roll control of a uav using an adaptive torsion structuremichael.friswell.com/pdf_files/c293.pdf ·...

1 American Institute of Aeronautics and Astronautics

Roll Control of a UAV Using an Adaptive Torsion Structure

R.M. Ajaj1, M.I. Friswell3

School of Engineering University of Wales Swansea, Swansea, United Kingdom, SA2 8PP

D.D. Smith2, G. Allegri4

Department of Aerospace Engineering University of Bristol, Bristol, United Kingdom, BS8 1TR

and

A.T. Isikveren5

Bauhaus Luftfahrt e.V. Lyonel-Feininger-Str. 28 80807 Munich Germany

This paper presents a novel Active Aeroelastic Structure (AAS) concept and performs a multidisciplinary design optimization (MDO) study by employing this aforementioned concept in a UAV wing to provide roll control, i.e. for replacing conventional ailerons. The Adaptive Torsion Structure (ATS) concept allows varying the torsional stiffness of a two-spar wing-box by changing the relative chord-wise positions of the front and rear spar webs. Each side of the wing is divided into five equal partitions from root to tip, and the ATS concept is employed along each partition. At the ends of each partition, connecting ribs join individual ATS units. Those ribs allow the spar webs of each partition to translate independently of the spar webs of the adjacent partitions and maintain continuous load path across the wing-box. An MDO suite consisting of the Genetic Algorithm (GA) optimizer coupled with a high-end low-fidelity aero-structural model was developed and employed to maximize the rolling moment generated by the wing under pre-defined structural and control constraints. The study is conducted at three different points across the climb segment of the UAV, namely: start of climb, mid climb, end of climb/initial cruise.

1 Research assistant, College of Engineering, Swansea University, Wales, UK, SA2 8PP, AIAA member. 2 PhD student, Department of Aerospace Engineering, University of Bristol, Bristol, UK, BS81TR, AIAA member. 3 Professor of Aerospace Structures, College of Engineering, Swansea University, Wales, UK, SA2 8PP. 4 Senior lecturer, Department of Aerospace Engineering, University of Bristol, Bristol, UK, BS8 1TR, 5 Head, Visionary Aircraft Concepts and Deputy Chief Technical Officer, Bauhaus Luftfahrt e.V. Lyonel-Feininger-Str. 28,80807 Munich, Germany, AIAA member.

52nd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference<BR> 19th4 - 7 April 2011, Denver, Colorado

AIAA 2011-1834

Copyright © 2011 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.


Nomenclature a = lift-curve slope A = enclosed area (m2) ds = infinitesimal segment along the perimeter of the closed section (m) ec = distance between aerodynamic centre and shear centre (m) {F} = nodal forces and moments vector (N or Nm) G = shear modulus (N/m2) GJ = torsional stiffness (Nm2) J = torsion constant (m4) [K] = stiffness matrix (N/m or Nm) l = wing semi-span (m) m = number of nodes per wing semi-span n = number of partitions per wing semi-span q = dynamic pressure (N/m2) RM = rolling moment (Nm) s = length of a beam element (m) t = thickness (m) T = external torque (Nm) U = speed (m/s) W = work done (J) X = position of the spar web (% of wing-box chord) y = nodal position along wing semi-span αo = initial angle of incidence (rad) {δ} = nodal deflections vector (m or rad) ΔUstrain = change in strain energy (J) θ = twist angle (rad) Subscripts act = actuation D = dive div = divergence ext = external f = flutter fw = front web rw = rear web ref = reference y = across the span

I. Introduction HE idea of modifying the aerodynamic profile of a lifting surface to enhance flight control and aircraft performance is not new. The Wright Brothers employed wing warping for seamless flight control in

their first flying machine. Conventional control surfaces present various limitations on the maneuverability and the capability of the air-vehicle, and they set different constraints on its flight envelope, mission effectiveness. Moreover, conventional controls can lead to a significant increase of the airframe installation and maintenance costs of the airframe significantly [1]. For instance, retaining aileron authority at large dynamic pressures and avoiding reversal lead to a significant weight penalty for the Myasishchev M-50 and Tsybin R-020 Soviet aircrafts, since the wing structure had to be substantially reinforced [2-4]. Aircraft designers aim to exploit adaptive structures and the resulting wing flexibility in order to augment the performance of conventional control surfaces or to completely replace them. Active Aeroelastic Structures (AAS) are adaptive structures based on manipulating the aerodynamic profile/shape of a lifting surface, without requiring significant planform modifications that typically demand complex and heavy mechanisms. AAS depend on the aerodynamic loads to deform them and maintain their shape; this reduces the energy requirements associated with these structures.

