Static and Dynamic Characterization of a Flexible Scaled

Joined-Wing UAV

Jose Carregadojose.carregado@tecnico.ulisboa.pt

Instituto Superior Tecnico, Universidade de Lisboa, Portugal

November 2017

Abstract

High Altitude and Long Endurance (HALE) aircraft are capable of providing revolutionaryintelligence, surveillance and reconnaissance (ISR) capabilities over vast geographic areas whenequipped with advanced sensor packages. As their use becomes more widespread, designers are nowexploring non-conventional configurations to meet the increasing demands. One such configuration isthe joined-wing concept, that typically connects front and aft wings in a diamond like planform. Oneexample is the Boeing Joined-Wing concept. While offering benefits regarding aerodynamic efficiencyand structural weight, the design requires careful consideration of elastic buckling resulting fromthe compression of the aft wing when supporting part of the forward wing structural loading. TheUniversity of Victoria Center for Aerospace Research (UVic CfAR) built a scaled UAV based on theBoeing sensorcraft design, using 3-meter wingspan flexible wings. Experimental results gathered fromthis test vehicle are used to validate predictions computed by a commercially available Finite Elementsolver. The main objective of this study is to develop a Ground Vibration Testing (GVT) campaign.Results from the experimental tests are used to characterize the modal dynamics of the aircraft, andto validate and update the numerical models. The GVT results are an important step towards a safeflight test program.Keywords: Joined-Wing, Ground Vibration Testing, Experimental Modal Analysis, FE Model Update.

1. Introduction

1.1. Project Background, Motivation & Objectives

Aicraft designers currently pursue the next gen-eration of High Altitude, Long Endurance (HALE)UAV to carry advanced sensor arrays in surveillancemission profiles.

Major players from the aircraft industry are in-volved in the development of prototypes. Boeing isone of these companies, coming up with the Joined-Wing Sensorcraft (JWSC) design. As part of theproject, the University of Victoria (UVic) Centerfor Aerospace Research (CfAR) was in charge ofbuilding a flexible aeroelastic flight test vehicle,scaled from the Boeing concept to a wingspan ofthree meters.

The joined-wing configuration typically presentsa variety of benefits over conventional aircraft: re-duced weight up to around 20% and higher stiffness[1], [2]. Also, Letcher and Kulhman have proven ex-perimentally that the joined-wing has less induceddrag and generally reduced transonic and super-sonic drag [3], [4].

However, this design imposes several challenges.

The major concern that motivates the present studyis the nonlinear aeroelastic response of the aftwings. To the extent of the author’s knowledge,the study of the modal dynamics of a highly flexi-ble, highly nonlinear joined-wing configuration hasnot been done before using experimental responsecorrelation. The first objective of the present workis hereby defined by the interest of validating themodal response of the aircraft through experimentalmodal analysis. The test vehicle was then subjectto a Ground Vibration Testing campaign.

It has been shown that localized structural non-linearities affect the nature of the modal responseof a conventional structure, even with the creationof resonance modes without linear counterpart [5].Nonlinear finite element tools are used to predictthe static behavior of the aircraft, but are not usedto update the FE model, given their increased com-putational cost.

The second objective of this study is then the useof linear tools to update the FE model, so that itmatches the experimental behavior of the test ve-hicle at small displacements. This was done usingboth the results from the static load tests performed

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by Almeida [6] and the modal dynamic results gath-ered by the author. The final result is a validatedFE model that fully and accurately characterizesthe static response and the modal dynamics of aflexible joined-wing UAV.

1.2. State of the Art in Ground Vibration Testing

Presently, two methods are commonly used to de-termine the modal response of aircraft structures:the Phase Separation Method (PSM) and the PhaseResonant Method (PRM).

The PSM presents the best compromise betweenresults and time consumption and is for this reasonthe main method applied in avant-garde testing. Itrelies on the curve-fitting of the frequency responsefunctions with the linear modal model. These func-tions are obtained by applying random or swept-sine excitations, using shakers, normally magnetic,to excite several modes contemporaneously [7], [8].

On the other hand, the time-consuming PRMwas the standard method used by major europeanaerospace laboratories before 2000 [8]. Today, itconstitutes a somewhat outdated procedure. De-spite that, it is still the most accurate and robustmethod for experimental modal analysis. This pro-cedure aims to vibrate the structure in a purely real,i.e., normal mode, by finding the best excitationforce pattern, a process called force appropriation.Consequently, when compared to the PSM it usessimpler post-processing algorithms, since the nor-mal modes can clearly be identified separately.

One major issue in ground vibration testing is themass loading of structures, caused by the attach-ment of sensors. Contact-less measurements canavoid this problem and also save time in the instal-lation of all the cabling required to acquire the sen-sor data. For instance, in the Airbus A380 GVTs,more than 25 kilometers of cabling were used [9].Some optical methods using laser Doppler vibrom-eters (LDV) are already applied in small, scaled air-craft [10] as well as in the automotive industry [9].

2. Theoretical Background

2.1. Modal Analysis Theory

Newton’s equation for a multi degree of freedom(MDOF) system is:

Mx(t) + Cx(t) + Kx(t) = p(t) (1)

where M, C and K are respectively the mass,damping and stiffness matrices, x(t), x(t) and x(t)are the displacement, velocity and acceleration vec-tors of the degrees of freedom (DOF) of the system,while p(t) corresponds to the force vector. Fromhere on, the notation is simplified: the time de-pendence is hidden and bold characters are used toidentify multidimensional entities.

Equation (1) can be rewritten into state-spaceform, using relations (2), yielding equation (3).{

z = [xTxT]T

z = [xTxT]T(2)

where the index T represents the transpose opera-tion.[

C MM 0

]{xx

}+

[K 00 −M

]{xx

}=

{p0

}(3)

A compact form can be achieved in equation (7),after writing:

A =

[C MM 0

](4)

B =

[K 00 −M

](5)

p′ =

{p0

}(6)

Az + Bz = p′ (7)

This is a first-order linear differential equation inx(t), meaning a family of solutions is found at:

z = Φeωt =

{φλφ

}eωt (8)

To find the free vibrations of the system, the forcevector is set to zero, i.e., p′ = 0, and the LaplaceTransform is then applied to equation (8):

[sA + B]Z(s) = 0 (9)

When pre-multiplying the equation by A−1, a re-arrangement leads to the appearance of the eigen-value problem written in equation (10), where s =−λ and I refers to the 2N × 2N identity matrix.

[A−1B− λ]Z(s) = 0 (10)

Since matrices A and B are real, the solutionswill either be real or come in complex conjugatepoles [11]. A real eigenvalue means that the sys-tem has an overdamped pole. Focusing on the casewhen modes come in complex conjugate pairs, giv-ing N pairs of both eigenvalues and eigenvectors.The poles with positive imaginary part are writtenas:

sr = −ζrwr + jwr√

1− ζ2r (11)

where the subscript r = 1, 2, ..., N indicates the vi-bration mode, wr the angular frequency and ζr thedamping coefficient. The angular frequency wr iscalled the natural frequency, and is often pre-sented in terms of cycles: fr = wr

2π . Each modehas then associated to it a frequency at which thestructure resonates.

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In most real-life application cases, knowledgeabout the damping matrix is, if existent, limited.For this reason, the study of the modal responseof a structure often begins with the calculationof the modes of the corresponding undamped sys-tem. These modes are called normal modes, withthe corresponding eigenvectors being designated asnormal mode shapes. These are unique for eachgiven structure at each set of boundary conditions.

Eliminating the damping matrix means that thefreely vibrating system is simplified as:

Mx + Kx = 0 (12)

In the general, non-proportionally damped case,the state-space formulation avoided the appearanceof a second order polynomial of s after the LaplaceTransform. In this particular case, it is not neces-sarily required to write the system into state-spacebecause the transform almost immediately gener-ates a linear eigenvalue problem. Applying theLaplace transform to equation (12) results in equa-tion (13), which can be rearranged as equation (14),when we assign λ = −s2.

[s2M + K]X(s) = 0 (13)

[M−1K− λI]X(s) = 0 (14)

The poles for the undamped system are thengiven by sr = ±

√−λr = ±j

√λr and come in com-

plex conjugate pairs, along the imaginary axis inthe Laplace domain, as a direct result of having nodamping. The undamped natural frequencies arethen extracted from the relation sr = jwr.

2.2. Signal Processing for Experimental ModalAnalysis

In Experimental Modal Analysis (EMA), the ex-perimental process is seen as a “black box”, where aknown input produces an output via a transfer func-tion. This function is the ratio of the Laplace trans-forms of the output y(t) over the input x(t) and is

usually represented by H(s) = Y (s)X(s) . The poles of

this system will give the frequencies at which thesystem vibrates freely, i.e., its normal frequencies.

