CC BY
95
Transport
Menshchikov Alexander, Center for Computational and Data-Intensive Science and Engineering (CDISE) Skolkovo Institute of Science and Technology (Skoltech) Skolkovo, Moscow Region, Russian Federation E-mail: [email protected]
DEVELOPMENT OF ADAPTIVE WING WITH ADAPTIVE FLAP AND SLAT FOR UNMANNED AERIAL VEHICLES
Abstract: Aircrafts perform flight in multiple regimes with different speeds, Angles of Attack (AoA), sideslip angles and different altitudes. Designers usually choose the airfoil with the best performance for the cruise mode only or which stays suboptimal for all the flight regimes. It leads to reduction of maximum lift-to-drag ratio for certain regime as well as reduction of the overall performance. That is why adaptive wing, which can stay optimal for any flight regime is promising technology, which could significantly improve the performance and maneuverability of the aircraft during the flight. This work demonstrates performance of the wing with traditional and adaptive mechanization of the flap and slat in computer simulation and in the wind tunnel testing. It also provides the design of the adaptive wing with adaptive flap and slat. All the investigations were performed for 2D airfoil under different Reynolds numbers and Ao A. This paper proves, that adaptive wing improves lift-to-drag ratio and maneuverability of the aircraft for different flight regimes. Improved lift-to-drag ratio increases the range and the mission time.
Keywords: Intelligent Structures, adaptive wing, aircraft mechanization.
1. Introduction
Adaptive wing is kind of so-called Intelligent Structures. Intelligent structures are kind of structures, which have close integration of the actuation, sensing, controlling systems and computer architecture. That kind of structures has ability to sense and perform actions according to the outer conditions, based on non-trivial controlling and computing algorithms. Highly cognitive properties of those structures make them kind of bio-inspired embedded neuro systems for engineering
applications. structures [1]
Section 5.
Figure 1. Classification of Intelligent Structures as a subset of adaptive, sensory and controlled
For better understanding of the Intelligent Structures concept it is better to refer to the article [1], where they are described as the subset of complex intersection of adaptive, sensory and controlled structures.
Intelligent structures are a subset of a much larger field of research. It incorporates Adaptive, Sensory, Controlled, Active structures and includes high authority control system [2].
Adaptive structures are defined as those, which possess actuators that allow the alteration of system states and characteristics in a controlled manner [1]. Another definition of Adaptive Structures are structures, that "can purposefully vary its geometric configuration as well as its physical properties" [3]. Adaptive structures have the system ofdistributed actuators. All the wings of conventional aircrafts have multiple actuators for flaps, slats, ailerons, etc. That is why wings of conventional aircrafts are adaptive structures.
Sensory structures have the system of distributed sensors. These sensors return the information about the state of the structures, or about outer conditions (temperature, flow velocity, pressure, current, etc.).
Controlled structures are inside overlap of adaptive and sensory structures. The state of these structures could be influenced by information from sensors in a simple close-loop or any more advanced control system architecture.
Active structures are inside the subset of controlled structures. Their actuators also have load-bearing functionality.
Intelligent structures are a subset of active structures. They have everything, mentioned above: highly distributed sensors and actuators system. These systems are united by control system. Actuators also have structural functionality. In addition to that intelligent structures have distributed control functions and advanced computing architecture.
Currently there are many examples of applications ofintelligent structures for modern UAVs. These examples include: aeroelastic control and maneuver enhancement of helicopter [4], high performance
aircraft-like wing, made from carbon laminate [5], active structure damping, wave propagation control, active stabilization (aircraft flutter), vibration and shape control. The first adaptive wing was created in 1986 as embedded part of F-111 jet fighter [6]. However, those time it was inefficient and impractical. A few years later, methods of incorporating actuators into wing substructure or skin, hence developing true adaptive aeroelastic structure, have been investigated with promising results [7]. However, 20 years later "FlexSys inc." proved, that adaptive wing with smooth variable flap for business jets could become effective and commercially attractive [8].
Currently "FlexSys inc." has the most advanced technology. However, it was created for business jets - fast passenger airplane, which flies in conditions with high Reynolds numbers, low AoA and performs gentle maneuvers with low g-loads. In contrast, UAV fly in low Reynolds numbers, low to medium AoA and could perform abrupt maneuvers with high g-loads. Furthermore, that concept has adaptive flap, but has not adaptive slat.
That is why the investigation of adaptive wing with adaptive flap and slat for UAV should be investigated.
2. Methodology
A. Computational Fluid Dynamics Analysis of Adaptive Flaps
Figure 2. The airfoil Clark YH with traditional (top) and adaptive (bottom) mechanization. The color depicts angle of deflection: 0O-red; 10O-orange; 20O-yellow; 30O-green. Solid line is for negative angles, dotted line is for positive angles
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Re = 100 000; Configuration - traditional;
Figure 3. CL vs CD characteristics for different Reynolds numbers and configurations of the wing. The color depicts angle of deflection: 0O-red; 10O-orange; 20O-yellow;
30O-green. Solid line is for negative angles, dotted line is for positive angles.
In the current work all experimentation is divid- to the wing with adaptive mechanization. The model
ed into two parts: 2D airfoil computational analysis of investigation is Clark YH airfoil. In all the experi-
and wind tunnel testing. Both methods applied to ments the flap is deflected from +30O to -30O with
the wing with traditional mechanization as well as 10O increment.
Figure 4. Lift coefficient vs Angle of Attack characteristics for different Reynolds numbers and configurations of the wing. The color depicts angle of deflection: 0O-red; 10O-orange; 20O-yellow; 30O-green. Solid line is for negative angles, dotted line is for positive angles
The investigation was performed for all the geometrical models of adaptive and traditional mechanization configurations for different conditions:
• Reynolds number varies from 100000 to 300000 with 100000 increment;
• AoA varies from -5o to 35o with 0.5o increment.
For Computational Fluid Dynamics (CFD) study XFLR5 software was used. It is open-source software for low Reynolds numbers investigations [9]. Every single configuration of the wing under certain conditions (Reynolds number and AoA) was calculated in 100 steps.
CFD study of adaptive wing with adaptive flap showed the improvement of aerodynamic characteristics. It is depicted in polar curve - dependency of lift coefficient (CL) from drag coefficient (CD)CL vs Cd (Fig. 3) and CL vs AoA (Fig. 4) characteristics.
Cl vs Cd characteristics shows, that in the same conditions adaptive wing has slightly lower cl, than traditional wing. However, cd for adaptive wing is significantly lower, than for traditional wing under the similar condition and configuration. That is why lift-to-drag ratio of the adaptive wing significantly increases. It leads to lower power consumption of the aircraft.
Cl vs Cd characteristic is in polar reference frame. That is why the modulus of the radius-vector and its inclination represent physical characteristics. The modulus of radius-vector shows total aerodynamic force and the inclination angle shows lift-to-drag ratio for the certain conditions. If radius vector is tangential line, which origins in the reference point, its inclination angle will represent maximum lift-to-drag ratio possible in such configuration:
Kmax = tan d; d - inclination of the tangential.
It is also seen from the graphs (Fig. 4), that they are slightly higher for adaptive wing, than for traditional wing, hence the maximum achievable lift-to-drag ratio is also increased.
All the Cl vs AoA (Fig. 4) graphs slightly moved in the positive direction of AoA (horizontal) axis.
Hence, the stalling angle increased for ~4o for all the configurations. It means improvement of stability and maneuverability.
Critical values of CL (for stalling angle) are also increased in every case. It means higher values of the lifting force before stalling. Hence, better controllability for near-critical regimes.
All the Cl vs AoA characteristics in the stalling angle region are low-sloped. It means, that aircraft doesn't lose controllability under stalling conditions.
3. Experimental Results
B. Experimental setup and measurements
All the experiments were conducted in the wind tunnel (Fig. 5). That wind tunnel is specialized for low-Reynolds number investigations. The characteristics of the wind tunnel are the following:
• Maximum airflow velocity: 30 m/s
• Test chamber width x length: 0.8m x 1.0m
• Mean turbulence level: 7%
Figure 5. The Wind Tunnel
That wind tunnel is equipped with particle generator and high FPS optical system. That methodology is widely known as Particle Image Velocimetry (PIV). In our case particles are produced by the fume generator, then they go to the inlet of the wind tunnel. Special mirror transforms the laser beam into light sheet. Than it illuminates particles inside the test chamber. Reflected light goes to the high-speed cameras, which capture the picture from different angles. That video becomes an input file for advanced videogrammetry software, which calculates position and velocity of
every particle in the flow for every frame. Kinematic characteristics of these particles over time allow to calculate pressure distribution over the object of investigation (e.g. airfoil), (Fig.6).
