Numerical Study of Applications of Active Flow Control for Drag Reduction

Transcription

1 Numerical Study of Applications of Active Flow Control for Drag Reduction RAMESH K. AGARWAL Department of Mechanical Engineering and Materials Science Washington University in St. Louis 1 Brookings Drive, St. Louis, MO USA rka@wustl.edu Abstract: - This paper describes the application of active flow control (AFC) devices such as synthetic jets and pulsed jets for reducing the drag of air and ground vehicles. In particular, the transonic drag reduction of an airfoil, and drag reduction of a truck shaped body is considered by using synthetic jet actuators. The flow fields without and with AFC are calculated using a Computational Fluid Dynamics (CFD) flow solver which solves the Unsteady -Averaged Navier-Stokes (URANS) equations in conjunction with a two-equation turbulence model. The numerical results show that it is possible to achieve significant reduction in drag of the vehicle by suitably deploying the AFC technology. Key-Words: - Active Flow Control, Synthetic Jet Actuators, Drag Reduction 1 Introduction In recent years, there has been great emphasis on the development of advanced aerodynamic technologies, based on fluidic modification of aerodynamic flow fields/forces that can cover multiple flight conditions without the need of conventional control surfaces such as flaps, spoilers and variable wing sweep. The fluidic modification (or flow control) can be accomplished by employing micro-surface effectors and fluidic devices/actuators dynamically operated by an intelligent control system. These new active flow control (AFC) technologies have the potential of resulting in radical improvement in aircraft performance and weight reduction. A variety of impressive flow control results have been achieved experimentally by many researchers using pulsed or synthetic (oscillatory) jet actuators including the vectoring of conventional propulsive jets, modification of aerodynamic characteristics of bluff bodies, control of lift and drag of airfoils, thrust augmentation in ejectors, reduction of skin-friction in a boundary layer flow, enhanced mixing in circular jets, control of external as well as internal flow separation and of cavity oscillations. A synthetic jet is formed by employing an oscillatory surface within a cavity. It is created entirely from the fluid that is being controlled. It is generated with a piezoelectric diaphragm in a periodic manner. Flow enters and exits the cavity through an orifice in a periodic manner. The unique feature of the synthetic jet is that no fluid ducting is required. Synthetic jets have been shown to exert significant control authority in many applications and have the additional benefit of being compact with zero net mass flux (ZNMF). An excellent review of synthetic jets and some of their applications has been given by Glezer and Amitay [1]. A pulsed jet, on the other hand, has both a steady and an oscillatory velocity component, with net mass flow and momentum injected into the flow. In a previous review paper, Agarwal et al. [2] presented some representative results of numerical simulations on flow control using synthetic or pulsed jets for four different flow fields dealing with thrustvectoring of a propulsive jet, control of separation on a backward facing step, control of cavity oscillations, and thrust augmentation of an ejector. These simulations were performed using the URANS equations in conjunction with either one- or a two-equation turbulence models. The numerical simulations compared well with the available experimental data. 2 CFD Solver Employed For the AFC computations reported in this paper the commercial CFD solver FLUENT 6.0 [3] is employed. FLUENT is a very versatile CFD solver with a number of numerical algorithms and turbulence models for solving the incompressible or compressible Unsteady -Averaged Navier- Stokes (URANS) equations. It is also supported by ISBN:

