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Outline

Title
Abstract
Key Takeaways
Figures
Introduction
Numerical Model for Flow Analysis
Optimization Methodology
Bump-Optimization Study
Discrete Suction-Optimization Study
Hybrid-Optimization Study
Conclusion
References

Drag Reduction Using Active and Passive Flow Control Techniques

Profile image of Bedir YagizBedir Yagiz
https://doi.org/10.1016/J.AST.2011.03.003
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Abstract

The objective of this paper is to appraise, the practicability of weakening the shock wave and hereby reducing the wave drag in transonic flight regime using flow control devices such as two dimensional contour bump, individual jet actuator, and also the hybrid control which includes both control devices together, and thereby to gain the desired improvements in aerodynamic performance of air-vehicle. To validate the numerical study, a natural laminar flow airfoil, Rae5243, is chosen and then comparisons with experimental data have been made before the optimization of flow control parameters. After validation study, an efficient Gradient Based optimization technique is used to optimize 2D bump parameters including the length, the maximum height, the bump position via shock location, and the crest position via bump and also the jet actuation parameters such as mass flow coefficient, suction/blowing angle, actuation location over the upper surface of the airfoil. The process generally consists of using the simulation code to obtain a flow solution for given parameters and then search the optimum parameters to reduce the total drag of the airfoil via the optimizer. Most importantly, it is shown that, the optimization yields 3:94% decrease in the total drag and 5:03% increase in lift, varying the design parameters of active and passive control devices.

Key takeaways
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  1. Hybrid control achieves a 3.94% drag reduction and 5.04% lift increase in transonic flight.
  2. Gradient-based optimization effectively minimizes drag while maintaining lift using flow control techniques.
  3. The study validates computational models against experimental data for the Rae5243 airfoil at Mach 0.6799.
  4. 2D contour bumps significantly mitigate shock strength, enhancing aerodynamic performance without additional energy input.
  5. Discrete suction optimizes drag reduction, yielding a 3.13% decrease while increasing lift by 3.17%.
Figures (16)
Fig. 1. The automated optimization process.
Fig. 1. The automated optimization process.
Fig. 2. The computational grid used in 2D simulation.
Fig. 2. The computational grid used in 2D simulation.
Initial and optimal parameters for bump optimization. Table 2 2D contour bump has been optimized for drag reduction at transonic speed using the gradient-based optimization. The length, Iz, maximum height, hg, and the crest position relative to bump and airfoil, cg, xg, depicted in Fig. 4 are chosen as the key param- eters. For the initial points as given in Table 2, asymmetric bump The Rae5243 airfoil was tested by Fulker and Simmons [7] at different transonic speeds with surface mass injection. The valida- tion case used in this study has a Reynolds number of 18.68 x 10° million based on airfoil chord, Mj. = 0.6799, a = 0.77°, corre-
Initial and optimal parameters for bump optimization. Table 2 2D contour bump has been optimized for drag reduction at transonic speed using the gradient-based optimization. The length, Iz, maximum height, hg, and the crest position relative to bump and airfoil, cg, xg, depicted in Fig. 4 are chosen as the key param- eters. For the initial points as given in Table 2, asymmetric bump The Rae5243 airfoil was tested by Fulker and Simmons [7] at different transonic speeds with surface mass injection. The valida- tion case used in this study has a Reynolds number of 18.68 x 10° million based on airfoil chord, Mj. = 0.6799, a = 0.77°, corre-
Rae5243 airfoil lift and drag coefficient without and with suction. Table 1 of the Rae5243 airfoil. The control devices are stayed in a much refined region. The domain was decomposed into four blocks to make the computations parallel. The cases were run at Old Do- minion University’s teraflop computer cluster.
Rae5243 airfoil lift and drag coefficient without and with suction. Table 1 of the Rae5243 airfoil. The control devices are stayed in a much refined region. The domain was decomposed into four blocks to make the computations parallel. The cases were run at Old Do- minion University’s teraflop computer cluster.
Fig. 5. Variation of (a) bump design and (b) normalized C;, Cp during the bump control optimization process. is used with a relative height of 1.2% chord consisting of the arcs located between x = 0.5c and 0.99c with the junction of the arcs at x =0.9c on the Rae5243 airfoil section. 5. Discrete suction-optimization study As in the case of 2D contour bump, the effect of discrete suc- tion on drag has been investigated. The flow control parameters such as mass flow coefficient, Cg, location of actuator, x;, and suction/blowing angle relative to the local surface tangent, 6, are optimized to get the minimum drag. The width of the actuator, Wg, is kept fixed as 0.0035 during the optimization. The angle is defined as the angle between the jet flow direction and the local airfoil surface tangent. The effects of these parameters were ana- lyzed by the authors of this paper in 2009 [22]. Fig. 8 shows the parameters of the control jet. The lift coefficient is constrained to the non-control value, C, = 0.5299, during the optimization process. The objective is to mini- mize the total drag coefficient. The optimization problem is defined in the following form:
Fig. 5. Variation of (a) bump design and (b) normalized C;, Cp during the bump control optimization process. is used with a relative height of 1.2% chord consisting of the arcs located between x = 0.5c and 0.99c with the junction of the arcs at x =0.9c on the Rae5243 airfoil section. 5. Discrete suction-optimization study As in the case of 2D contour bump, the effect of discrete suc- tion on drag has been investigated. The flow control parameters such as mass flow coefficient, Cg, location of actuator, x;, and suction/blowing angle relative to the local surface tangent, 6, are optimized to get the minimum drag. The width of the actuator, Wg, is kept fixed as 0.0035 during the optimization. The angle is defined as the angle between the jet flow direction and the local airfoil surface tangent. The effects of these parameters were ana- lyzed by the authors of this paper in 2009 [22]. Fig. 8 shows the parameters of the control jet. The lift coefficient is constrained to the non-control value, C, = 0.5299, during the optimization process. The objective is to mini- mize the total drag coefficient. The optimization problem is defined in the following form:
Fig. 6. Bump-optimization study results: (a) comparison of airfoil shapes, (b) Cp distributions, (c) BL comparison.
Fig. 6. Bump-optimization study results: (a) comparison of airfoil shapes, (b) Cp distributions, (c) BL comparison.
Fig. 7. Mach contours: (a) baseline and (b) with bump.
Fig. 7. Mach contours: (a) baseline and (b) with bump.
is reliant on the actuator location. The suction, located at x/c = 0.3944, slightly reduces the BL thickness upstream of the shock location, x,/c = 0.53, after the shock the decrement is considerably increased. The actuator located upstream of the shock, dependent on the suction rate, spreading the top of the shock to three small pieces. The separation can be seen from the Mach contours given in Fig. 11. These small partitions cause reduction in pressure drag with increment in lift value.
is reliant on the actuator location. The suction, located at x/c = 0.3944, slightly reduces the BL thickness upstream of the shock location, x,/c = 0.53, after the shock the decrement is considerably increased. The actuator located upstream of the shock, dependent on the suction rate, spreading the top of the shock to three small pieces. The separation can be seen from the Mach contours given in Fig. 11. These small partitions cause reduction in pressure drag with increment in lift value.
value. The optimization results are given in Table 3. The aerody- namic performance, lift-to-drag ratios is increased 6.5%. Initial and optimal values for actuator optimization. Fig. 10 indicates the comparison of the pressure distributions and the boundary layer thickness normalized by the baseline BL thickness for the cases without flow control and with optimized flow control. The effect of suction on the pressure distributions Table 3
value. The optimization results are given in Table 3. The aerody- namic performance, lift-to-drag ratios is increased 6.5%. Initial and optimal values for actuator optimization. Fig. 10 indicates the comparison of the pressure distributions and the boundary layer thickness normalized by the baseline BL thickness for the cases without flow control and with optimized flow control. The effect of suction on the pressure distributions Table 3
Fig. 9. The variation of (a) Cg and (b) normalized C,, Cp values during the optimization process.
Fig. 9. The variation of (a) Cg and (b) normalized C,, Cp values during the optimization process.
The results of the computations of a contour bump in combi- nation with downstream slot suction is illustrated in Fig. 13 by pressure distributions and boundary layer parameter. The bound- ary layer was only increased at the interaction region. As already seen in the previous sections, the surface suction with appropriate selection of Cg, 6, X-c decreases the drag, 3.13% with an increment in lift value, 3.17% and the optimum bump alone results in a drag reductions of 0.9843% without any penalty in lift. The optimiza- tion of hybrid control raises the drag reduction to 3.94% and lift increment to 5.04%. The corresponding pressure distributions and boundary layer thickness in Fig. 13 show that the hybrid control reduces the wave drag, 32.58%, due to pushing and spreading the shock upstream with increasing viscous drag around 19.15%. As
The results of the computations of a contour bump in combi- nation with downstream slot suction is illustrated in Fig. 13 by pressure distributions and boundary layer parameter. The bound- ary layer was only increased at the interaction region. As already seen in the previous sections, the surface suction with appropriate selection of Cg, 6, X-c decreases the drag, 3.13% with an increment in lift value, 3.17% and the optimum bump alone results in a drag reductions of 0.9843% without any penalty in lift. The optimiza- tion of hybrid control raises the drag reduction to 3.94% and lift increment to 5.04%. The corresponding pressure distributions and boundary layer thickness in Fig. 13 show that the hybrid control reduces the wave drag, 32.58%, due to pushing and spreading the shock upstream with increasing viscous drag around 19.15%. As
Fig. 12. Variation of (a) design variables and (b) normalized C,, Cp during hybrid control optimization process.
Fig. 12. Variation of (a) design variables and (b) normalized C,, Cp during hybrid control optimization process.
Fig. 13. Comparison of (a) airfoil, (b) Cp distributions and (c) BL thickness.
Fig. 13. Comparison of (a) airfoil, (b) Cp distributions and (c) BL thickness.
Fig. 14. Mach contours: (a) baseline and (b) with hybrid control.
Fig. 14. Mach contours: (a) baseline and (b) with hybrid control.

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Karim Mazaheri

Shock Waves, 2015

A shock control bump (SCB) is a flow control method which uses local small deformations in a flexible wing surface to considerably reduce the strength of shock waves and the resulting wave drag in transonic flows. Most of the reported research is devoted to optimization in a single flow condition. Here, we have used a multi-point adjoint optimization scheme to optimize shape and location of the SCB. Practically, this introduces transonic airfoils equipped with the SCB which are simultaneously optimized for different off-design transonic flight conditions. Here, we use this optimization algorithm to enhance and optimize the performance of SCBs in two benchmark airfoils, i.e., RAE-2822 and NACA-64A010, over a wide range of off-design Mach numbers. All results are compared with the usual single-point optimization. We use numerical simulation of the turbulent viscous flow and a gradient-based adjoint algorithm to find the optimum location and shape of the SCB. We show that the application of SCBs may increase the aerodynamic performance of an RAE-2822 airfoil by 21.9 and by 22.8 % for a NACA-64A010 airfoil compared to the no-bump design in a particular flight condition. We have also investigated the Communicated by A. Hadjadj.

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Performance Characterization of a New Model for a Cyclone Separator of Particles Using Computational Fluid Dynamics
María José Torres

Applied Sciences

In the present study, through Computational Fluid Dynamics techniques, the performance characterization of a new Stairmand-type separator cyclone was carried out using the commercial software ANSYS Fluent. Four models for the geometrical cyclone separator were built, namely model A as per the dimensions reported in the literature and models B, C, and D by applying square and circular shape cavities as a passive flow control technique on the surface of its cylindrical section. The Navier-Stokes equations with the RSM turbulence model were formulated to solve the continuous phase of the cyclone separator and, the Lagrangian approach was adopted to track the solid particles with one way-coupling. The proposed model’s separation efficiency and pressure drop were compared against those recorded in the previous studies reported in the literature. Model D was the cyclone separator that stood out as the most valuable by demonstrating a separation efficiency and pressure drop decrement of 0....

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A Parametric Study of Trailing Edge Flap Implementation on Three Different Airfoils Through an Artificial Neuronal Network
Jesús Ilzarbe

Symmetry, 2020

Trailing edge flaps (TEFs) are high-lift devices that generate changes in the lift and drag coefficients of an airfoil. A large number of 2D simulations are performed in this study, in order to measure these changes in aerodynamic coefficients and to analyze them for a given Reynolds number. Three different airfoils, namely NACA 0012, NACA 64(3)-618, and S810, are studied in relation to three combinations of the following parameters: angle of attack, flap angle (deflection), and flaplength. Results are in concordance with the aerodynamic results expected when studying a TEF on an airfoil, showing the effect exerted by the three parameters on both aerodynamic coefficients lift and drag. Depending on whether the airfoil flap is deployed on either the pressure zone or the suction zone, the lift-to-drag ratio, CL/CD, will increase or decrease, respectively. Besides, the use of a larger flap length will increase the higher values and decrease the lower values of the CL/CD ratio. In addit...

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