Numerical Study of Internal Flow Structures in a ...

AIAA 2015-2424 AIAA Aviation 22-26 June 2015, Dallas, TX 33rd AIAA Applied Aerodynamics Conference

Numerical Study of Internal Flow Structures in a Sweeping Jet Actuator

Downloaded by Kursat Kara on July 3, 2016 | http://arc.aiaa.org | DOI: 10.2514/6.2015-2424

Kursat Kara1 Aerospace Engineering Department, Khalifa University, Abu Dhabi, United Arab Emirates, 127788

This study focuses on the generation and interaction of internal flow structures, jet oscillation process, and pressure drop mechanism of a Sweeping Jet Actuator. Timedependent numerical analysis was performed over a range of inlet mass flow rates. The effect of varying inlet mass flow rate on the sweeping jet oscillation frequency was calculated and a strong agreement was found with the experimental measurements. The velocity, temperature and pressure fields are provided. The complex flow field inside the Sweeping Jet Actuator for half an oscillation cycle are presented by velocity magnitude and total pressure contours. Formation of vortices from sharp corners in the actuator core surfaces were observed, and their role in jet oscillation is shown.

I. Introduction

I

MPROVED aerodynamic designs and new aerodynamic technologies will play a key role in improving the next generation aircrafts’ performance and contribute strongly to the product cost and operability. Active flow control (AFC) is one of the promising technologies to control boundary-layer separation, mixing, and noise. In recent years, actuators for active flow control and especially fluidic oscillators received a lot of interest from the community. State of the art in improving aerodynamic performance 1, AFC actuators2 and fluidic oscillators3 are provided from excellently reviewed papers. Fluidic actuators increase momentum in the local flow field by fluid injection or suction. Sweeping jet actuators (SJA) belong to this category of actuators and are based on fluidic oscillators with no moving parts3 as shown in Figure 1. A sweeping jet actuator emits a continuous but spatially oscillating jet when pressurized with a fluid 4. The oscillations are entirely self-induced and self-sustaining. As the supplied fluid passes through the SJA, the jet attaches itself to either side due to the Coanda effect. Then pressure increases in the feedback loop and pushes the jet to the other side. This process repeats in a cyclic fashion. An oscillating jet is thus obtained that sweeps from one side of the exit nozzle to the other1. Earlier designs of sweeping jet actuators have been utilized with liquids as working fluids for applications, such as windshield washers, sprinklers and Jacuzzis6, 7. Recently, an increasing number of studies were published, especially in flow separation control. Significant aerodynamic performance improvements were achieved using new concepts8, 9 or sweeping jets10-19. The sweeping jet actuator has been proven to be an effective and efficient tool for separation control. Nonetheless, a lack of knowledge continues regarding the actuator’s properties, underlying mechanisms, and governing parameters for flow control applications4. Further development of sweeping jet actuators is needed before their deployment into actual applications. 2 Figure 1. Schematic of a sweeping jet fluidic actuator 1

Assistant Prof., Aerospace Engineering Department, Khalifa University, H335E, Abu Dhabi, United Arab Emirates, 127788, Senior Member, AIAA. 1 American Institute of Aeronautics and Astronautics Copyright © 2015 by Kursat Kara. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission.


II. Motivation and Objectives Sweeping jet actuators (SJA) have been shown to be an effective and efficient tool for separation control, but further development is needed before their deployment into actual applications3. First of all, there is no established design methodology on hand for the design of a sweeping jet actuator. The performance of SJA depends on many parameters such as, flow rate, size of feedback channel, design of Coanda surfaces, exit nozzle angle, etc. In addition, there is no systematic study available in the literature on the effect of feedback channel geometry to sweeping frequency. Furthermore, the scaling rules of these actuators are yet to be fully understood. Moreover, there are no measurements available on the pressure drop in the device as a function of air supply pressure. The external flow field properties of a fluidic oscillator were investigated before10-20. However, the internal flow field of such devices has only been examined by two experimental4, 5 and four numerical21-24 studies. The fluidic actuators investigated in these studies vary by type, geometry, working fluid, etc. So far, there is just one numerical study24, which employs the Lattice-Boltzman based PowerFLOW solver, available in the literature for the interior flow field of a sweeping jet actuator. The main objective of this paper is to understand internal flow physics, jet oscillation process, and pressure drop mechanism using a time-dependent numerical analysis. This understanding will help in the development of design methodologies for the sweeping jet with minimum pressure losses, controllable sweeping frequency, and a more efficient flow control actuator. This paper includes a systematic mesh study, steady-state and time-dependent velocity, temperature, and total pressure plots. Internal oscillatory motion and external jet oscillation frequency are successfully predicted and compared with existing experimental and numerical data. The oscillator geometry in earlier numerical21-24 and experimental4, 24 studies in the literature are intentionally distorted or blocked due to the proprietary nature of the design. Therefore, on another path, a well defined experimental investigation is planned for validation of numerical simulation. The sweeping jet geometry with an aspect ration equal to one at the exit nozzle throat is created using a 3D Connex printer with 600 gr of VeroBlue as shown in Figure 2. Sharpe Corner (creates vortex)