Nonetheless, when studying normal modes it ismore practical to evaluate transfer functions in thefrequency range. Essentially, that means evaluat-ing the Laplace domain over the imaginary axis,where s = jω. The closer to the imaginary axis,the larger the peaks will appear in the FrequencyResponse Function (FRF), essentially defined as

H(f) = Y (f)X(f) .

With a MDOF system, H is a matrix that con-tains the receptance frequency responses, i.e., theratio between every individual input q and everyindividual output p. Each element of this matrix

can be written as a sum of every mode’s residues di-vided by the complex conjugate poles of the systemfor that mode. This fraction expansion is defined in(15) by the modal superposition equation [11].

Hpq(s) =

N∑r=1

Apq,rs− sr

+A∗pq,rs− s∗r

(15)

After acquiring the FRF of the system, the moderesidues are determined by curve-fitting. The mostclassical and simple way is to use the Quadra-ture method: near a resonance peak, the respectivemode is dominant, and the structure is imagined asan approximate SDOF system. The peak values inthe imaginary part of the receptance FRF are takenas residue components [11],[12]. When curve-fittingMDOF systems, the Rational Fraction Polynomial(RFP) method is applied. This method directlycurve-fits the empirical data to a rational fractionwhere the numerator and denominator polynomialsare determined and compared to the modal super-position equation. In the end of the curve-fit, theresidues are transformed into shape vectors, exploit-ing the orthogonality of the mode shapes [13].

2.3. Finite Element Model Updating

The mode shapes from analytical and experimen-tal modal analysis are correlated using the ModalAssurance Criteria (MAC). The MAC is expressedin equation (16) for two real vectors, correspondingto two normal eigenvectors. In essence, it is a mea-sure of the squared cosine of the angle between twovectors, which ultimately means that it determinesthe “degree of orthogonality” of those vectors, thusbeing able to determine the proximity between twomode shapes defined by its correspondent eigenvec-tors.

MAC(ψFEA, ψEMA) =|{ψFEA}T {ψEMA}|2

({ψFEA}T {ψFEA}) ({ψEMA}T {ψEMA})(16)

Statistical tools are further used to change theproperties of the elements in the model. A scat-ter parameter is used to define the confidence inthe experimental data and the user can choose thepercentage in which each parameter changes.

There exist two main families of FE update meth-ods: the direct and the iterative methods. The sec-ond is of particular interest in this application, sinceit is more suitable in the presence of measuring noise[14].

The iterative techniques used today in commer-cial software split into two approaches. One is theinverse eigensensitivity method (IESM), which wasoriginally proposed in 1974 by Collins et al [15].This technique uses modal data, such as eigenval-ues, eigenvectors and damping ratios to build the

3

error function. The other is the Response Func-tion Method (RFM), as proposed by Lin and Ewins[16], where the measured FRF are directly used formodel updating. In the absence of noise, the RFMworks better than IESM, while the latter performsbetter in the existence of noise, especially when alarge number of modes are being studied.

3. The Finite Element Model of the Joined-Wing Sensorcraft

The FE model of the JWSC was written in NAS-TRAN, using beam elements (CBEAM), quadri-lateral shell elements (CQUAD4), punctual masses(CONM1) and rigid body elements (RBE2). Thebeam elements were used to model the fuselage,the front wings and the extremity parts of the aftwings. Shell elements were used to model the longplate-like aft spars. The punctual mass elementswere used to model the mass of the ribs and skinsof the several wing sections, as well as the weightof instrumentation, both in the wings and insidethe fuselage. An additional mass element is used totrim the total mass and inertia values to those ob-tained during a bifilar pendulum test of the vehicle.The FE model can be seen in figure 1.

Figure 1: FE model of the JWSC - version a77h.

3.1. Early FE Model Static Results

At the beginning of this study, the FE modelversion was a77f. The first step was to redesignand remesh the aft wing, seeking an improvementon strain correlation. This modification can beseen in figure 2. Afterwards, it was updated us-ing the experimental results from the static loadtests of the vehicle, carried out by Almeida [6].Both the displacements and strain data were used inFEMTools, through the IESM method, to upgradethe redesigned FE model, originating version a77h.Only slight Young’s modulus changes occurred inthe structural elements: less than 6% from the base-line value used (Aluminum 7075, E=71.7Gpa).

Computational predictions are shown in figure 3using the early model a77f. Correlation is madewith the experimental load scenario, corresponding

Figure 2: Comparison between aft spar modellingon a77f(top) and a77h(bottom).

to the maximum load, equivalent to a 2.25g pull-up maneuver load. The linear FEA correspondsto predictions by the static linear solver (SOL101),whilst the nonlinear results are output by MSCNASTRAN Nonlinear Advanced Solver (SOL400).Several nonlinear path-following schemes were ex-perimented using the latter, but after running themodel with equivalent convergence criteria, resultswere quite similar. Newton’s iteration method wasthe fastest, making it the ultimate choice for thismodel. To give a practical example, Modified Rikstook more than 35 minutes to run, using the samehardware where the Newton Method runs in 12 min-utes.

4. Ground Vibration Testing of the Joined-Wing Sensorcraft

A preliminary study assessed the effect of struc-tural nonlinearities in the modal response of theaircraft. Different preloads were added to the struc-ture before extracting the normal modes, using theAdvanced Nonlinear Solver. The predictions gavestrength to the idea that, at low deformations,these modes would not change significantly. Conse-quently, at small excitation levels, the modal analy-sis could be kept within the linear realm, and com-mercial tools could be used.

A sequential testing and updating procedure wasundertaken. Firstly, the aircraft without aft wingswas tested and updated. Afterwards, the tail alonewas updated. Given the symmetry, only half of thetail was tested, meaning one of the aft wings, in-

4

cluding the joint with the front wing and the upperstrake part. Finally, the global vehicle was testedand updated. Roving impact tests were done us-ing fixed boundary conditions and single referenc-ing. When the model was closer to reality, a multi-reference shaker test was performed, under free-freeconditions, recreated using bungee chords to hangthe aircraft.

Eight modes were targeted in the last, most re-fined test. Pretesting procedures were performedin FEMTools using the Sensor Elimination usingMAC (SEAMAC) method. This algorithm teststhe removal of a sensor in each candidate degree offreedom. The removal that results in the minimumlargest off-diagonal term of MAC is then carriedout, and the process is repeated for the remainingcandidates [17].

Table 1 presents the resulting experimentalmodal parameters obtained after curve-fitting theFRF. These were obtained using the RFP methodinside the ME’Scope software.

Table 1: Experimental modal parameters extractedfrom shaker testing.

EMA Mode Frequency (Hz) Damping1 2.375 2.23·10-1

2 5.110 6.23·10-2

3 6.068 1.76·10-1

4 6.676 1.22·10-1

5 13.938 6.95·10-2

6 14.494 2.54·10-1

7 17.721 1.85·10-1

8 18.412 8.10·10-2

5. Update of the Finite Element Model usingModal Data

In this study, the post-processing of the FRF wasdone through curve fitting and the resulting exper-imental modal parameters are used to update theFE models. A modern version of the IESM was thusused with FEMTools’ own algorithm. At each step,an eigenmodes run is performed, using MSC NAS-TRAN Lanczos method as the external FE solver.A sensitivity analysis is performed, calculating thederivatives of each eigenvalue and each eigenvectorfor each of the selected parameters. FE and ex-perimental mode shapes are paired using the MACcriteria and a residue vector is built on the discrep-ancy between them. A least-squares approach isused to minimize the residual, solving for new pa-rameters, until convergence is met for the responseschosen.

In what concerns statics, the a77h model alreadypresented decent correlation with experimental re-sults. This meant that changing stiffness values was

highly avoided. In order for the modal data to cor-relate, the lumped masses used to model the ribsand skins of the wings were chosen as parameters,and were allowed to change. The results showedthat, in the degrees of freedom where the chang-ing of the mass parameter showed higher sensitivity,there was a rearrangement of the directions in whichthe lumped masses acted. These small changes inthe mass matrix of the system ultimately refinedthe correlation between the EMA and FEA modeshapes. The absolute maximum parameter changewas below 20%.

A comparison between pre and post-updatemodal parameters is given by table 2.

Table 2: Comparison of modal parameters beforeand after the update of the global model usingshaker test results. Index ♦ refers to quantities cal-culated after the update.