2 the geometry modeling and mesh generation software GAMBIT [4] which can generate structured, unstructured, and hybrid adaptive meshes. In all the simulations reported in this paper, the boundary conditions for the actuator are applied at the exit of the jet; the influence of the cavity of the actuator is not included. The computations from FLUENT were further validated by computing the nominally 2-D and 3-D Active Flow Control (AFC) flow fields for which the experimental data is available. Honohan s 2-D experimental data on the interaction of a synthetic jet with a flat plate boundary layer [5] and NASA Langley s experimental data for three 3-D AFC flow fields [6] were used in validation in addition to the cases reported in Reference [2]. For all the computations reported in this paper, the grid refinement study was performed to ensure that the computed solutions were grid-independent. 3 Results 3.1 Active Flow Control for Transonic Drag Reduction of an Airfoil The objective of this calculation is to evaluate, via numerical simulation, the feasibility of weakening the shock wave(s) and reducing the pockets of supersonic regions on wings and rotor blades in high speed (transonic) flight using active flow control (AFC) devices such as synthetic jet actuators (SJA). In fixed wing application, the weakening of shock and control of shock/boundary-layer (SBL) interaction can lead to reduced drag with minimal change in lift while for rotor blades it can result in both the reduction of drag and high speed impulsive noise (HSI). For subsonic flow past airfoils at low angles of attack, recently it was shown experimentally by Amitay et al [7] and computationally by Vadillo et al [8] that the pressure drag of an airfoil can be significantly reduced with a minimum change in lift by modification of the apparent aerodynamic shape of the airfoil. This virtual aerodynamic shape modification can be achieved by creating a small domain adjacent to the upper airfoil surface (downstream of the leading edge) using a synthetic jet actuator which displaces the local streamlines sufficiently to modify the local pressure distribution. These results were very encouraging. However, although some computational and experimental work has been done in the application of active flow control for virtual aeroshaping of airfoil to reduce drag in subsonic flow, limited work has been reported in the literature for active control of the strength of shock waves and their unsteadiness, the supersonic regions, and shock-induced separation due to shock/boundary layer interaction. This problem is of great relevance for reducing the transonic cruise drag of a transport aircraft and high speed impulsive noise of a rotorcraft. To explore this possibility, Vadillo et al recently conducted a numerical study [9] which showed the potential for modulation of aerodynamic forces and moments in transonic flow past an airfoil using a synthetic jet actuator. However in the study reported in [9], the goal of reduction of transonic drag with minimum change in lift was not achieved. It was recognized that the multiple actuators suitably placed on the surface of the airfoil will be needed to achieve this objective. The goal of this paper is to conduct a numerical study with multiple actuators suitably placed on the surface of the airfoil. In order to determine the optimal placement of actuators, a genetic algorithm is employed. The present study and the experiments conducted at Boeing have demonstrated the potential for reducing the transonic drag with minimum change in lift (or even enhancing the lift) by optimally placing the three synthetic jet actuators on the upper surface of the airfoil. Computations were performed for flow past a NACA 64A010 airfoil in transonic flow at Mach 0.8 and 0.9, angle of attack = 2.8, chord number Re = , with and without AFC using three synthetic jets on the upper surface of the airfoil; experiments for this configuration were performed at Boeing. The three SJAs were installed on the upper surface of the airfoil at x/c = 0.1, 0.53 and 0.68 determined by an optimization study using a genetic algorithm. The three jets were identical in width b/c = , frequency f = 220 Hz and velocity amplitude Mj = 0.3 and operated in the same phase. Figures 1-4 and 5-8 respectively show for free stream Mach of 0.8 and 0.9, (a) the grid, (b) the pressure distribution without AFC, (c) the lift coefficient without and with AFC, and (d) the pressure drag coefficient without and with AFC. These computations employed the hybrid URANS/LES model. It should be noted that the hybrid model is as accurate as the LES but is an order of magnitude more efficient. These figures demonstrate the effectiveness of AFC using suitably placed multiple SJs, in both reducing the drag and enhancing the lift. Tables 1and 2 show the reduction in drag and increase in lift at Mach 0.8 and 0.9 using AFC. These results are very close to the experimental values obtained in the experiments performed at Boeing. These calculations, for the ISBN:

3 first time, demonstrate the potential of AFC in transonic drag reduction. Fig. 1: Grid used for M =0.8 Fig. 5: Grid used for M =0.9 Fig. 2: C p at M =0.8 Fig. 6: C p at M =0.9 Fig. 3: C lp with and without AFC at M =0.8 Fig. 7: C lp with and without AFC at M =0.9 Fig. 4: C dp with and without AFC at M =0.8 Fig. 8: C dp with and without AFC at M =0.9 ISBN:

4 Table 1: Comparison of the computed lift and drag coefficients for the flow past a NACA64A010 airfoil at M = 0.8, Re c = 5.67e+6 and α = 2.8 o Table 2: Comparison of the computed lift and drag coefficients for the flow past a NACA64A010 airfoil at M = 0.9, Re c = 5.67e+6 and α = 2.8 o numbers and comparison of results with the experimental data [14]. Here we report the results of AFC simulation for case (a). The results for cases (b) and (c) are very similar. We consider the flow past a generic truck model used in the experiments of Seifert et al. [12]. The geometry of the model is shown in Figure 9. Figure 10 shows the final adapted structured mesh. 3.2 Active Flow Control for Drag Reduction of a Truck Shaped Body In U.S., the ground vehicles consume about 77% of all (domestic and imported) petroleum; 34% is consumed by automobiles, 25% by light trucks and 18% by large heavy duty trucks and trailers. It has been estimated that 1% increase in fuel economy can save 245 million gallons of fuel/year. Additionally, the fuel consumption by ground vehicles accounts for over 30% of CO 2 and other greenhouse gas (GHG) emissions. Most of the usable energy from the engine goes into overcoming the aerodynamic drag (53%) and rolling resistance (32%); only 9% is required for auxiliary equipment and 6% is used by the drive-train [10]. Therefore, 15% reduction in aerodynamic drag at highway speed of 55mph can result in about 5-7% in fuel saving. The goal of this work is to explore the potential of active flow control (AFC) technology to reduce the aerodynamic drag of ground vehicles by 15-20%. The successful demonstration of AFC technology in reducing aerodynamic drag technology will pave the way for both retrofitting and deployment on new vehicles that will result in significant impact in reducing the fuel consumption (at least by 5%) and the GHG emissions worldwide. We have performed the CFD simulations on three generic truck configurations to demonstrate the feasibility of using AFC with oscillatory jet actuators for reducing the aerodynamic drag of vehicles [11]. These cases are: (a) 2D numerical simulations of the steady and AFC flow fields of a model 2D truck configuration at various numbers and comparison of results with the experimental data of Seifert et al. [12], (b) 2D numerical simulations of the steady and AFC flow fields of a model D-shaped body at various numbers and comparison of results with the experimental data of Pastoor et al. [13], and (c) 3D numerical simulations of the steady and AFC flow fields of Ahmed body at various Fig. 9: Generic truck model Fig. 10: Solution adaptive structured mesh Computations were performed at free-stream velocities of 10 m/s, 20 m/s, and 30 m/s (22 mph, 44 mph, and 66 mph) used in the experiments of Seifert et al. [12].The optimal adaptive mesh shown in Figure 10 with a second-order CFD solver and realizable k-ε model gave the best results, within ±5% of the experimental values at all the three numbers (3.08x10 5, 6.1x10 5 and 9.24x10 5 corresponding to free-stream velocities of 10 m/s, 20m/s and 30 m/s respectively). Figure 11 shows the comparison between the experimental and computed values of drag coefficient for the three numbers. The error bars on the graph are within the ±5% range. It is important to note that the computed drag coefficient decreases as the number increases as expected from physical considerations; however this trend is not observed in the experimental data and has not been possible to diagnose. Figure 12 shows the three AFC configurations employed in the study for the three numbers. The synthetic or oscillatory jet is defined by Vjet = V 0 sin (2π f t) with f = 100 Hz and V 0 = 0.5 x free-stream velocity. The jet width is 1.7 ISBN:

5 mm. Figure 13 shows the static pressure behind the rear face of the truck for configurations (a), (b) and (c) at Re = 3.08 x It is clear that the use of AFC increases the pressure considerably compared to that without AFC. This increase in pressure is responsible for 15 to 20% reduction in drag. Figures 14 and 15 show the velocity contours in the flow field behind the truck. (a) (b) (c) Fig. 13: Cp for the generic truck model at Re = 3.08 x10 5, Baseline (Blue) versus AFC (Red) [for cases (a), (b), (c) of Figure 12] Fig. 11: Variation of drag coefficient with number; Blue: Experiment, Red: Computation Fig. 14: Velocity contours without AFC for Re = 3.08 x 10 5 Fig. 12: Three AFC configurations studied Without AFC, the asymmetric Karman vortices are shed as expected (see Figure 14). With AFC (see case (a) of Figure 12), the flow becomes almost symmetric in the near wake region which also extends further from the rear end of the body resulting in increased pressure on the rear face (see Figure 15). This mechanism is also seen in other simulations at the other two numbers and for cases (b) and (c) of Figure 12 [11]. Figure 16 shows the reduction in drag with number using three different configurations of AFC in Figure 12, compared to baseline configuration without AFC; it shows that 13-15% reduction in drag can be achieved at higher numbers (close to highway speed of 55mph). Table 3 shows the comparison between computed and experimental drag coefficient Cd at different numbers for steady flow without AFC. Good agreement is obtained. Table 4 shows the reduction in computed drag for three different configurations of AFC (see Figure 12) compared to baseline configuration. Additional details for this case are given in Reference 11. Fig 15::Velocity contours with AFC for case (a) of Figure 12 at Re=3.08 x 10 5 Fig.16: Variation of drag coefficient with number without and with AFC (for cases (a) - (c) of Figure 12) ISBN:

6 It should be noted that in the computations, f = 100 Hz was chosen which is the same as in the experiments of Seifert et al. [12]. The operating range of most of the robust actuators is between 250 to 500 Hz. Low frequency is less desirable because it can result in a very noisy actuator. Table 3: Computed and experimental drag coefficients for steady flow without AFC Experimental Cd Baseline Cd 3.08 x x x Table 4: Computed values of drag coefficients for flow with AFC for three configurations of Fig Back SJA*s % Change in Cd compared to baseline 3.08E E E Back SJAs, 90 Top SJA % Change in Cd compared to baseline 3.08E E E Back SJAs % Change in Cd compared to baseline 3.08E E E * SJA = Synthetic Jet Actuator 4 Conclusion The numerical studies presented in this paper demonstrate that significant reduction in drag of both air and ground vehicles can be achieved by careful implementation of active flow control technology using ZNMF synthetic jet actuators. References: [1] A. Glezer and M. Amitay, Synthetic Jets, Annual Rev. Fluid Mech., Vol. XXXIV, 2002, pp [2] R. K. Agarwal, J. Vadillo, Y.Tan, J. Cui, D. Guo, H. Jain, A.W. Cary and W.W. Bower, Flow Control with Synthetic and Pulsed Jets: Applications to Virtual Aeroshaping, Thrust-Vectoring, and Control of Separation and Cavity Oscillations, in Frontiers of Computational Fluid Dynamics, Chapter 11, D.A. Caughey and M. Hafez Editors, World Scientific, NJ, [3] FLUENT 6.0, ANSYS Inc., [4] GAMBIT, ANSYS Inc., [5]A.M. Honohan, The Interaction of Synthetic Jets with Cross Flow and the Modification of Aerodynamic Surfaces, PhD Thesis, Georgia Institute of Technology, May [6] T. Gatski and C. Rumsey, CFD Validation of Synthetic Jets and Turbulent Separation Control, NASA Langley Research Centre Workshop, March 2004 ( [7] M. Amitay, M. Horvath, M. Michaux and A. Glezer, Virtual Aerodynamic Shape Modification at Low Angles of Attack Using Synthetic Jet Actuators, AIAA Paper , [8] J. L. Vadillo, R.K. Agarwal, A. W. Cary and W. W. Bower, Numerical Study of Virtual Aerodynamic Shape Modification of an Airfoil Using a Synthetic Jet Actuator, AIAA Paper , [9] J. L. Vadillo, R. K. Agarwal, and A. A. Hassan, Modulation of Aerodynamic Forces and Moments at Transonic Speeds Using Active Flow Control, AIAA Paper , [10] K. Salari, DOE s Effort to Reduce Truck Aerodynamic Drag through Joint Experiments and Computations, LLNL-PRES Presentation, Lawrence Livermore National Laboratory, 28 February [11]M. Bellman, Numerical Drag Reduction Studies of Generic Truck Models Using Passive and Active Flow Control, M.S. Thesis, Washington University in St. Louis, June [12]A. Seifert., O. Stalnov, D. Sperber, G. Arwatz, V. Palei, S. David, I. Dayan and I. Fono, I., Large Trucks Drag Reduction Using Active Flow Control, AIAA Paper, AIAA Aerospace Sciences Meeting, Reno, NV, January [13]M. Pastoor, L. Henning, B.R. Noack, R. King, and G. Tadmor, Feedback Shear Layer Control for Bluff Body Drag Reduction, Journal of Fluid Mechanics, Vol. 608, 2008, pp [14]E. Wassen, and F. Thiele, Drag Reduction for a Generic Car Model Using Steady Blowing, AIAA Paper , ISBN: