels. The first empty slice in this case is more inicative of the point. Because of the overlapping nature of the slices, two or more slices may see the same pixel an therefore more than one slice coul be counting the last bounary pixel at the point. Therefore the first empty slice was chosen to ientify the point because it is efinitely unique whereas the last slice containing bounary pixels may be ambiguous because some of the pixels may have alreay been counte. Breakpoint Error Analysis. Two ifferent types of error are inherent to the point assessment. The first was the qualitative classification of the liqui column point. Ientification of the liqui column point by visual inspection may be ifferent then the local area that the algorithm ientifie as the point. The program was calibrate such that the ientification of a point was conservative. In many respects large liqui clumps act in a fashion similar to the liqui column, an so the program is likely to chose a point that was esigne to be longer than what may be ientifie by visual inspection. This type of error will be ignore as a wiely accepte point efinition oes not yet exist. The secon type of error is the quantitative errors ue to iscretization an calculation. The error in the x irection is 1.8 pixels. Once the x location is foun it is use with the trajectory equation to fin the y location. This metho yiels a maximum error in y of 31.4 pixels. In the current stuy, for a typical fiel of view this is approximately 1.5 mm. The uncertainty in the y irection is much higher because it is epenant on the x uncertainty in aition to the uncertainty associate with trajectory fit. In general the error still falls within the spatial resolution of phase Doppler measurements. Break Time Calculation. In the current stuy the liqui column location was measure irectly but to erive the time, the liqui velocity is also require. In previous stuies, the liqui velocity magnitue was assume constant [9] an likewise the assumption of constant liqui velocity magnitue was use here. Therefore the time is simply the length of the liqui column ivie by the injection velocity. Figure 8 shows a plot of liqui column times measure in the current stuy. As shown in the plot, the liqui column time ecreases continuously in an exponential fashion with respect to the momentum ratio an Weber number. Also, the ifferent orifice iameters with similar momentum ratios an Weber numbers (iamons an circles on the plot) have similar results suggesting that the orifice iameter oes not effect the liqui column time. In summary, the results of the liqui column time suggest a single value continuous correlation between liqui column time an the relevant experimental parameters. Results Break Time Correlation. The first step to fining the best possible fit equation is selecting the proper inepenent variables. Although 5 cases is a small sample of the total possibilities in the region over which a final correlation woul be applicable, each of the points are base on 00 samples giving a high confience that each case is accurately represente. The pool of variables use to test for correlation was all of the pertinent non-imensional escriptor for the experimental conitions an the orifice iameter ue to its prominence in many previous stuies [5, 10, 11]. These variables inclue: aeroynamic Weber number, momentum flux ratio, liqui column Reynols number, air Reynols number (base on orifice iameter), an the orifice iameter. Using all non-imensional variables alleviates possible complications with units an fractional exponents later in the regression process. Also, Ohnesorge number was not use because it iffers from Reynols an Weber numbers by only surface tension which was not varie in the current stuy. Once the variables were selecte, the correlation coefficients to the liqui column time were foun. Using an R-table to fin the correlation coefficient require for 99% confience that the variable is correlate to the value in question yiels a value of for a system with 3 egrees of freeom (5 cases ). Base on this criteria liqui column time is significantly correlate to Weber number an both gas an liqui Reynols numbers. The physical implications of epenence on Weber an the Reynols numbers are that the liqui column time is ominate by the instability wave length an growth rate which in turn are epenant on the force balance between momentum, viscous effects, an surface tension. This result is in agreement with 7

8 existing ieology, the etail of which can be foun in work by Reitz [6] an Varga [13]. y = We 0.05 q 0.5 [1.46ln o o x + 3.3] (14) Regression Analysis. Base on the results from the correlation results, a least squares regression was performe using the variables aeroynamic Weber number, We, air Reynols number, Re gas, an liqui Reynols number, Re liq. The form of equation use for the Least Squares regression is: A B B t [ ms] = KWe Re Re (9) where K, A, B, an C are fitting coefficients. The equation was linearize an the results of the Least Square fit are as follows: t [ ms] (10) = 6We Re g Rel A comparison of the measure an preicte times are shown in Figure 9. In aition to using the variables ientifie in the correlation analysis, a regression using the variable Weber number, We, an momentum ratio, q, was performe. The selection of We an q is base on previous work for liqui column times, an penetration analysis. [5,9]. The fit using only We an q shown in Figure 10 resulte in Equation (11) an has a fit R value of t [ ] 64 ms We q g = (11) The results show that with variable sets, [We, Re air, an Re liq ] an [We an q] the liqui column time is accurately preicte for experimental conitions in all three up regimes. Liqui Column Trajectory Correlation. Using the same process as liqui column time, correlations for location (Equations (1) an (13) an column trajectory (Equation (14) were create. X o = 6.9 l (1) Y 0.53 =.5q (13) o Evaluation of Results. While correlations for spray penetrations are numerous an have been stuie wiely [8], a stuy specifically aime at the liqui column trajectory was not apparent in the current literature. However, irect comparison can be mae for liqui column time as shown in Figure 11. The correlations evelope in previous stuies are shown in Equations (15) an (16). t b t ρ o l = 3.44 (15) [5] U g ρ g D ρ = (16) [14,15] 0.6 o l 5 We Urel ρ g The present results inicate shorter up times for high momentum ratios an gentler graients with both Weber number an momentum ratio. The major ifferences are stronger epenence on air velocity, incluing its effect on Weber number, at low flows an inclusion of liqui momentum ratio as a etermining factor. The inclusion of liqui momentum ratio accounts for the effect of the liqui column surface instabilities which are the primary cause of liqui column up when the liqui velocity is much greater than air velocity. Correlations for the current stuy are base on a wier range of both air velocity an liqui velocity than previous stuies. In aition, previous stuies were built on a phenomenological founation with example results to etermine coefficients an to justify the form of the equation. The current stuy uses a statistical approach where all of the measure system variables were teste for correlation an engineering jugment was use after the fact to contextualize the correlation reache. Using the statistical approach was mae possible by the large number of experimental conitions use an the large number of ata points at each conition an the automate fashion that the processing was complete. 8