Mode fEMA (Hz) fFEA (Hz) f♦FEA (Hz)1 2.375 2.607 2.6012 5.110 3.966 4.0063 6.068 6.386 6.4574 6.676 7.149 7.2035 13.938 13.451 13.6906 14.494 13.908 13.8607 17.721 17.623 17.8318 18.412 15.216 15.349

Mode ∆f (%) ∆f♦ (%)1 9.80 9.532 -22.40 -21.623 5.25 6.414 7.09 7.895 -3.49 -1.786 -4.04 -4.377 -0.55 0.628 -17.36 -16.64

Mode MAC (%) MAC♦ (%)1 77.6 77.82 85.3 85.53 88.2 87.64 87.7 87.65 69.5 71.26 82.2 85.17 89.2 91.28 62.4 67.2

5

0 0.2 0.4 0.6 0.8 10

5

10

15

20

25

Non dimensional forward wing span

Relativeerror(%

)

Forward wing Z displacement - 2.25g

0.00

0.10

0.20

0.30

0.40

Zco

ord

inate

(m)

FEA Linear

FEA Non Linear

Experimental

Error - FEA NL

0 0.2 0.4 0.6 0.8 10

10

20

30

Non dimensional aft wing spar span

Relativeerror(%

)

Aft wing Z displacement - 2.25g

0.00

0.05

0.10

0.15

0.20

Zco

ord

inate

(m)

FEA Linear

FEA Non Linear

Experimental

Error - FEA NL

0 0.2 0.4 0.6 0.8 14

6

8

10

12

Forward wing spar (m)

Relativeerror(%

)Forward wing beam bending strain - 2.25g

−3.00

−2.00

−1.00

·10−3

Strainε

FEA Linear

FEA Non linear

Experimental

Error - FEA NL

0 0.2 0.4 0.6 0.8 10

200

400

600

Aft wing spar (m)

Relativeerror(%

)

Aft leading edge beam bending strain - 2.25g

−2.00

−1.00

0.00

1.00·10−3

Strainε

FEA Linear

FEA Non Linear

Experimental

Error - FEA NL

0 0.2 0.4 0.6 0.8 10

2

4

6

8

10

12

Non dimensional forward wing spar span

Relativeerror(%

)

Streamwise forward wing twist angle - 2.25g

−12.00

−10.00

−8.00

−6.00

−4.00

−2.00

0.00

Strea

mwisetw

istangle

(◦)

FEA Linear

FEA Non Linear

Experimental

Error - FEA NL

0 0.2 0.4 0.6 0.8 10

10

20

30

40

Non dimensional aft wing spar span

Relativeerror(%

)

Streamwise aft wing twist angle - 2.25g

−10.00

−5.00

0.00

5.00

Strea

mwisetw

istangle

(◦)

FEA Linear

FEA Non Linear

Experimental

Error - FEA NL

Figure 3: Static results for the maximum load using model a77f.

6

(a) Mode 1 - 2.786Hz (b) Mode 2 - 4.164Hz

(c) Mode 3 - 6.915Hz (d) Mode 4 - 7.574Hz

(e) Mode 5 - 13.599Hz (f) Mode 6 - 14.618Hz

(g) Mode 7 - 15.634Hz (h) Mode 8 - 18.863Hz

(i) Mode 9 - 19.454Hz (j) Mode 10 - 29.896Hz

Figure 4: Normal mode shapes for the first ten flexible modes predicted by model a77i.

7

0 0.2 0.4 0.6 0.8 10

5

10

15

20

25

Non dimensional forward wing span

Relativeerror(%

)

Forward wing Z displacement - 2.25g

0.00

0.10

0.20

0.30

0.40

Zco

ord

inate

(m)

FEA Linear

FEA Non Linear

Experimental

Error - FEA NL

0 0.2 0.4 0.6 0.8 10

20

40

60

Non dimensional aft wing spar span

Relativeerror(%

)

Aft wing Z displacement - 2.25g

0.00

0.05

0.10

0.15

0.20

Zco

ord

inate

(m)

FEA Linear

FEA Non Linear

Experimental

Error - FEA NL

0 0.2 0.4 0.6 0.8 10

2

4

6

Forward wing spar (m)

Relativeerror(%

)

Forward wing beam bending strain - 2.25g

−3.00

−2.00

−1.00

·10−3

Strainε

FEA Linear

FEA Non linear

Experimental

Error - FEA NL

0 0.2 0.4 0.6 0.8 10

10

20

30

Aft wing spar (m)

Relativeerror(%

)

Aft leading edge beam bending strain - 2.25g

−1.50

−1.00

−0.50

0.00

0.50

1.00·10−3

Strainε

FEA Linear

FEA Non Linear

Experimental

Error - FEA NL

0 0.2 0.4 0.6 0.8 10

2

4

6

8

10

Non dimensional forward wing spar span

Relativeerror(%

)

Streamwise forward wing twist angle - 2.25g

−12.00

−10.00

−8.00

−6.00

−4.00

−2.00

0.00

Strea

mwisetw

istangle

(◦)

FEA Linear

FEA Non Linear

Experimental

Error - FEA NL

0 0.2 0.4 0.6 0.8 10

5

10

15

Non dimensional aft wing spar span

Relativeerror(%

)

Streamwise aft wing twist angle - 2.25g

−10.00

−5.00

0.00

5.00

Strea

mwisetw

istangle

(◦)

FEA Linear

FEA Non Linear

Experimental

Error - FEA NL

Figure 5: Static results for the maximum load using model a77i.

8

The MAC values give confidence in the FEmodel. During the test, some mass changes wereperformed, namely to facilitate the attachment ofbungee chords and the placement of sensors intostiff parts of the structure. The updated model isre-trimmed to the mass and inertia values of the bi-filar pendulum test and is finally relabeled as a77i.This ultimate model can not be correlated withthe experimental results, given the mass changes.Nonetheless, predictions for the first ten flexiblenormal mode shapes are displayed in figure 4, forfree free boundary conditions. As a last exercise,the static predictions for this model are correlatedwith the experimental results from the static loadtests, as can be seen in figure 5. In order to under-stand the progress of the FE model, an interestingcomparison can be made with the results from theearly model a77f, depicted in figure 3.

6. Conclusions

The present work furnishes a static and dy-namic characterization of a scaled elastic joined-wing UAV. It has done so by firstly correlating thestatic measurements already available at CfAR, andsecondly by planning and conducting a full groundvibration testing campaign to correlate the modaldynamics of the aircraft.

Substantial improvements were made on the FEmodel, from a77f to a77i. The actual model nowcompetently mirrors both the static and dynamicbehavior of the aircraft, and that is the biggest prac-tical achievement of this work. On the theoreticalside, it is interesting to note that, even though thestructure can display nonlinear behavior, the actualupdate process was successful using linear tools.

In this paper, it was not possible to go in detailabout some of the aspects in the work. A couple ofthose merit here a brief remark. One regards thestatic results for model a77h, that could not fit inthese pages. It must be noted that most of the im-provement done in the correlation of static resultsfrom the early a77f model was due to the remodel-ing, remeshing and initial update. This is the sameas to say that, as far as the static characterizationis concerned, more was achieved from model a77fto model a77h than from model a77h to a77i. Infact, the omitted static results for the intermediatemodel are almost identical to those depicted for thefinal model. Another important topic to remark isthat the updating process was instrumental in un-veiling details about the aircraft structure. Duringthe tailless model update, it was discovered that thecarbon fiber tail boom is significantly lighter and,in general, less stiff than what was initially believedand modeled. This particular characteristic couldnot have been determined during the static load

tests, since those focused on the wings.When updating the model using shaker results,

the model was already predicting the aircraft’s be-havior with such accuracy that the procedure re-sembles a trade-off between resonant frequency fig-ures and MAC values, with the errors being shiftedbetween them without any significant improvementin the global sense. Even though the shaker testswere not indispensable to the model updating strat-egy implemented, they provided the means to vi-sualize and validate the mode shapes of a flexiblejoined-wing structure. By doing so, the shaker testshelped to demonstrate the assumption that a struc-ture that presents nonlinear behavior can still beupdated using modal data at small deformations,using linear FEA tools.

Acknowledgements

Given that this work summarizes the Master’sthesis research done at CfAR, it is only fair thatsome of the people involved are acknowledged fortheir support.

My first words must go to the director of CfAR,my supervisor Doctor Afzal Suleman. To him is duethe wonderful learning experience I lived at the Uni-versity of Victoria Center for Aerospace Research,in Canada. For that, I am utterly grateful.

Secondly, I would like to thank the team at CfAR.In particular, Stephen Warwick, for walking methrough the early stages of the project and gettingme up to speed. I am truly grateful for his helpfulinsight on how to steer a testing campaign that wasmy own responsibility. Also, a thank you note mustbe addressed to Doctor Jenner Richards, a pioneerof the sensorcraft project, for all the inspiration andknowledge shared.

Lastly, as the work represents the ending of a cy-cle in my academic life, I would like to thank myparents. Thank you very much for always support-ing my every endeavor, providing me with an un-shakable sense of confidence and love.

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