T


Recently, the use of AAS to improve flight performance, enhance control authority, and enhance stealth characteristics of air-vehicles has been under thorough investigation in a number of research programs and projects across the world. In the United States of America (USA), both the Active Flexible Wing (AFW) program [5] and the Active Aeroelastic Wing (AAW) program [6, 7] investigated the use of flexible wing structures coupled with leading and trailing edge control surfaces. The structural deformations of an advanced fighter wing were manipulated in order to eliminate aileron reversal problems at large dynamic pressures. Also the rolling performance was maximized without using the horizontal tail. Furthermore, Griffin et al. [8] investigated the use of a smart spar concept to vary the torsional stiffness and to control the aeroelastic behavior of a representative wing. This design concept also aimed to enhance the roll rate of high performance aircraft at large dynamic pressures. The solution proposed was based on the simultaneous actuation of control surfaces and the modification of the wing torsional stiffness using the aforementioned smart spar concept. The latter has a web that can either transfer shear between the upper and lower caps or disable such load transmission mechanism. This is achieved by allowing the smart spar to move from a reference position along the leading edge to a diagonal arrangement where the front caps at the wing root are connected to the aft most ones at the wing tips. Similarly, Chen et al. [9] developed the Variable Stiffness Spar (VSS) concept to vary the torsional stiffness of the wing and again enhance the roll performance. Their VSS concept consisted of a segmented spar having articulated joints at the connections with the wing ribs; these were actuated by electrical motor capable of rotating the spar through 90 degrees. In the horizontal position, the segments of the spar are uncoupled, so the spar offers no bending stiffness. In the vertical position, the segments are fully joined, thus the spar provides the maximum torsional and bending stiffness. The concept allows the stiffness and aeroelastic deformations of the wing to be actively controlled depending on the flight conditions.

Nam et al. [10] took the VSS solution a step forward and developed the torsion-free wing concept. This aims to attain a post-reversal aeroelastic amplification of wing twist. The primary structure of the torsion free wing consists of two main parts. The first is a narrow wingbox tightly attached to the upper and lower wing skin in order to provide the basic wing torsional stiffness. The second part consists of two variable stiffness spars placed near the leading and trailing edges, passing through all the rib holes. Nam et al. demonstrated that the torsion-free wing can provide significant aeroelastic amplification, leading to an increase in roll-rate between up to 48% with respect to the baseline performance in the worst possible flight conditions. Florance et al. [11] investigated the use of the VSS concept to exploit the wing flexibility and to improve the aircraft aerodynamic performance. Their wing incorporated a spar with a rectangular cross-section that runs from the wing root up to 58% of the overall wing span. The spar is used to change the wing bending and torsional stiffness as it is rotated between its vertical and horizontal positions.

In Europe, the Active Aeroelastic Aircraft Structures (3AS) research project [2-4], involved a consortium of 15 European partners belonging to the aerospace industry and was partially funded by the European Community. It focused on developing active aeroelastic design concepts via exploiting structural flexibility in a beneficial manner. The final aim was to improve the aircraft aerodynamic efficiency. One of the novel concepts proposed was the All-Moving Vertical Tail (AMVT) with a variable torsional stiffness attachment [12]. The AMVT concept was employed to design a smaller and lighter fin while maintaining stability and rudder effectiveness for a wide range of airspeeds. The AMVT employs a single airframe attachment whose position of the attachment can be adjusted in the chord-wise direction relative to the position of the centre of pressure; this allows achieving an aeroelastic effectiveness above unity [12]. Furthermore, the 3AS project investigated a variety of variable stiffness attachments and mechanisms for the AMVT concept, including a pneumatic device developed at the University of Manchester [13]. As part of the 3AS project, Cooper et al. [14-16] investigated two active aeroelastic structure concepts that modify the static aeroelastic twist of the wing by modifying its internal structure. The first concept exploited the chord-wise translation of an intermediate spar in a three spars wing-box in order to vary its torsional stiffness and the position of the shear centre. The second concept was similar to the VSS concept where rotating spars are employed to vary the torsional and bending stiffness as well as the shear centre positions. Prototypes of such concepts were built and tested in the wind tunnel to examine their behavior under aerodynamic loadings.