Feedback Channel (upper) a) Exit Nozzle Throat

Supply Flow Direction Actuator Core

y

x b) Figure 2. Sweeping jet printed using a 3D Connex printer. Exit nozzle throat width=6.35mm, depth=6.35mm

2 American Institute of Aeronautics and Astronautics

III. Numerical Simulation Setup


A standard commercial CFD software Ansys Fluent 16 is employed for the numerical investigation. The computational domain is two-dimensional and the oscillating jet comes out of a single exit nozzle, located at the left side of the origin, as illustrated in Figures 2 and 3. Here the x-axis aligns with the primary jet flow direction; whereas the y-axis refers to the spanwise direction. Vatsa, et. al24 numerically and experimentally examined the characteristics of three different fluidic actuators for active flow control applications. A Type II curved actuator (in the current paper, it is referred as Sweeping Jet Actuator) with an exit nozzle throat width of 0.25 inches (6.35 mm) was tested, and then hot-wire measured time history of the velocity magnitude at points (6 mm, 0 mm) and (6 mm, 10 mm) were reported. To use the hot wire data for verification and validation purposes, and to tune up the computational model, the same problem was adopted here. However, the geometric description of the sweeping jet actuator was not adequate for reproduction. Therefore, the same actuator geometry is reproduced from Figure 1 of Raman et al25, where the exit throat width was 0.14 cm, using a CAD software SolidWorks. Later, the geometry is scaled up without changing the aspect ratio to have an exit nozzle throat height of h=6.35mm. Wall

(6,10)

pressure outlet

Wall

(6,0)

inlet x=24 x=12 x=3 x=6 x=18

Figure 3 Computational domain and boundary conditions. Table 1 Boundary contions P T h d AR hi Ai

Pa

101325

ambient static pressure

K mm mm

298.16 6.35 6.35 1 16.21 102.934

ambient static temperature exit nozzle throat height depth Aspect Ratio inlet channel height inlet cross section area

𝑚̇

lb/s kg/(m2 s) m/s

mass flux Vi Mi P0i T0i

mm mm2

Pa K

0.010 44.067 37.2 0.108 102147.5 298.8

0.015 66.100 55.8 0.161 103182.3 299.7

0.020 88.133 74.4 0.215 104643.5 300.9

mass flow rate mass flux inlet velocity inlet Mach number inlet total pressure inlet total temperature



A semi-circular domain with a radius of 100h (635 mm) is added to the nozzle exit plane. Furthermore, a twodimensional straight channel with a height of 16.21 mm is added to the actuator inlet to have a fully developed flow at the upstream of converging nozzle part. Vatsa, et. al24 tested three different mass flow rates (0.010, 0.015, and 0.020 lb/s) in their three-dimensional study, where the aspect ratio of the nozzle exit throat was equal to one. However, the inlet flow conditions were not reported. To match the inlet flow conditions with the experimental setup 24, inlet static temperature of 25 degC is taken from their simulation24, and inlet static pressure of 101325 Pa is assumed. The depth of the geometry set to 6.35 mm. Using a flow calculator, mass flux, total pressure and total temperature are calculated as shown in Table 1. Boundary conditions for all the solid surfaces (sweeping jet actuator, inlet channel and nozzle exit plane) are defined as no-slip, no-penetration for velocity, and isothermal wall at the temperature of 298.16 K. Figure 3 shows mass flow inlet (implemented using mass flux), pressure outlet and wall boundaries, in addition to sampling points and lines for velocity profile plots. The pressure outlet is assumed to open to the ambient environment at p=101325 Pa and T=298.16 K.

Figure 4 Computational mesh around the sweewing jet actuator.