9 Break Time, ms Momentum Ratio, q Figure 8. Plot of Liqui Column Break Time Results. We=9.3 We=93.8 We=140 We=314 We=345 Break Time, ms We=9.3 We=93.8 We=140 We=314 We= Momentum Ratio, q * Hollow markers enote measure an soli marker enote fitte values Figure 9. Comparison of Measure Liqui Column Break Time an Fitte Values (Eq. 10). Break Time, ms We=9.3 We=93.8 We=140 We=314 We= Momentum Ratio, q * Hollow markers enote measure an soli marker enote fitte values Figure 10. Comparison of Measure Liqui Column Break Time an Fitte Values (Equ 11). 9

10 a) Equation 15 Conclusions An automate processing methoology was use to extract liqui column time an liqui column trajectory from high spee vieo. Correlations were then create using ata from 5 experimental cases each with over 00 ata points using the methoology evelope. The correlations evelope: t [ ] 64 ms We q = (11) y = We 0.05 q 0.5 [1.46ln o o x + 3.3] (14) b) Equation 16 give a preiction of the liqui column time an liqui column trajectory that agree well with measurements over all three up regimes. The feature extraction methoology evelope is scalable to large ata sets incluing a more etaile matrix of test conitions an incluing all of the key variables riving the physical phenomena of liqui column evolution over an above liqui column time an liqui column trajectory. Acknowlegements The authors gratefully acknowlege support from the U.S. Air Force (Contract FA C- 53, Barry Kiel contract monitor) an NASA (Contract NNX08CB33P, Yolana Hicks, contract monitor). The assistance of Mr. Chris Antes in the setup of the facility is note. c) Present Stuy Equation 14 Nomenclature = iameter m = slope (fit parameter) y o = intercept (fit parameter) t = time U, V = velocity ε = error ρ = ensity σ = liqui surface tension μ = viscosity φ = Hough angle parameter r = Hough raius parameter x,y,z = istance per sketch below u,v,w = velocity component (see below) Figure 11. Liqui Column Break Time Comparison. 10

11 Air cross flow z, w y, v Liqui injection Subscripts c = crossflow g = gas j,l = liqui jet rel = relative velocity o = initial 1 = upstream = ownstream Break = point Rel = relative x, u References 1. Kush Jr. E.A., an Schetz J.A., Liqui Jet Injection into a Supersonic Flow, AIAA Paper (1975).. Heister S.D., Nguyen T.T., Karagonzian A.R., Moeling of Liqui jets Injecte Transversely into a Supersonic Flow, AIAA Journal 7:4-443 (1989). 3. Brown C.T., McDonell, V.G., Near Fiel Behavior of a Liqui Jet in a Crossflow, 19 h Annual ILASS Americas, Toronto, Canaa, May Maabhushi R.K., A Moel For numerical Simulation of Breakup of a Liqui Jet in Crossflow, Atomization an Sprays 13: (003). 5. Wu P.K., Kirkenall K.A, Fuller R.P., Neja A.S. Breakup Processes of Liqui Jets in Subsonic Crossflows, Journal of Power an Propulsion 13:1:64-73 (1997). 6. Reitz R.D. Moeling Atomization Processes in High-pressure Vaporizing Sprays, Atomization an Sprays, 3:3: (1987). 7. Sallam K.A., Aalburg C., Faeth G.M., Breakup of Roun Nonturbulent Liqui Jets in Gaseous Crossflow, AIAA Journal 4:1: (004). 8. Brown C.T., Monragon U.M, McDonell V.G., Investigation of the Effect of Injector Discharge Coefficient on Penetration of a Plain Liqui Jet into a Subsonic Crossflow, 0 th Annual ILASS Americas, Chicago, Illinois, May Lin K.C., Kenney P.J., an Jackson T.A., A Review on Penetration Heights of Transverse Liqui Jets in High Spee Flows, 15 th Annual ILASS Americas, Maison, Wisconsin, May Becker J. an Hassa C., Breakup an Atomization of a Kerosene Jet in Crossflow at Elevate Pressure, Atomization an Sprays, 1:1:49-63 (00). 11. Stenzler J.M., Lee J.G., an Santavicca D.A., Penetration of Liqui Jets in a Crossflow, AIAA Paper No (003) 1. Pratt W.K., Digital Image Processing, n Eition, John Wiley & Sons, New York (1991). 13. Varga C.M., Lasheras J.C., Hopfinger E.J., Initial Breakup of a Small-iameter Liqui Jet by a High-spee Gas Stream, Journal of Flui Mechanics, 497: (003). 14 Khosla, S. an Crocker, D.S. (004). CFD Moeling of the Atomization of Plain Liqui Jets in Crosslfow for Gas Turbine Applications, Paper GT , Turbo Expo 004, Vienna, Austria, June. 15 Mazallon, J., Dai, Z., an Faeth, G.M. (1999). Primary Breakup of Non-Turbulent Roun Liqui Jets in Gas Crossflows, Atomization an Sprays, Vol. 9, pp