Modeling Sprays for Lean Premix Prevaporized Liquid Fuel Injection in Gas Turbines

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1 ILASS Americas, 2 th Annual Conference on Liquid Atomization and Spray Systems, Chicago, IL, May 27. Modeling Sprays for Lean Premix Prevaporized Liquid Fuel Injection in Gas Turbines Gareth W. Oskam Solar Turbines Incorporated San Diego, California, 9211, USA. Abstract Lean Premix Prevaporized combustion reduces nitrogen oxide emissions in stationary gas turbines. LPP requires the formation of a fuel spray in a stream of air prior to injection into the combustor. CFD modeling may speed development but success is limited by the quality of the spray models. Solar is assessing LES/DES spray predictions by comparison with drop measurements in a 2D premix duct. This paper will present a selection of the data collected and the comparison with LES/DES predictions. Comments will be made on the types of spray model that must be considered when analyzing premixing flows.

2 Introduction In combustion, flame temperature drives the process of NOx formation. Using a fuel-lean uniform mixture of fuel and air to reduce the peak flame temperature and obtain a uniform temperature distribution reduces the rate of formation of nitrogen oxides. One means to obtain these effects is to premix the fuel with a large proportion of the combustion airflow. The lean mixture provides the conditions to achieve the reduced peak temperature while premixing attempts to provide the uniform mixture distribution that leads to the greater uniformity of temperature. When liquid fuels are used, premixing leads to prevaporization of some of the liquid fuel and this reduces the formation of NOx because more of the liquid fuel burns in the vapor phase, like a gas, rather than in a droplet cloud phase where peak temperatures will reach stoichiometric conditions. Thus, when applied to liquid fuels, Lean Premix injection becomes Lean Premix Prevaporized (LPP) injection. The challenges of LPP fuel injection are many. The method of forming the initial spray, the design of the mixing channel and the control of the time history of the spray are critical to success. Practical problems abound in the mechanical design of such an injector. For example; the fuel must be distributed evenly into the air stream, droplets must be prevented from contacting mixing duct surfaces, mixing time must be short enough to avoid autoignition in the vapor while giving sufficient time for a large portion of the spray to vaporize, the passages that feed fuel to injection nozzles must be kept cool to avoid coking and must be easily cleared of fuel residues after a fuel changeover or shutdown. Many challenges are inter-related. For example, in order to distribute the fuel as evenly as possible a large number of injection locations may be desirable. However, the larger the number of injection points, the lower the pressure drop available to feed fuel at each location, which, in turn, reduces the range of operation where stable flows will be attained. Practically, it is necessary to restrict the number of fuel injection locations and create a design that spreads the fuel across the mixing duct in such a way that the mixture strength is as uniform as possible. Limiting the number of injection locations may lead to high momentum fuel streams that could impact on duct walls, which is undesirable because wall wetting generally leads to a reduction in mixture uniformity and hence flame regions of increased temperature. There are many devices that can be employed to disperse fuel as a spray into a duct; jets, pressure atomizers, swirl atomizers and air-blast atomizers being the most practical for gas turbine applications. This paper will examine some aspects of fuel dispersal by a simple jet in crossflow. Not only is the jet-in-crossflow a simple mechanical design, it is also amenable to high quality process control during manufacture, thereby leading to consistent performance across large numbers of jets. The disadvantage is that the formation of the droplet spray is controlled almost entirely by the fluid dynamics of the jet breakup process rather than the nozzle itself and thus it becomes extremely important to be able to predict the spray characteristics across the range of operating conditions of the application. Correlations may give predictions of the general shape of the jet and its trajectory [e.g. 1,2], and the mean drop size diameter, usually expressed as SMD. However, since correlations are dependent on the geometry and test conditions they do not provide sufficient information for general-purpose design of a fuel injector where complex 3-D airflows may be generated by, for example, a set of swirl blades. Thus it is necessary to use CFD codes to predict both the air stream and fuel droplet generation and mixing. The difficulty, here, is that each application has its own unique set of controlling parameters and applying general CFD codes to a wide variety of situations is complicated by the fact that the physics associated with those parameters may not be well defined. Adding to this complexity is the fact that gas turbine fuel injectors must operate over a wide range of conditions. Liquid jets that issue from nozzles flush with a wall are the classic jet-in-crossflow situation but there is a large body of study results [e.g. 1,2] showing that these jets tend to have reduced penetration into the airflow as the liquid flow rate is reduced from a design condition. This would lead to the wall wetting condition that must be minimized. One possible means to mitigate this problem is to position the nozzle at the end of a tube that protrudes into the air stream. However, there appears to be no published experience with this type of installation. Thus a need existed to generate basic data and comparisons with CFD predictions. Recognizing this need, Solar established the requirements for an idealized jet-in-crossflow premix test model, contracting the services of the University of California, Irvine, Combustion Laboratory (UCICL) to measure the drop size characteristics of sprays generated by different nozzles when operating under test conditions that simulated typical DLN conditions for the air stream and liquid jet. The results of these measurements were then compared with predictions from CFD codes that employed LES and DES fluid flow predictions coupled with Langrangian droplet

3 tracking techniques. The results neither a validation nor an endorsement of any particular code but served the purpose of attempting to understand the challenges that would be faced in employing CFD in the injector design process and indicating what modeling strategies might be required to provide useful predictions. Test Apparatus Figure 1 shows the 2-D duct. Within the duct is a vane and the liquid jet nozzle is positioned at the downstream edge. The reason for using the vane was to simulate the effect of a swirl vane. The diameter of the tube was similar to the radius to the vane trailing edge, thus it would appear in the flow as an extension to the trailing edge and thereby reduce the effects of shed vortices. Sprays generated by two nozzles are reported here, referred to as Nozzle A and Nozzle B. The cross sectional area of the duct and the diameters of the nozzles were chosen to suit the flow capabilities of the test facility. Nozzle A has a length to diameter (L/D) ratio of approximately 1.5 while nozzle B has L/D approximately 5. vessel with optical access for photography and LDA/PDPA measurements. The rig is capable of operating at pressures up to 16 bar and temperatures up to 8 C. Installation of the test section is shown in Figure 3. For the current tests, operating conditions were limited to 5 bar and 2 C in order to avoid autoignition of the liquid fuel. The fuel used in this study was MIL-PRF-724-Type II, a primary reference fuel composed of pure hydrocarbons. A typical composition is given in Table 1 while properties can be found in Table 2. Test conditions, were chosen to mimic the local air to liquid ratio (ALR) that would be expected in the vicinity of the fuel nozzle when placed in a working fuel injector. It could be argued that initial crossflow momentum ratio or Weber number might offer improved simulations of initial liquid column and break-up but because the principal focus of the investigation was gathering data on the downstream spray, ALR was used as this gave a higher liquid flow rate and therefore more drops and easier control of liquid flow. The test section was installed in the UCICL Pressure Rig as shown in Figure 2. The rig is a down flowing Vane Trailing Edge Inlet Plane Figure 1 Test Section Figure 2. Schematic of Pressure Rig Figure 3 Installation in UCICL Pressure Rig Figure 4. Typical spray from a plain nozzle at the end of a fuel tube creating a jet in crossflow.

4 Table 1. MIL-PRF-721-II Composition Weight% Moles Mol% C7H C8H C9H C1H C11H C12H Table 2. Typical properties Parameter Temp F Results Surface Tension per ASTM 1331 (dyne/cm) Vapor 318 F (psia) Vapor 333 F (psia) Vapor 348 F (psia) 15 C (kg/m^3) 4 C (cst) IBP MBP FBP > IBP = Initial Boiling point MBP = Mid Boiling Point FBP = Final Boiling Point Measured Data Prior to embarking on the measurement of drop sizes at defined locations, high-speed videos were used to gain an understanding of the flow characteristics and to help select suitable configurations for study. A typical screen shot from the selection study is shown in Figure 4. Whilst this is not a configuration chosen for CFD analysis it serves to demonstrate features that could be important. For example, liquid was seen to migrate along the downstream surface of the tube, presumably within the separation region, and then break away in what appeared to be small drops. Depending in the liquid flow rate, some of the main spray would impact on the opposite wall forming a film that would persist until the exit plane, spreading transversely as it moved downstream. There was some indication, though generally unclear due to limited framing rates, that some of the film would be sheared off the wall forming drops that re-entered the spray cloud. Complex behavior was observed in all configurations tested. It was clear that the spray was extremely turbulent and unsteady thereby necessitating the need for simulations using an unsteady method such as LES. The spray cloud was probed using a TSI PDPA system. Several cross-flow planes were mapped in order to establish a detailed database of spray characteristics. The results were reported as SMD, drop size distributions and velocity distributions (in streamwise and cross stream directions) at up to twenty points in each plane. In some planes beam cut-off by the test section upper and lower surfaces or very low droplet concentrations prevented all target locations being probed. Measurement planes commenced at the most downstream position and moved Inlet upstream in x/d=14 increments, where x/d is the ratio of distance to nozzle Vane diameter. A total of 5 planes were mapped, Nozzle centerline

5 Inlet Vane Nozzle centerline x/d=14 plane Figure 5: Illustration showing nozzle location and first measurement plane. Subsequent measurement planes were located at equal intervals along the duct. the most upstream plane being x/d=14 from the centerline of the jet as illustrated in Figure 5. Typical results are shown in Figures 6 and 7. Figure 6 shows spray SMD at the most upstream plane while Figure 7 shows the SMD distribution at the most downstream location. Notice that the SMD at a given location does not necessarily reduce in size as the spray progresses along the duct. This is consistent with observations of significant turbulent motion in the droplet cloud that could have resulted in drops translating across planes and wall wetting that might result in large drops stripping from the wall into downstream locations. When the data for individual measurement locations are plotted along the length of the duct, using the centerline of the nozzle as the origin plane, it is found that a variety of droplet histories exist. An example of this behavior is shown in Figure 8. Similar behavior was observed for Nozzle B, also. Figure 9 shows this behavior for the central plane. Drop sizes for Nozzle A - x/d=14 d/s of nozzle CL D32 Probe Volume Correction, Coincident Ch1 and Ch2 measurement of SMD Nozzle A - x/d=73 d/s of nozzle CL D32 Probe Volume Correction, Coincident Ch1 and Ch2 measurement of SMD 1 Plane SMD 37.5 microns 1 Plane SMD 49.5 microns % duct height % duct height % duct height local SMD Figure 6. Nozzle A: SMD Distribution at Upstream Plane. Spray SMD x/d=14 d/s of nozzle CL (D32 Probe Volume Correction, Coincident Ch1 and Ch2 measurement of SMD % duct height local SMD Figure 7. Nozzle A: SMD Distribution at Downstream Plane. Spray SMD x/d=73 d/s of nozzle CL D32 Probe Volume Correction, Coincident Ch1 and Ch2 measurement of SMD

6 Nozzle A: Drop sizes at plane on nozzle centerline NozzleB: Drop sizes at plane on nozzle centerline SMD - microns SMD - microns Distance downstream of jet C/L - % duct height Figure 8. Nozzle A: Local SMD downstream of the nozzle on the centerline of the flow field for 5 transverse planes. Each legend entry is the percent duct height, measured from the wall through which the nozzle is inserted Distance downstream of jet C/L - % duct height Figure 9. Nozzle B: Local SMD downstream of the nozzle on the centerline of the flow field for 5 transverse planes. Each legend entry is the percent duct height, measured from the wall through which the nozzle is inserted. Such changes in measured mean drop size could have occurred due to strong turbulent mixing, drop agglomeration through turbulence-driven collisions and liquid stripping from the wall film. The latter has been described in detail by Lightfoot [3]. Similar drop size variation behavior was seen in all other axial planes and, like the data shown in Figure 8, is apparently somewhat random in nature. Accumulating the SMD measured at each point in a transverse plane into a single measure for that complete plane produces the results shown in Figure 1. For Nozzle A it is seen that the overall average drop size increases for the first half of its history and then decreases at a slightly lower rate. However, for Nozzle B, the mean SMD was seen to increase fairly consistently as the spray progressed along the duct. The observations indicate that drop-wall interactions, drop-drop interactions and liquid film atomization may have a significant influence on spray cloud evolution by offsetting the effects of break-up and evaporation. This provides a guide towards the level of complexity necessary in the CFD simulation and suggested that full unsteady treatment of the continuous phase must be coupled with droplet evolution models that are as comprehensive as current knowledge allows. In considering what strategy to use in the CFD predictions it seemed that attempting to determine the overall distribution for a complete transverse plane could be a more fruitful target than attempting to predict mean drop size at any particular point location. This is likely to be more valuable in the design of an injector since we would not attempt to trace the fate of individual drops through combustion but would use the overall distribution of the spray as the initial condition. Variation of planar SMD with distance from injection point SMD - microns distance - % duct height Nozzle A Nozzle B Figure 1. Planar Cumulative SMD for 5 planes downstream of the nozzle

7 CFD Predictions The UCI rig and the spray test section were modeled using the Pro/E CAD software from PTC Inc. CFD analysis was conducted for Solar by CD-Adapco, in Tulsa, OK, using STAR-CD version 4. and by CFD Research Corp, Huntsville, AL, using CFD-ACE. The Detached Eddy Simulation (DES) technique was used for the unsteady spray calculations in STAR-CD in order to economize computing time while the predictions in CFD-ACE were performed in full LES. The fluid solid was meshed to create about 1.65 million cells in STAR-CD and about 3.5 million in CFD-ACE. LES requires very fine mesh near wall to resolve the boundary layer when Reynolds number is high; this also requires a small time step to be used. In DES, the near-wall region is treated using a RANS-model, which allows that the mesh can be substantially coarser; in regions away from wall, LES modeling is used. The time step size is now governed by the requirement for resolution of larger (detached from wall) eddies, which again allows reduction of effort compared to full LES up to walls. In flows for which a RANS-model near wall is sufficiently accurate, the computing effort can in this way be substantially reduced while gaining full benefits from LES-treatment of flow domain away from wall. The two regions are blended by a smooth transition [4]. Initial flow field calculations for the entire rig were performed using a steady-state RANS formulation to create a boundary set that was applied as the initial conditions for the full LES simulation of the airflow which commenced at the inlet to the test section and terminated just downstream of the exit plane. In order to verify that DES would provide similar results to LES, the airflow was calculated in STAR-CD with both LES and DES and applying two different turbulence models. The turbulence models used for DES were [Standard] K-Epsilon and Spalart-Allmaras. The K-E turbulence model appeared to provide velocities, gradients, etc that matched the LES predictions better than when using Spalart-Allmaras. This comparison was performed solely on the basis of the CFD results as no measurements were taken of the airflow. For the STAR-CD simulations, droplet calculations commenced at the nozzle exit using the drop size distribution measured at the x/d=14 (most upstream) measurement plane. Drop sizes were frozen up to the first measurement plane in order to preserve results. Droplet histories were also compared using RANS and DES simulations. The following snapshots, Figure 11, show the difference in the level of detail captured. Clearly, the DES spray simulation has the appearance of the actual spray, including the fine spray that is entrained into the region downstream of the nozzle tube. Drop size distributions were then calculated for each of four downstream planes. Typical results for nozzle A are shown in Figure 12, which shows that the drop size distributions are well predicted at the planes closest to the nozzle but that the models tended to under-predict the number counts for larger drops at the more downstream locations. RANS DES Figure 11. Nozzle A: Comparison of Unsteady RANS and DES.

8 Figure 12. Nozzle A: Drop size distributions at downstream planes The droplet break up model used in the predictions was that of Reitz and Diwakar [5]. This model has two mechanisms of breakup and, in STAR-CD, a critical Weber Number, We(crit), controls the onset of breakup. A typical value of We(crit) used is 6 but, in the geometry and conditions of the test cases, this may be inappropriate. Therefore, the Nozzle B test case was evaluated with We(crit) set at 6 and 12. The results are shown in Figure 13. Clearly, the influence of increasing We(crit) is to increase the proportion of larger drops but the basis for choosing a suitable value to use has yet to be determined. Notice also that the agreement between predictions and measurements is not as close at the x/d=29 and x/d=44 planes with this nozzle as was obtained with Nozzle A. Initial conditions are extremely important and it seems likely that the influence of wall interactions and drop-drop interactions within the spray cloud on the predicted drop sizes should not be excluded from the simulation. The predictions conducted in CFD-ACE used only droplet evaporation with droplet drag being modeled and did not include a break-up model but did use full LES treatment. Further, the treatment of the spray used the measured values of SMD, mass flux and velocity distribution at the plane x/d=14 from the nozzle as the initial conditions. The differences between the modeling strategies have led to some interesting observations regarding the differences and similarities between the two sets of predictions. A comparison of the predictions from CFD-ACE with the measured drop sizes, for Nozzle A, is given in Figure 14 and for nozzle B in Figure 15. At the two planes closest to the nozzle (x/d=29 and x/d=44), the predictions using evaporation alone give different distributions to those using break-up and evaporation, as might be expected. Using break-up and evaporation models appears to provide slightly better agreement between measurements and predictions. However, at the x/d=58 and x/d=73 planes the predictions using evaporation alone seem to provide better agreement with the measured values over certain ranges within the distribution.

9 Figure 13. Nozzle B: Effect of Critical Weber Number for initial breakup on droplet size history Figure 14. Comparison of droplet distributions predicted by CFD-ACE with measured drop distributions.

10 Figure 15. Comparison of droplet distributions predicted by CFD-ACE with measured drop distributions. Discussion The combination of break-up and evaporation appears to model the small drop size component of the distribution quite well, especially in the early stages of the spray history while modeling evaporation alone seems to provide a better estimation of large drop content through the entire spray. Thus both modeling approaches seem to have strengths and weaknesses. It is tempting to suggest from these results that droplet break-up must be considered in the early stages of the spray with evaporation being given the dominant role in the latter stages. However, this would be to ignore the effects of imposed initial conditions in the models and the possibility of drop interactions within the cloud and droplet stripping from the wall film, both of which could be significant influences. Therefore, the final goal of the comprehensive model of LPP fuel injection should be the consideration of the type of break-up model, the possible need for droplet-turbulence interactions, the influence of wall impacts and film formation, droplet stripping from films and the detail of the evaporation model. It could be extremely difficult to separate these various influences completely but, with care some could be eliminated from test cases and others built up over progressively more complex geometries and fuels. It would seem that both the LES and DES methods could provide reasonable indications of droplet distribution history but the models may need calibration against some measured data. DES may have some advantage in that the method is computationally less expensive than a full LES simulation. The ability of LES codes to predict the initial drop size distribution using VOF or similar methods has not yet been tested and is, perhaps, a more important goal since this would be valuable in many fuel injection strategies. Conclusions Drop size distributions measured in a two dimensional representation of a lean premix fuel injection system have been compared with DES and LES predictions of droplet history. Measurements show significant differences in the trends of SMD, at different longitudinal locations within the premixing duct that may indicate additional influences such as strong turbulent mixing, droplet interactions and stripping of drops from wall films. Considering the spray as a whole may be more appropriate than attempting to predict droplet characteristics at fixed locations. This applies to both measured data and predictions. LES and

11 DES methods offer the prospect of reasonable representations of planar drop size distributions but may need calibration, or at very least checking, against measurements. The DES method appears to provide simulation quality that is similar to LES but is computationally more economical. Droplet break up models may need to be optimized for the jet in crossflow and film stripping models may need to be included. References 1. Masuda, B. J., Hack, R, L., McDonell, V. G., Oskam, G. W. and Cramb, D. J., 25, Some observations of liquid jets in crossflow. ILASS-Americas, 18th Annual Conference on Liquid Atomization and Spray Systems, May 22-25, Irvine, California. 2. Linn, K.-C., Kennedy, P. and Jackson, T., 22, Penetration Heights of Liquid Jets in High-Speed Crossflows. 4th AIAA Aerospace Sciences Meeting, AIAA paper Lightfoot, M. D. A., 26, Atomization of wall-bounded two-phase flows. ILASS-Americas, 19th Annual Conference on Liquid Atomization and Spray Systems, May 23-26, Toronto, Canada. 4. Spalart, P. R., Deck S., Shur M. L., Squires K. D., Strelets M. Kh. And Travin A., 26, A new version of detached-eddy simulation, resistant to ambiguous grid densities. Theoretical Computational Fluid Dynamics, 2, pp Reitz, R. D., and Diwakar, R., 1987, Structure of High-Pressure Fuel Sprays. SAE Paper 87598, SAE Transactions, 96, (5), pp Acknowledgements: The author wishes to thank Solar Turbines Incorporated for permission to publish this work, Dr. V. McDonell, Mr. R. Hack and Mr. B. Masuda, of UCICL, for their tireless efforts to collect the PDPA data and to CD-Adapco and CFD Research Corporation for their support and agreement to publish the CFD results.