This paper presents a novel AAS concept, which allows tailored aeroelastic twist deformations of the structure of the wing. A preliminary Multidisciplinary Design Optimization (MDO) study is here performed by employing the novel concept along the wing of a UAV to replace conventional ailerons and provide roll control. The Adaptive Torsion Structure (ATS) concept modifies the torsional stiffness of a


thin-walled closed section two-spar wing-box by changing the area enclosed between the front and rear spar webs [17]. The enclosed area is modified by translating the spar webs in the chord-wise direction towards each other using internal actuators as shown Fig.1. Each side of the wing of the UAV is split into five equal partitions from root to tip, and the ATS concept is employed in each partition. The partitions are connected by ribs that allow the spar webs to translate independently of the webs of the adjacent partitions and maintain a smooth and continuous load path across the wing-box. An MDO suite consisting of a Genetic Algorithm (GA) optimizer coupled with a high-end low-fidelity aero-structural model [18] was developed (in MATLABTM) and employed to maximize the rolling moment generated by the wing while meeting given structural and control constraints. The study is conducted at three different points across the climb segment of the UAV, namely: start of climb, mid climb, end of climb/initial cruise.

II. Adaptive Torsion Structure Concept The Adaptive Torsion Structure (ATS) concept belongs to the family of AAS whose torsional stiffness

can be modified by changing the chordwise position of the front and rear spar webs as shown in Fig. 1. This changes the area enclosed by the front and rear spars; hence the torsional stiffness of the structure varies as:

a) Maximum torsional stiffness web positions (original

position)

b) Minimum torsional stiffness web positions

c) Adaptive torsion wingbox

Figure 1. The Adaptive Torsion Structure concept.


(1)

where J is the torsion constant (m4), A is the enclosed area (m2), t is the wall thickness (m), and ds is a infinitesimal length element (m) along the perimeter. In addition to the torsional stiffness, the position of the shear centre position and the chordwise bending stiffness vary, while the spanwise bending stiffness remains unaffected. The change in the torsional stiffness allows the external aerodynamic loads to induce aeroelastic twist deformations on the structure. These twist deformations can be controlled by changing the relative position of the webs as a function of the flight conditions.

III. Actuation Requirements The major benefit of the ATS concept is that the energy required to twist the structure and to maintain

its deformed shape can be extracted from the external aerodynamic loads once the change in torsional stiffness has been induced by the internal actuators. This reduces the required actuation energy in comparison with other adaptive structure concepts where actuators are required to directly twist the structure and continuously maintain its deformed shape. An analysis of the energy requirement associated with the ATS solution follows. This is meant to provide the aircraft designer with an estimate of the minimum level of actuation energy required and it defines the range of mechanisms and actuators (potentially off the shelf) that can satisfy such operational constraints. This paper does not focus on designing and analyzing the internal mechanism and internal actuators, as this will be dealt with in future work. The amount of energy required to initiate the energy extraction from the external flow depends on various factors:

the distance travelled by the webs; the relative position of the webs. the flight conditions (altitude and speed); the load path (transfer of loads between wing box components); and, the details of the mechanism employed.

However, at the level of conceptual design optimization, detailed knowledge of the actuation

mechanism employed is unavailable. This implies that the fraction of the actuation energy dissipated in the form of friction, aerodynamic damping and other conversion losses are neglected. Furthermore, in this analysis the spar webs are assumed to be locked in their final positions (using some locking mechanism) and the change between their original positions and final positions occurs in an instantaneous fashion. Under such assumptions, the minimum actuation energy required is equal to the change in the elastic potential energy of the wing after its torsional stiffness is varied, which is also equal to the work done by the external aerodynamic loads. Therefore the minimum actuation energy ( ) required when neglecting losses can be obtained as:

∆ (2)

Hence, the change in total elastic strain energy stored in the wing is

(3)

where GJ(i) is the torsional stiffness (Nm2) at the ith wing element, ∆ is the change in twist angle (rad) across the span of the ith element, is the span (m) of the ith element, and n is the number of elements across the wing semi-span. On the other hand, the total external work done by the external loads on the wing is:

4

∮

∆2

∆


(4)

where is the nodal torque (Nm) at the ith node, ∆ is the change in twist angle(rad) at the ith node, and m is the total number of nodes across the wing semi-span and it is equal to n+1.