The advantage of such a methodology is no touch measurements, which do not produce any
disturbance of flow. Advanced software allows to calculate characteristics of the flow in every point with high accuracy. The disadvantage is that setup returns only 2D pictures of the flow. Furthermore, it doesn't work for the whole object simultaneously, but for the top or bottom surface at a time.
Figure 6. Particle Image Velocimetry methodology [10]
C. Adaptive wing design
The adaptive wing was designed and tested in SolidWorks 2016 software (Fig. 7). The wing consists of 40 parts. The mechanization has the following characteristics:
• Slat can deflect up to 30o;
• Flap can deflect from -30o to +30°;
• The thickness varies from 11.9% to 22%.
The section of adaptive wing has 11.2 cm chord
and 40 cm wingspan. All the control surfaces are deflected by means of 6 servos, which are situated inside the wing. They are controlled by Arduino Uno microcontroller. All the parts of the wing were made from ABS plastic, using additive manufacturing technologies. The skin of the wing was made from silicon.
Figure 7. The design of the adaptive wing segment in the Solid Works 2016 software
Figure 8. The manufacturing of the section of the adaptive wing
D. Experiment
The experiment was performed for every deflection angle of the flap and for AoA from -5o to +35o with 5o step. The velocity of the flow had the following values: 10 m/s, 20 m/s, 30 m/s to achieve 100000, 200000 and 300000 Reynolds numbers.
posrtion (mm)
Figure 9. The test chamber of the wind tunnel with the laser, turned on (top); the data, received during the experiment by the cameras (bottom)
During the experiment, the pictures of illuminated airflow over the wing was received (Fig. 9).
These pictures and videos were input data for the OpenPIV software. As the result, all the important aerodynamic characteristics were calculated: lift, drag, pressure and velocity distribution (Fig. 10).
Figure 10. Visualization of the pressure distribution of the airflow over the segment of the adaptive wing
The analysis of the experimental results proved the initial hypothesis. Furthermore, the experiment proves, that flap deflection leads to increase of lifting force as well as lift-to-drag ratio. It also increases the torque, that means improvement of controllability and maneuverability of the aircraft. The deflection of the adaptive flap also leads to decrease of the drag force, hence, it lowers the power consumption of the aircraft.
The experiment also showed significant decreasing of the pressure over the upper surface of the wing. It increases lifting force.
4. Conclusions
1. Verification of the XFLR5 software showed, that the error for low Reynolds number is ~1%.
2. The advantage of adaptive mechanization was showed:
a. The lift-to-drag ratio of the adaptive wing significantly increases. It leads to lower power consumption of the aircraft. The maximum lift-to-drag
ratio is also increased up to 5% for all the configurations and regimes.
b. The stalling angle increased for ~4° for all the configurations. It means improvement of stability and maneuverability.
c. Critical values of CL increased in every case. It means higher values of the lifting force and better controllability for near-critical regimes.
d. Improvement of controllability under stalling conditions.
3. During the investigation the segment of the adaptive wing was successfully designed and manufactured. It reliably worked during the experiment in the wind tunnel.
4. As a result of the experiment, the initial hypothesis, that adaptive flap would significantly im-
prove aerodynamic characteristics of the wing, was successfully proved.
5. Future Work
In the current work numerical investigation and the wind tunnel experiment of the adaptive flap was represented. However, the prototype of the adaptive wing segment also has adaptive slat and variable thickness mechanization. Investigation of their influence on aerodynamic characteristics should become the objectives of the future work.
Acknowledgment
Authors thank foundation "Innovations Promotions Fund" and "UMNIK" scientific grant program for young specialists for supporting current research. Grant number is № 9189ry/2015.
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