Figure 5 Computational mesh around the sweewing jet actuator and close-up view of exit nozzle. Computational mesh is created using Ansys Meshing tool and the sensitivity of calculations with respect to grid size is checked to ascertain sufficient resolution and mesh quality metrics. As shown in Figures 4 and 5, three levels of element sizing are applied using the sphere of influence method with sphere radii of 20, 80, and 320 mm and corresponding element sizes of 0.05, 0.20, and 0.80 mm, respectively. Additionally, the boundary-layer grid is created 4 American Institute of Aeronautics and Astronautics

using the inflation method with 11 layers and a growth rate of 1.15. The first point of the wall is placed at 0.01 mm in a wall normal direction. For all the wall regions, y+ values are in the range of 1 and 5 during time dependent simulations. The mesh consists of Quad4 and Tri3 elements, and contains a mixture of structured and unstructured 1,113,193 cells (2,275,219 interior faces, 4,100 wall faces, 878 pressure-outlet faces, and 42 mass-flow-inlet faces). The mesh has an average orthogonal quality of 0.9905 with a standard deviation of 0.024. Mesh adaptation is not used.


IV. Results Simulations are done for a fully-turbulent, compressible flow. Pressure based, coupled, steady-state, and time dependent (transient) solvers with a constant time step of t=10-5 seconds are employed. Vatsa, et. al24 reported that jet oscillation frequency was 306.42 Hz for mass flow rate of 0.015 lb/s. In this case, using a time step t=10-5 seconds, one period of jet oscillation will be simulated approximately at 326 time steps. Time accurate simulations are performed using the bounded second order implicit scheme. The second-order discretization for pressure and the second-order-upwind formulation for density, momentum, turbulent kinetic energy, specific dissipation rate and energy are adopted. Time accurate simulations are started using the converged steady-state results. Following the approach of earlier numerical studies21-23, numerical parameters are tuned with steady-state calculations to determine the most appropriate turbulence model for the current problem. Realizable k- and SST k- models both with and without wall functions are considered. Since SST k-offers more accurate treatment of the near-wall region and is reliable for flows with adverse pressure gradient, it is used in time-accurate simulations. The computations run on a Dell Precision T7610 workstation with Dual Intel Xeon CPU (E5-2967 v2 at 2.70GHz, 24 cores, hyper-treading disabled) and 192 GB ram running with 64 bit Windows 7. Ansys Fluent is set to parallel calculation mode with 12 processes. Time-accurate calculations are run for a large number of time steps (20,000) to generate a database from which statistically converged flow quantities could be analyzed. A. Comparison of Turbulence Models Today, many turbulence models are available ranging from one equation models to Large Eddy Simulation in the Fluent 16 fluid flow solver. Computational cost per iteration increases with the level of complexity of the turbulence model, especially in time-accurate solutions. Therefore, determining the most appropriate turbulence model for any given problem is important. Reynolds Averaged Navier-Stokes (RANS) simulation solves time-averaged NavierStokes equations, and all the turbulent motions are modeled with many different choices. Using an earlier version of the same flow solver, Gokoglu, et. al21 systematically compared RANS based turbulence models, including 1) k- with standard wall function, 2) k- with enhanced wall functions, and 3) k- with shear stress transport (SST) in steady state and time-accurate simulations. They showed that the SST k- turbulence model offers a more accurate treatment of near-wall region and is reliable for flows with adverse pressure gradient. The same turbulence model is also employed in other studies22, 23 of the same group. Furthermore, the Fluent solver documentation26 recommends Realizable k- or SST k- for standard cases. Here, Realizable k- or SST k- turbulence models are applied with steady-state and time-accurate simulations for 𝑚̇=0.015 lb/s case. Similar to the findings of Gokoglu, et. al21, the steady-state simulations do not capture the jet oscillation as shown in Figure 6. Symmetric flow indicates that the level of accuracy is sufficient and the amplitudes of perturbations are not large. Figure 7 (a) compares the decay of velocity magnitude along the x-axis (defined in Figure 3) until x=24mm. Velocity magnitude for the Realizable k- model stays constant up to x=7 mm and then decays linearly in the streamwise directon; while for the SST k- model, the velocity magnitude decays linearly from x=0. However, the difference between the velocity after x=6 mm is around 1%. Figure 7 (b) compares the Temperature profiles at x=24 mm and shows the effect of high-speed cooling that is larger in magnitude for Realizable k-; however, it happens in a narrower region. Figure 8 compares time-averaged velocity magnitudes. The Realizable k- model estimates a larger jet velocity in the feedback channels and in the actuator core.Time-averaged velocity along the symmetry line (x-axis) and at x=24 mm are compared in Figure 9. Velocity magnitudes from steady-state simulation (Figure 7a) and time-accurate simulation (Figure 9a) are completely different due to an oscillating jet in the latter case. The difference between the velocity profiles for the two turbulence models using the same flow conditions and the grid is clearly shown in Figure 9 (b) where the SST k- model predicts the jet velocity profile noticeably. In a systematical mesh study, SST k- model predictions are checked by varying the mesh density, changing boundary-layer grid parameters (max number of layers, growth rate, the first layer height), the number of cells put across the smallest segment in the domain (n=10, 20, 30, 40), and the element sizing in circular regions as shown in Figure 5. In steady-state simulations, the mesh density affected convergence and overall flow field, including the flow symmetry. The jet flow oscillates for 2nd-order steady-state calculations while using coarser meshes. During simulations, mesh adaptation is not employed.



a)

b) Figure 6 Steady-state velocity magnitude, 𝒎̇ = 𝟎. 𝟎𝟏𝟓 lb/s, a) SST k-, b) Realizable k-

a) b) Figure 7 𝒎̇ = 𝟎. 𝟎𝟏𝟓 lb/s, a) Decay of velocity along the centerline, b) Temperature profile at x=24mm.

a)

b) Figure 8 Time-averaged velocity magnitude, 𝒎̇ = 𝟎. 𝟎𝟏𝟓 lb/s, a) SST k-, b) Realizable k-

Figure 9 𝒎̇ = 𝟎. 𝟎𝟏𝟓 lb/s, a) Decay of time-averaged velocity along the centerline, b) time-averaged velocity profile at x=24mm. (Red line - SST k-, Blue line - Realizable k- 6 American Institute of Aeronautics and Astronautics


B. Comparison of Mass Flow Rate The sweeping jet actuator produces a spanwise oscillating jet; sweeping extent and oscillation frequency depend on the geometric parameters and mass flow rate entering through the inlet. To shed light on the complex flow physics inside the sweeping jet actuator, steady-state and time-accurate flow simulations are performed using three different mass flow rates. Simulation setup and computational mesh are explained in detail in Section III. Figures 10 and 11 compare the steady-state velocity magnitude for the various mass flow rate. As expected, increasing mass flow rate increases jet velocity. For all off these steady-state simulations, symmetry of the flow field is not broken as shown in Figure 10. This indicates that perturbations do not gain amplitude with increasing velocities in steady-state simulation. Figure 11 shows the static temperature contour, which is also symmetric, and high speed cooling is clear. All the steady-state cases are converged, and they are used as initial conditions for time-accurate simulations. Time-accurate calculations are run using the same time step (t=10-5 s) for a large number of time steps (20,000) so as to generate a database from which statistically converged flow quantities could be analyzed. For each case, time variation of velocity magnitudes at sampling points (6mm, 0mm) and (6mm, 10mm) (locations are shown in Figure 3) are recorded for further analysis. Figure 13 shows the velocity magnitude at (6,0) between 0.10-0.12 seconds and a distinct pattern for velocity magnitudes are clear. In Figure 13 (a), we can see multiple low and high-velocity regions following each other and resembling a square wave. Similar wave structures can be seen in Figure 13 (b) and (c); however, clearly increasing mass flow rate increased the peak velocity and decreased the period. Figure 14 and 15 show the fast Fouries transform (FFT) of the velocity magnitudes recorded over time at least ten periods for points (6,0) and (6,10), respectively. Figure 14 (a) shows the peak frequency of 251.9 Hz at the point (6,0) for a mass flow rate of 0.010 lb/s. However, higher harmonics with high amplitudes are also visible in the spectrum.

a) b) c) Figure 10 Velocity magnitude, SST k-, a) 𝒎̇ = 𝟎. 𝟎𝟏𝟎 lb/s, b) 𝒎̇ = 𝟎. 𝟎𝟏𝟓 lb/s, c) 𝒎̇ = 𝟎. 𝟎𝟐𝟎 lb/s

a)

b) Figure 11 a) Decay of velocity along the centerline, b) Velocity profile at x=24mm. (Red line - 𝒎̇ = 𝟎. 𝟎𝟏𝟎 lb/s, Blue line - 𝒎̇ = 𝟎. 𝟎𝟏𝟓 lb/s, Green line - 𝒎̇ = 𝟎. 𝟎𝟐𝟎 lb/s)

a) b) c) Figure 12 Static Temperature contour, SST k-, a) 𝒎̇ = 𝟎. 𝟎𝟏𝟎 lb/s, b) 𝒎̇ = 𝟎. 𝟎𝟏𝟓 lb/s, c) 𝒎̇ = 𝟎. 𝟎𝟐𝟎 lb/s



a)

b)

c) Figure 13 Time variation of velocity magnitude at sampling point (6,0), SST k-, a) 𝒎̇ = 𝟎. 𝟎𝟏𝟎 lb/s, b) 𝒎̇ = 𝟎. 𝟎𝟏𝟓 lb/s, c) 𝒎̇ = 𝟎. 𝟎𝟐𝟎 lb/s



a)

b)

c) Figure 14 FFT analysis of velocity magnitude at the sampling point (6,0), SST k-, a) 𝒎̇ = 𝟎. 𝟎𝟏𝟎 lb/s, b) 𝒎̇ = 𝟎. 𝟎𝟏𝟓 lb/s, c) 𝒎̇ = 𝟎. 𝟎𝟐𝟎 lb/s



a)

b)

c) Figure 15 FFT analysis of velocity magnitude at the sampling point (6,10), SST k-, a) 𝒎̇ = 𝟎. 𝟎𝟏𝟎 lb/s, b) 𝒎̇ = 𝟎. 𝟎𝟏𝟓 lb/s, c) 𝒎̇ = 𝟎. 𝟎𝟐𝟎 lb/s



a)

b) Figure 16 Comparison of jet oscillation frequency variation with mass flow rate. Figure 14 shows that at the point (6,0), the peak frequencies are 251.9, 354.6, and 390 Hz corresponding to mass flow rate of 0.010, 0.015, and 0.020 lb/s, respectively. Figure 15 shows the FFT analysis of the velocity magnitude at the point (6,10). The peak frequency has the same value at both points for mass flow rate of 0.010 and 0.020 lb/s; however, for 0.015 lb/s, it is slightly reduced to 345.7 Hz. Furthermore, in Figure 15 (c), a sub harmonic at 195 Hz is the gained amplitude. The primary frequency of oscillations at the point (6, 10) obtained from numerical simulations in Section IV-B is compared with the experimental data and simulation of Vatsa, et. al24. As expected, the oscillation frequency increases with the mass flow rate due to increase in flow velocities. The predicted frequencies (circle) are higher than the experiment24 (square) for all mass flow rates. There is approximately a 20% error at 0.010 lb/s, and the error decreases with increasing mass flow rate. Another look at Figure 16 (a), from a different perspective, by changing ±20% error with ±40 Hz, reveals that all of the predicted frequencies are almost 40 Hz more than the experimental data, as shown in Figure 16 (b). On the other hand, the difference in frequency between the experimental data24 and simulations24 is increasing with the mass flow rate while the difference is almost constant for the simulations in Section IV-B of this paper. As mentioned at the beginning of this section, jet oscillation frequency depends on the geometric parameters and mass flow rate entering through the inlet. The geometry used in the experiment 24 has not been available to the author, until now, and it was reproduced from Figure 1 of Raman et. al25. However, slight differences in geometry might have caused the frequency mismatch. In addition, the experiment24 only reports the mass flow rate of the inlet, which by itself is not enough to define the inlet boundary condition in a numerical simulation. Therefore, additional assumptions about inlet flow conditions are made. These assumptions might affect the simulation results. To find the effect of these possible sources of errors, an additional experiment is planned.



a)

b)

c) Figure 17 Flow field inside the sweeping jet actuator for half an oscillation cycle (a-c); left snapshots of velocity magnitude; right snapshots of total pressure. Both of the contors are superimposed with velocity vectors.



a)

b)

c) Figure 18 Flow field inside the sweeping jet actuator for half an oscillation cycle (a-c); left snapshots of velocity magnitude; right snapshots of total pressure. Both of the contors are superimposed with velocity vectors. 13 American Institute of Aeronautics and Astronautics


a)

b)

c) Figure 19 Flow field inside the sweeping jet actuator for half an oscillation cycle (a-c); left snapshots of velocity magnitude; right snapshots of total pressure. Both of the contors are superimposed with velocity vectors. 14 American Institute of Aeronautics and Astronautics


a)

b)

c) Figure 20 Flow field inside the sweeping jet actuator for half an oscillation cycle (a-c); left snapshots of velocity magnitude; right snapshots of total pressure. Both of the contors are superimposed with velocity vectors.



Figures 17 to 20 show time-accurate flow field snapshots inside the sweeping jet actuator for half of an oscillation cycle in a consecutive way from 137.31 ms to 138.51 ms for mass flow rate of 0.015 lb/s. Velocity magnitude contours are shown on the left columns and total pressure contours are shown at the right. To show the flow direction, both contour plots are superimposed with velocity vectors in the form of fixed size arrows. Contour legends are placed at the top of each column for Figures 17-20 to save space. Understanding and minimizing the pressure losses for flow control applications is critical to improve actuator performance. Therefore, total pressure contour is given next to velocity contour for the same time step. Figure 17 (a) shows that the jet is attached to the lower surface of actuator core area due to Coanda effect, goes around a large vortex, and leaves the actuator through the exit nozzle throat. The total pressure contour on the right, clearly shows a high total pressure area as in an imprint of the jet. The leaks of the total pressure to feedback channels are minimal at this instant. In the upper feedback channel, counter rotating vortices block the channel and increases the path in the feedback flow. In the lower feedback channel, two vortices formed at the right and the left end of the channel, streamlining the channel for incoming flow from the right end. In Figure 17 (c), a small jet branches out to right entrance of the lower feedback channel, which in turn pushes the main jet entering the actuator towards the upper surface of the actuator core. In the upper feedback channel, counter rotating vortices block the channel and the amount of flow going around the vortices is minimal at this stage. In Figure 18 (a), the main jet entering the actuator core area is almost parallel to the x-axis. The main jet pushes the main vortex in the actuator core, goes under and around the vortex, a smaller part of it branches out to lower feedback channel with increasing velocity, and the larger part moves to the exit nozzle at a higher velocity. At the same time, another vortex starts forming on the lower surface of the actuator core near the sharp corner. In Figure 18 (c), the first vortex is pushed to upper-right hand corner, and the newly formed small vortex grows on the lower surface of the actuator core and starts pushing the main jet upward. At that instant, with the increase of pressure in the lower feedback channel, the main jet is pushed more strongly towards the upper surface of the actuator core. At this moment, the four counter rotating vortices in the upper feedback channel keep their position. The main jet is continuously pushed upward by the flow from feedback channel and the newly formed vortex until the main jet completely flip and attach the upper surface of the actuator core, as shown in Figure 20.

V. Conclusions Time-dependent numerical analysis of a sweeping jet actuator using Ansys Fluent 16 is performed to develop an understanding of internal flow oscillations. To improve the performance of the sweeping jet actuator for flow control applications, understanding the oscillation mechanisim and minimizing the pressure losses are critical. During the shifting of the main jet from the lower to upper surface of the actuator core, the main jet pushes the large vortex in the actuator core towards the exit nozzle, and goes under and around of the vortex. Later, a smaller part of the main jet branches out to lower feedback channel with increasing velocity while the remaining part moves to the exit nozzle at high velocity. The flow inside the lower feedback channel pushes the main jet upward around the left end of the channel. Around the sharp edge, another vortex forms on the lower surface of the actuator core and grows in size, pushes the jet upward, and moves downstream. A similar vortex formation was observed in a recent experiment of Bobusch, et. al4. A detailed investigation of internal flow structures reveals the contribution of vortexes created at the sharp edge to the jet oscillation mechanism. The effect of varying inlet mass flow rate on the sweeping jet oscillation frequency is calculated and a strong agreement found within the experimental data24. Time-averaged velocity profiles at x=6 mm in experimental24 and three-dimensional computational work24 have two peaks. However, in the current two-dimensional simulations, only one velocity peak is observed. Furthermore, spreading of the jet after leaving the exit nozzle throat is much less than in previous results24. Sweeping jet oscillation frequency depends on the geometric parameters and mass flow rate entering through the inlet. The geometry used in the experiment24 has not been available to the author, until now, and it was reproduced from another source25. However slight differences in geometry might have caused the frequency mismatch. Furthermore, the inlet conditions in the experiment24 only reported the mass flow rate, which by itself is not enough to define the inlet boundary condition in a numerical simulation. Therefore, additional assumptions about inlet flow conditions are made. These assumptions might have an effect upon the simulation results. To find the effect of these possible sources of errors, a similar experiment to Vatsa, et. al24 is planned. Future work will focus on a validation study between a well-defined experiment and a three-dimensional simulation. After successful validation, an effort will be made to investigate the minimization of pressure losses via geometric modifications.


Acknowledgements This work is supported by Khalifa University. The author would like to thank Dr. Mehti Koklu of NASA Langley Research Center for discussions about experimental setup and boundary conditions.

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