IV. The MDO Suite The MDO suite employed belongs to the family of high-end, low-fidelity optimization tools. It consists

of the Genetic Algorithm (GA) optimizer coupled with the Tornado Vortex Lattice Method (TVLM) code [19,20], a Conceptual Thin-walled Structural model [18,21], and a 1-D Finite Element (FE) model as shown in Fig. 2.

A) Genetic Algorithm (GA) The GA optimizer was employed in this analysis for various reasons, summarized as:

Its ability to find a global optimum; It is available in MATLABTM format which simplifies the coupling with the aero-

structural models (that already exists in MATLABTM format); and, It follows the population-based approach which allows parallelizing the operation of the

suite and reduces computation time significantly [22].

B) Aerodynamics The MDO suite utilizes the Tornado Vortex Lattice Method (TVLM) [19,20] for the aerodynamic

predictions. Tornado is a linear aerodynamics code, and thus has limitations in application, such as the discounting of wing thickness and viscous effects, leading to a lack of conformity for angles above 8-10◦ for slender wings. These limitations mean that Tornado cannot be used within certain parts of the flight envelope. Nevertheless, linear aerodynamic theory is still very useful, as most aircrafts typically operate within the linear region (operating lift coefficients at reference speeds) in cruise, as well as both take off and landing phases. These are therefore the flight stages in which most of the research and analysis has been undertaken. More advanced nonlinear aerodynamic predicting tools would not be a practical tool for use in this work due to the substantial increase in solution time and complexity that would result.

C) Structures The Structural Model (SM) is based on thin-walled beam theory. The model discretize the wing into

elements, and generic sizing of the equivalent upper and lower skin panels (including stringers), and spar webs, is performed locally along the wing span based on the ultimate loading scenario. Then, the mechanical and structural properties of the elements are estimated. The wing sized is performed assuming that the webs are at their original positions (maximum torsional stiffness) as shown in Fig 1a. An expert module dedicated to identifying the location of the shear centre for different web positions is embedded within the model. Furthermore, static divergence speed and flutter speed checks are embedded into the SM to determine their variations with different web positions.

Figure 2. Flowchart and relational diagram of the MDO Suite.

∆

2


i) Shear centre position The shear centre module models the actual wingbox with an equivalent rectangular wingbox as shown

in Fig. 3. The shear centre is defined as the point in the cross-section through which shear loads produce no twisting [26]. Therefore its position is estimated by equating the moment produced by the overall shear force acting on the cross-section and the total moment produced by the shear forces in the individual members of the wingbox (equivalent skins and webs). The individual shear forces in each member are obtained by integrating the shear flow in those members (as shown in Fig. 3).

Figure 3. The shear flow in the equivalent wingbox.

ii) Divergence speed for finite wing

The standard method to estimate the divergence speed for a finite rectangular cantilever wing cannot be applied to the multi-partition wing used here because the torsional stiffness of the wing varies along the span. Therefore the divergence speed of the multi-partition wing is estimated using the principle of virtual work combined with a finite element approach. The fundamental steps followed are described briefly here. The virtual work done by internal torque and the virtual work done by the external aerodynamic moment on an Euler-Bernoulli beam of length s can be approximated as:

(5)

(6)

Now consider the multi-partition wing made-up of 5 beam elements, each of length s as shown in

Fig. 4, the twist angle at any point on the element can be approximated as a linear function of the twist angles at the extremities of the element:

(7)

Figure 4. Linear twist variation along the length of a wing element.

where and are the twist angles at the extremities of the element. Then, the work done by internal torques on this element (ith) element can be approximated as:

1


(8)

On the other hand, the work done by external aerodynamic moment on this element (ith) element can be approximated as:

(9)

where q is the dynamic pressure (N/m2) and is the initial angle of incidence (rad). The total work done by internal torque on the wing is the summation of the work done by internal torques on the individual elements:

(10)

The total work done by internal torque is the summation of the individual work done by internal torques:

(11)

Finally, applying the principal of virtual work (Eq. 12) yields a five by five matrix. The determinate of this matrix is set to zero; and hence 5 solutions for the divergence speed are obtained. The actual solution used in this analysis is the lowest real solutions.

0 (12)

iii) Flutter speed for finite wing The flutter speed of the wing is estimated using Galerkin’s Method [24,25] for unswept rectangular

cantilever wings. This method is limited to the fundamental torsion-bending binary flutter mode. The uncoupled flexural and torsional modes of the cantilever wing with uniform cross-section are respectively represented by the functions and defined as

cosh cos 0.734 sinh sin (13)

where

(14)

and

(15)

where is the nodal position along the wing semi-span and is the wing semi-span. The flutter modes are approximated as the fundamental modes of purely flexural and purely torsional oscillations in still air of a cantilever beam of uniform cross-section [26]. After assuming the mode shapes, they are substituted in the two equations of motion given in [24] and the equations are solved to obtain the flutter speed. However due to the multi-partition nature of the wing, the contribution of each wing partition to the coefficients of the equations of motion is estimated independently, and then the overall contribution of all the partitions are

1.875

sin2

,1 11 1

,6

2 11 2

3

3


summed to provide the contribution of the entire wing. In fact, the flutter speed could be estimated using higher fidelity approaches, however at the conceptual design level; the use of the high fidelity approaches will increase the computation time significantly. The emphasis here is to gain a fundamental understanding of the performance gain and power requirements associated with the ATS concept, and so detailed high fidelity modeling is beyond the scope of this study.

D) Finite Element (FE) Model The FE models the wing-box as an Euler-Bernoulli beam. The span-wise aerodynamic loads are

rearranged into equivalent loads at the nodes of each element (defined by the Structural Model). The stiffness matrices of the elements are transformed from their local coordinates to the wing coordinates (global) and then the wing stiffness matrix [K] is assembled from the stiffness matrices of those elements. Then, the nodal deflections of the wing-box in six degrees of freedom are computed as

(6)

where is the nodal forces and moments vector acting on the wing, is the global stiffness matrix for the wing, is the nodal deflection vector of the wing. These nodal deflections are fed into Tornado to modify the wing vortex lattice geometry and generate new aerodynamic loads. This process iterates until equilibrium between aerodynamic loads and wing deflection is established, and hence the final wing deflection is reached.

E) Operation of the suite Based on the reference flight condition which is assumed as initial cruise in this case, the VLM

generates the appropriate span-wise aerodynamic forces and moments on the wing. The SM converts these forces and moments into stresses and sizes the upper and lower equivalent skins and the webs of the front and rear spar, so that they are able to withstand the aerodynamic loads multiplied by an appropriate load factor and a factor of safety of 1.5. The stiffness matrix for each element is computed and the resulting aerodynamic forces and moments acting on each element are resolved into nodal forces and moments. The loads and structural stiffness matrices are assembled into the global FE model, and the nodal deflections for each element are estimated. The wing deflections are fed back into the VLM, and new aerodynamic loads are generated and passed again to the FE model. The process iterates until the change in wing lift is below a prescribed tolerance. Then the objective function is assessed. The optimization process continues until the convergence criterion of the suite is met.

V. Roll control using the ATS concept

The ATS concept is employed at the wing of a UAV to replace ailerons and provide roll control. A UAV was selected for this study because of the increasing demand to reduce radar cross section (RCS), enhance stealth characteristics, and increase the mission effectiveness of this category of air-vehicles. The design parameters of the selected UAV are listed in Table 1. In order to exploit the performance benefits of the ATS concept, each side of the wing is split into 5 equal partitions from root to tip. At the ends of each partition there are connecting ribs, and between these ribs there are two spar webs that can translate independently of the webs of the adjacent partitions as shown in Fig. 5. This allows the tip twist to be controlled and also it allows the twist distribution along the semi-span of the wing to be controlled. In addition to their traditional functions, the connecting ribs transfer the loads between the webs of adjacent partitions and maintain smooth and continuous load path across the wing-box. Hence the number of design variables and constraints associated with this study is large, and it becomes tedious to assess the sensitivity of the objective function to those design variables without the use of a multi-disciplinary design optimizer.


Table 1: Design Parameters of the UAV under investigations.

Design Variable Value MTOW 550 kg

Cruise speed 50 m/s Dive speed 70 m/s

Service ceiling 3050 m Wing area 12.6 m2 Wing span 10 m

Figure 5. Multiple partition ATS concept.

The ATS concept must replace conventional ailerons; hence it must provide adequate roll control at all points of the flight envelope. In this paper, the analysis is limited to the climb segment where the dynamic pressure is relatively low compared to the high speed phase, and hence roll authority is a critical control scenario. Three points across the climb segment were considered. These points correspond to different flight conditions (speed and altitude) and to different wing loading (fuel burn). At each flight point, the suite indicates the optimum combination of web positions for each partition to maximize or minimize the objective function. A list of these points and their corresponding flight conditions at are summarized in Table 2.

Table 2: Flight conditions at the three flight points. Flight point Altitude (m) Air density (kg/m3) Speed (m/s) Start of climb 0 1.204 25 Mid climb 1525 1.060 35 Initial cruise 3050 0.9041 50

A. Objective function In order to simplify the analysis, a single objective function is employed. The target is to maximize the rolling moment (RM) produced by the ATS concept.

B. Design variables

For each partition there are two main variables:

the position of the front web; and the position of the rear web.

This means that the number of design variables is double the number of partitions per wing semi-span.

C. Design constraints The design constraints set the range of positions where each web can translate as a fraction of the wing-

box chord. Each web is allowed to translate between its original position and 30% of the wingbox chord. Furthermore, the flutter and static divergence speed of the wing are set to be greater or equal to 150% of the design dive speed of the UAV. Finally, the actuation energy must be smaller or equal to 85% of the


reference actuation energy, which is the energy required to move the webs in all the five partitions from their original positions to 30% of the wing-box chord. An outline of the optimization problem is given in Table 3.

Table 3: Outline of the optimization problem. Objective function

Design variables : position of front web for

the ith partition : position of rear web for the

ith partition for i = 1:5 partitions

Design constraints 0 ≤ ≤ 0.3 0 ≤ ≤ 0.3 for i=1:5 partitions Uf ≥ 1.5 UD Udiv ≥ 1.5 UD

0.85

D. Results As stated before, the analysis in this paper focuses on three flight points of the climb segment. The GA

optimizer runs with 50 generations, each generation having 50 individuals. A selection rate of 10% and a crossover rate of 70 % were employed. Figure 6 shows the convergence of the suite at the three flight points.

a) At the start of climb


b) At mid climb

c) At the start of cruise

Figure 6. Convergence of the MDO suite at the 3 flight points.


Table 4: The optimum configuration of web positions at the 3 flight points. Flight point Webs positions 1st

partition 2nd

partition 3rd

partition 4th

partition 5th

partition

Start of climb front web position

( 0.2993 0.2811 0.2493 0.2963 0.2245

rear web position (

0.2741 0.2999 0.2992 0.2978 0.1337

Mid climb

front web position (

0.2980 0.2954 0.2406 0.2971 0.2996

rear web position (

0.2978 0.2978 0.0655 0.2999 0.0800

Start of cruise

front web position (

0.2929 0.2935 0.2988 0.2857 0.2851

rear web position (

0.2963 0.2791 0.2950 0.2278 0.0895

Table 5: The design parameters of the ATS wing at the 3 flight points. Flight point Tip twist (deg) Wact (J) Best RM (Nm) Dynamic pressure (N/m2) Start of climb 3.130 33.26 1157.0 377 Mid climb 4.200 56.30 2692.5 650 Start of cruise 7.800 172.70 10230.0 1130

Figure 6 indicates that convergence is achieved by the 50th generation. By examining Table 4, it can be seen that at the three flight points the optimizer tends to move both webs as close as possible to 30% of wing-box chord in the inboard region of the wing. On the other hand, for the tip region, the optimizer tries to minimize the movement of the rear web while moves the front web close to 30% of the wing-box chord. The reason for this is that the movement of the front web with the rear web fixed reduces the torsion stiffness and moves the shear centre aft, hence increasing the moment arm. In contrast shifting the rear web and keeping the front web fixed reduces the torsion stiffness, but moves the shear centre forward, hence reducing the aerodynamic moment arm. This means that larger tip twists and therefore larger rolling moments are gained by moving the front web alone [17]. Clearly the optimizer opts for moving the outboard front spar webs more due to the larger distance from the aircraft longitudinal body axis.

At the start of cruise, the dynamic pressure (1130 N/m2) is relatively large in comparison to the other cases. Therefore a large tip twist of 7.8 deg was achieved, yielding a rolling moment of 10 kNm as indicated in Table 5. In this condition considerably larger actuation energy and driving forces are required to translate the webs compared to the other cases. The average rate of twist along the wing was 1.56 deg/m, which is sufficiently small for the linear analysis (aerodynamics and structure) performed in this paper to be still valid.

Conclusions

An Active Aeroelastic Structure (AAS) concept to control the twist deformations of a wing has been introduced. A Multidisciplinary Design Optimization (MDO) study has been performed by adopting such concept in a representative UAV wing to replace ailerons and provide roll control. The study was conducted at three points of the climb segment of the UAV using a Genetic Algorithm (GA) optimizer coupled with a high-end low-fidelity aero-structural model. Preliminary results indicate that the optimizer favors the movement of the front web in the tip region of the wing to shift the shear centre aft and maximize the twisting moment arm, which will maximize the rolling moment generated by the wing with respect to the available actuation power. A multi-objective MDO problem is to be formulated using an aggregate objective function (AOF). The AOF will include two main terms: the rolling moment and the wing-box weight. The target of the optimization is to maximize the rolling moment generated while minimizing the wing-box weight through using different lay-ups of Carbon Fibre Reinforced Plastics (CFRP) across the wing-box.


Acknowledgments The authors acknowledge funding from the European Research Council through Grant Number 247045

entitled "Optimisation of Multiscale Structures with Applications to Morphing Aircraft".

References 1Friswell, M.I. & Inman, D.J. “Morphing Concepts for UAVs”, 21st Bristol UAV Systems Conference, April 2006. 2Kuzmina, S., Amiryants, G., Schweiger, J., Cooper, J., Amprikidis, M., and Sensberg, O., “Review and Outlook

on Active and Passive Aeroelastic Design Concept for Future Aircraft”. International Congress of the Aeronautical Sciences (ICAS 2002), September 8-13, Toronto, Canada, ICAS Vol. 432, pp. 1–10, 2002.

3Schweiger, J. & Suleman, A., “The European Research Project – Active Aeroelastic Structures” CEAS Int Forum on Aeroelasticity and Structural Dynamics, 2003.

4Simpson J., Anguita-Delgado L., Kawieski G., Nilsson B., Vaccaro V., and Kawiecki, G. “Review of European Research Project Active Aeroelastic Aircraft Structures (3AS),” European Conference for Aerospace Sciences (EUCASS), Moscow, Russia, 2005.

5Miller, G.D. “Active Flexible Wing (AFW) Technology,” Rockwell International North American Aircraft Operations, Los Angeles, CA, Report: AFWAL-TR-87-3096, 1988.

6Clarke, R., Allen, M.J., Dibley, R.P., Gera, J., and Hodgkinson, J. “Flight Test of the F/A-18 Active Aeroelastic Wing Airplane,” AIAA Atmospheric Flight Mechanics Conference and Exhibit, San Francisco, CA, AIAA 2005-6316, 2005.

7Pendleton, E.W., Bessette, D., Field, P.B., Miller, G.D., and Griffin, K.E. “Active Aeroelastic Wing Flight Research Program: Technical Program and Model Analytical Development,” Journal of Aircraft, 37(4):554-561, doi: 10.2514/2.2654, 2000.

8Griffin, K.E. and Hopkins, M.A. “Smart Stiffness for Improved Roll Control,” Journal of Aircraft, Engineering Notes, 34(3):445-447, 1997.

9Chen, P.C., Sarhaddi, D., Jha, R., Liu, D.D., Griffin, K., and Yurkovich, R. “Variable stiffness spar approach for aircraft manoeuvre enhancement using ASTROS,” Journal of Aircraft, Vol.37, No.5, September-October, 2000.

10Nam, C., Chen, P.C., Sarhaddi, D., Liu, D., Griffin, K., and Yurkovich, R. “Torsion-Free Wing Concept for Aircraft Maneuver Enhancement,” AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference, Atlanta, GA, AIAA 2000-1620, 2000.

11Florance, J.R., Heeg, J., Spain, C.V., and Lively, P.S. “Variable Stiffness Spar Wind-Tunnel Modal Development and Testing,” 45th AIAA/ASME/ASCE/AHS/ASC/ Structures, Structural Dynamics and Materials Conference, Palm Springs, California, AIAA 2004-1588, 2004.

12Amprikidis, M., Cooper, J.E., and Sensburg, O.. “Development of an Adaptive Stiffness All-Moving Vertical Tail,” 45th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, Palm Spring, California, AIAA 2004-1883, 2004.

13Cooper, J.E., Amprikidis, M., Ameduri, S., Concilio, A., San Millan, J., and Castanon, M., “Adaptive Stiffness Systems for an Active All-Moving Vertical Tail,” European Conference for Aerospace Sciences (EUCASS) July 4-7th, Moscow, Russia, 2005.

14Cooper, J.E. “Adaptive stiffness structures for air vehicle drag reduction.” In Multifunctional Structures/Integration of Sensors and Antennas (pp.15-1-15-12). Meeting Proceedings RTO-MP-AVT-141,Paper 15.Nueuilly-sur-Seine,France:RTO, 2006.

15Cooper, J.E.” Towards the Optimisation of Adaptive Aeroelastic Structures”, School of Mechanical, Aerospace and Civil Engineering, University of Manchester, M13 9PL, UK, 2006.

16Hodigere-Siddaramaiah, V. and Cooper, J.E. “On the Use of Adaptive Internal Structures to Optimise Wing Aerodynamics Distribution,” 47th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, Newport, Rhode Island, AIAA 2006-2131, 2006.

17Ajaj, R.M., Friswell, M.I., Dettmer, W.G., Allegri, G., and Isikveren, A.T., “Conceptual Modeling of an Adaptive Torsion Wing Structure,” 19th AIAA/ASME/AHS Adaptive Structures Conference, Denver, Colorado, 2011 (submitted for publication).

18Smith, D.D., Isikveren, A.T., Ajaj, R.M., and Friswell, M.I. “Multidisciplinary design optimization of an active nonplanar polymorphing wing,” 27th International Congress of the Aeronautical Sciences (ICAS 2010) Nice, France, 19-24 September 2010.

19Melin, T., “A Vortex Lattice MATLAB Implementation for Linear Aerodynamic Wing Applications,” Royal Institute of Technology (KTH), December 2000.

20Melin, T., “User’s guide and reference manual for Tornado”, Royal Institute of Technology (KTH), Department of Aeronautics, 2000-12.

21 Ajaj, R.M., Smith, D.D., Isikveren, A.T., and Friswell, M.I., “A Quasi-analytical Wing-box Weight Estimation and Advanced Sizing Model for Transport Aircraft,” Journal of Aircraft (submitted for review).

22Spall, J.C., Introduction to Stochastic Search and Optimization: Estimation, Simulation and Control, Chap.9 Evolutionary Computation I: Genetic Algorithms, pp.231-256, John Wiley & Sons, Inc., New Jersey, USA, 2003.


23Megson, T.H.G., Aircraft Structures for Engineering Students, Chap.9 Bending, shear and torsion of open and closed, thin-walled beams, pp. 276-345, Butterworth-Heinemann, Burlington, USA, 2003.

24Fung, Y.C. An Introduction to the Theory of Aeroelasticity, , Chap 6. Fundamentals of Flutter Analysis, pp. 186-245, Dover Publication Inc. New York 1993.

25Bisplinghoff, R.L., Ashley, H., and Holfman, R.L. Aeroelasticity, Chap.9 Flutter, pp.527-626, Dover publications, New York, USA, 1996.

26Young, D. and Felgar, R.P. “Tables of Characteristic Functions Representing Normal Modes of Vibration of a Beam,” The University of Texas Publication, No.4913, July 1, 1949.

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