54th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference
April 8-11, 2013, Boston, Massachusetts
The use of MDO and Advanced Manufacturing to
Demonstrate Rapid, Agile Construction of a Mission
Optimized UAV
Martin Muir1
EADS Innovation Works, Filton, Bristol, England
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Carl Muldal2, Edward Kolb2, Graham Robertson2, Aaron Parkinson2,
Osvaldo M. Querin3, Robert W. Hewson4 and Vassili V. Toropov5
University of Leeds, Leeds, West Yorkshire, England
Broad spectrum mission capability for minimal cost is a critical objective for many
systems undergoing design and development for use in modern theaters. Through utilization
of combinatory and mutually beneficial technologies, a technique has been developed and
demonstrated which allows for optimized mission performance characteristics to be
achievable and deployable at minimal cost and vastly reduced lead time. Using typical
surveillance mission parameters and allowing for variation in deployment zones and
atmospheric conditions, multi-method modeling was used to simulate aerodynamic
performance characteristics of a NACA6414 based aerofoil. Optimized structural layout
was then determined accounting for both structural load cases and the delicate complexities
of selected laser sintering and electron beam melting approaches to additive manufacturing.
Using the derived structural layout, an iterative aerodynamic optimization using mesh
deformation and maximization of elliptical lift distribution was performed, culminating in a
design validation analysis and comparison. Export, correction and parameterization of the
final design was compiled and delivered to a pre-selected additive manufacturing process
ready for automated build. The developed process represents an automated MDO design
and manufacturing methodology capable of delivering a uniquely optimized mission
capability at a fraction of the time and cost ordinarily required to redevelop already
supplied hardware for a bespoke purpose. Furthermore, the process demonstrates how
through the combined use of additive manufacturing and structural topology optimization
benefits greater than the sum of their parts can be achievable through suitable design
parameterization.
1
Research Engineer, Technology Capability Centre 2 - Metallic Technologies, EADS Innovation Works UK, Airbus
Operations, Filton, Bristol, BS997AR, England. AIAA Member. E-mail: martin.muir@eads.com
2
Alumni Student, School of Mechanical Engineering, University of Leeds, West Yorkshire, LS2 9JT, UK.
3
Associate Professor, School of Mechanical Engineering, University of Leeds, West Yorkshire, LS2 9JT, UK.
AIAA Senior Member. E-mail: o.m.querin@leeds.ac.uk
4
Lecturer, School of Mechanical Engineering, University of Leeds, West Yorkshire, LS2 9JT, UK. E-mail:
r.w.hewson@leeds.ac.uk
5
Professor of Aerospace and Structural Engineering, School of Civil Engineering and School of Mechanical
Engineering, University of Leeds, West Yorkshire, LS2 9JT, UK. AIAA Associate Fellow. E-mail:
v.v.toropov@leeds.ac.uk
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Copyright © 2013 by Vassili Toropov, Martin Muir, Osvaldo Querin, Robert Hewson. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission.
AIAA 2013-1675
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Nomenclature
α
Ω
Cp
CL
CD
c
L/D
W
ALM
CFD
COTS
DMLS
EMB
FEA
FEM
FSI
GSS
HALE
HFA
LFA
MFAA
SLS
TO
UAS
UAV
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
=
angle of attack
angle of twist
pressure coefficient
lift coefficient
drag coefficient
chord
lift to drag ratio
weight
additive layer manufacturing
computational fluid dynamics
commercial off the shelf
direct metal laser sintering
electron beam melting
finite element analysis
finite element method/mesh
fluid structure interface
grid sensitivity study
high altitude long endurance
high fidelity approach
low fidelity approach
multi-fidelity aerodynamic optimization
selective laser sintering
topology optimization
unmanned air system
unmanned air vehicle
I. Introduction
key growth area for aerospace in the last decade has been in the field of unmanned aerial vehicles (UAVs)1
Pilotless, and either autonomous or controlled remotely, they are capable of performing missions deemed to
hazardous or too costly for a piloted aircraft. The capabilities of UAVs have evolved rapidly over the past 10 years
with myriad different systems now capable of performing many bespoke missions in place of their piloted
brethren2. Indeed the total published spending on the UAVs in 2011 was approximated at $5.9 billion (a figure set to
double over the next decade3), with estimates that UAV’s will become part of everyday air traffic operations in the
next decade4. Beyond military application there are an ever increasing number of civilian applications where
UAV’s can and have been introduced these range from law enforcement, and crop monitoring, to soil erosion and
exploration of volcanic environments 5. The size and complexity of any Unmanned Air System(s) (UAS) is
related directly to its predicted mission requirements, which can range from High Altitude Long Endurance
(HALE) to Nano Air Vehicle6 with most systems being specific to a particular type of mission. As with most
systems, complexity of design generally increases base cost, and so, smaller UAVs tend to be less complex and
therefore less costly to purchase and operate. Conversely, these low cost solutions are often less versatile as a
result7.
However, with forces (military and civilian) facing budgetary cuts, many existing UAS’ are now being tasked
to perform roles for which their designers never intended; the result of this re-tasking is a twofold compromise in
reduced performance, and/or increasing cost8.
In order to provide a solution to the twin challenges of versatility and the cost of design complexity, EADS
Innovation Works UK in association with the University of Leeds has set about the creation of a mission optimized
UAV capable of being rapidly redesigned and remanufactured at limited cost. This research details the process
from conception to manufacture, of a bespoke, but highly modular airframe designed using aerodynamic,
structural and manufacturing optimization techniques. Foregoing conventional design techniques in favor
of exploring largely free design space, structural optimization techniques are used to determine internal
structural layouts and required skin thicknesses based on aerodynamic loadings. In combination with
novel design techniques, further technology enablers are used during the manufacturing phase in order to
allow manufacturing of the inherently complex features associated with MDO structures. The construction
of the airframe is completed entirely by Additive Layer Manufacturing (ALM) at EADS IWs production
A
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facilities, with final assembly completed using a combination of commercial off the shelf (COTS)
components and EADS technology.
The UAV was statically demonstrated at the Farnborough
International Airshow in July 2012.
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II. Problem Statement
A. Design Purpose
The principal aim of the UAV project was to determine if, through the combined use of multiple emergent
technologies, a highly customizable, previously very expensive product or system could be offered at a relatively
low cost and with high levels of versatility.
Several products were considered, however, with UAVs showing a 1000% fold growth in funding9 over the past
decade and with some 7500 UAVs of various types now in service with all branches of the US military, a product
offering high levels of versatility, with no compromise in mission performance was deemed to have significant
market appeal. There are several methods by with improved aircraft mission performance can be achieved10;
however, while many of these system can offer performance increases, they do so through added complexity in
either the design or operational phases of the mission leading to increased cost or reduction in performance during
other phases. With these goals and previous compromises acknowledged, EADS Innovation Works identified a
number of key technologies the use of which might allow true mission versatility to be achieved principally through
agile manufacturing and optimization.
B. Mission and Design Requirements
The primary mission for the UAV is to act as an observation platform intended to give the civilian/military
operator a significant increase in local situational awareness. Additional requirements (both optional and
compulsory) are stated below, the most important of which are the manufacturing constraints impose by use of
ALM.
Objective
Increased situational awareness for ground personnel.
Size
Mission portable, smaller than 1500×650×250mm, transportable and operable by a single
individual (low mass).
Operation
Quiet, non-disposable (must endure multiple harsh landings) and capable of rapid re-fuel
and re-use. Minimum endurance in excess of 30 minutes with maximum deployment time
less than 90 seconds. No tools for assembly.
Ergonomics
Simple to control. Easy to deploy/launch, (no runways/ ramps, tube holder/projector
acceptable).
Communication
Secure, high definition, continuous communication system.
Manufacturing
The vehicle must be capable of being rapidly constructed using EADS Innovation Works
UK. Additive Layer Manufacturing facilities, either in parts for bondingless assembly, or
as a complete item.
C. Preliminary Design – Manufacturing Constraints
Chief amongst the EADS IW design requirements were the items relating to portability, flexibility, performance
and manufacturing, with the latter forming the major geometric constraints for the project. As ALM (specifically
powder bed ALM) is fixed as the primary method of construction, the geometric restrictions of the available build
platforms () formed the major geometric constraints for any and all sections of the aircraft structure.
Ultimately, material choice would dictate final geometric constraints, but at this early stage, provisions were made
for the manufacture of the UAV from a number of materials.
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Copyright © 2013 by Vassili Toropov, Martin Muir, Osvaldo Querin, Robert Hewson. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission.
Table 1 Geometric and material manufacturing constraints for PBF ALM facilities at EADS IW in 2010
PB Fusion Capability
Material
Types
ARCAM A2
EOS M270
EOS P385
Metallic
Metallic
Polymer
Max xy
Dimensions
(mm)
250*
250*
320*
Max z
Dimension
(mm)
250*
250*
620*
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III. Preliminary Problem Formulation
A. Project Design Drivers and Information Flow-process
As with many aircraft design studies, once the mission and design requirements were understood, identification
and parallelization of major tasks formed the first stage of the problem formulation. However, contrary to many
conventional aircraft design problems, the rules and constraints related to the novel method of manufacture for the
aircraft were of particular importance in the first instance. Once constraints and complexities for manufacture were
identified and parameterized, four major and somewhat intertwined threads for design were identified and where
possible, parallelized. The flowchart shown in identifies the major themes and their associated large scale tasks.
Figure 1 Information flowchart for UAV design project identifying major themes and outputs
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B. Preliminary Design - Generation of Initial Design Inputs Through Aircraft Sizing
Based largely on the requirement for portability, preliminary sizing was completed using conventional
approaches11 at an approximated 3kg maximum take-off weight (MTOW). Using performance and sizing tables
based on idealized values for aircraft wing loading (Figure 2) a wing area of approximately 0.3m2 was found to give
a favorable wing loading of 105Nm3, thereby satisfying the base requirements for lift (
) in all flight
conditions.
Figure 2 Vehicle sizing diagarm showing wing loadings and power requirements at determined values for C L
A NACA 6414 Aerofoil was selected as the primary chord profile based on the results of a tradeoff study
measuring aerodynamic performance against required internal system space. The 6414 chord is used throughout the
entire semi-span, tapering at a ratio of 0.7 toward the tip. In order to accommodate taper without sweep and to
provide for the inclusion for a single (flaperon) control surface on the trailing edge, the NACA 6414 chord is scaled
at 4 locations in order to provide the transition points and give the wing its final profile/layout. The single TE
flaperon is sized to approximately 70% of semi-span and represents 30% of the chord in that location in order to
provide the low speed lift required for approach and controlled landing, see Figure 3.
Wing area (S)
Span (b)
Aspect ratio (AR)
Taper ratio
Sweep angle C/4
Dihedral angle
Root Chord Cr
Tip Chord Ct
0.281m2
1.30 m
6.0
0.70
0.0°
2.0°
0.254 m
0.178 m
Figure 3 Wing plan-form showing changes in chord sizing and overall wing layout
In order to maximize internal fuselage space and aid versatility, a wing mounted pusher propeller was selected
for inclusion into the design layout. The selection of a pusher propeller, while advantageous for versatility does not
come without compromise in the form of empennage. Several solutions exist, such as a low mounted fuselage boom
or pylon mounted propeller, though both of these analogues come with significant aerodynamic penalties12. After
simple power calculations it was determined that the inclusion of a TE twin boom attachment for empennage could
be accommodated if sufficient clearance were allowed for a 7” two-bladed propeller. The selected attachment was
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then mated to an inverted V-tail design after critical analysis of aerodynamic performance, manufacturing
complexity and operational considerations ruled out the inclusion of a twin rudder H-tail. The preliminary layout is
shown in Figure 4.
Figure 4 Proposed internal layout for the UAV systems
C. Preliminary Design – Systems Integration
In tandem with primary sizing, power and control systems were assessed in accordance with estimated MTOW,
aircraft layout, and design requirements. The design requirements formed the initial dictates for the main system
architectures in areas such power system type and navigation/communications, while the results of primary sizing
helped determine both maximum payload requirements, but also control surfaces numeracy and likely power
requirements.
The initial design requirements stated that the aircraft should be both quiet during operation, capable of rapid
refuel and re-launch and form a stable observation platform during primary operations. Additionally, it was known
that regardless of selected propulsion system, an electrical system would be required to power communication and
controls. In an effort to simplify the design an all-electric propulsion system using motors, speed controllers and
batteries selected as the primary power source for both systems and propulsion.
Several established and novel battery technologies currently exist, the most common (for this application) being
Lithium-Polymer (LiPo) With a high energy density for a commercial battery and available in a plethora of shapes
and sizes, its inclusion into the design was almost guaranteed. One of two emergent technologies, Lithium Sulphur
(LiS) batteries are possessing of higher specific energy (though similar energy density) than more conventional LiPo
batteries. Though still in development, provision for their later inclusion is not difficult and they could be
considered in the design as a means to increase platform endurance. The second emergent technology is that of
hydrogen fuel cells (HFCs) or more specifically for this application, Micro Solid Oxide Fuel Cells (mSOFCs).
mSOFCs have a tremendous energy density far surpassing that of LiPo and even LiS batteries, indeed research has
demonstrated that when combined with conventional battery technologies, the endurance of a comparable sized
UAS can be tripled13 through use of the technology. However, the relatively low TRL of mSOFCs, coupled with a
number of operational difficulties relating to temperature and fragility meant would not be the primary means of
propulsion for the prototype.
It is intended that the UAV, once launched, will be largely autonomous, maintaining a loiter pattern
approximately concentric to the control system held by the UAV operator. As such, the aircraft will perform the
complete mission profile under the control of an autopilot such as the AdruPilotMega2.0 unless overridden and retasked by the controller. The selected control system uses both GPS and IMU to calculate its position and attitude
and uses a barometric pressure gauge in order to determine altitude14. The control system allows for full guidance
and control surface manipulation (of all 4 primary control surfaces) in all phases of flight, maintaining a continuous
two-way communication with the operator, who can dynamically re-task if required. In order to maintain
communication over short distances, the surveillance platform will utilize an Xbee Pro digital wireless transmitter
allowing a maximum communication range of approximately 1.5km while maintaining maximum signal bandwidth.
If longer range is required, the APM2/Xbee15 package can both receive and transmit data using the GSM network
whenever in range16.
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D. Preliminary Design – Structural Loading
In order to determine the appropriate conditions required to elicit values for maximum wing loading, a V-N
diagram of the UAV flight envelope was constructed and evaluated (Figure 5) Interrogation of the charts revealed
that a maximum wing loading (n=5.25) occurred when attempting to stabilize the aircraft from induced gust
(regulatory requirement) whilst performing a high velocity dive. The results are comparable to other aircraft 17.
Figure 5 A velocity/loading diagram showing climb and descent gust loading and flight phase velocities
In order to accurately analyze the structural requirements at multiple airfoil locations, an aerodynamic analysis of
the identified extreme loads cases found within the flight envelope had to first be performed18. Problematically,
even at low velocities and with relatively simplistic airfoil geometry, delineation of pressure distributions around in
flight airfoils can generally only be approximated using computational techniques such as CFD.
E. Generation of Optimization Inputs - Multi-Fidelity Aerodynamic Analysis (MFAA)
CFD analysis techniques, particularly in complex flow domains are resource intensive, both in terms of initial
model parameterization and the required computational time for convergence 19. Problematically, pressure
distributions obtainable only via CFD were required at an early stage of aircraft design in order to determine the
inputs for structural optimization. A simplified means of obtaining the primary loading data was required.
A low-fidelity approach (LFA) using a combination Vortex Lattice (VLM) and Panel Methods (PM) were
developed in order to assess the emergent aerodynamic performance of the proposed aircraft20. Though simplistic,
the techniques are ideal precursors to full aerodynamic analysis, as they allow rapid parameterization and
computational analysis for the determination of many aerodynamic performance indicators prior to full scale CFD.
While there are known difficulties in the application of both techniques21, the simplistic airfoil profiles and low
speed flight domain of the proposed UAV allow a relatively accurate analysis if the flow domain to be achieved.
Though useful in the initial stages and instrumental in achieving the design goals for the project, the method of
analysis used by the LFA was deemed insufficient to accurately analyze the flow domain when subjected to the
small perturbations as performed during the aerodynamic optimization. As such, a high-fidelity approach (HFA)
was developed in parallel with the LFA, using more robust and accurate analysis techniques. The HFA makes use
of a pressure based solver in order to calculate RANS equations ate each elemental location within a finite volume.
Ansys ICEM was the designated pre-processor for the HFA providing a direct link between geometry and analysis22
while offering vast geometric import versatility, and allowances for substantial mesh variation.
Due to computational limits, only the wing of the aircraft was modeled and analyzed using grid study of the
HFA. In addition a symmetry boundary condition along the center of the aircraft (Figure 6) was employed thereby
halving the required element count and offering a correlative decrease in required simulation time. In order to
alleviate problems associated with bad geometric profiles and grid generation, A semi-automated routine was
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written in order to examine and correct imported geometry prior to generation of the FEM. This code was intended
to address perceived downstream problems related to the outputs from the aerodynamic optimizer.
Figure 6 ICEM volume mesh for the proposed UAV
Within the pre-processor, a flow domain was established mimicking the behavior of as the transportable wind
tunnel7 which, for a maximum chord length of 0.254m, demonstrates a 3m separation from the inlet, upper and
lower boundaries with the outlet positioned 5m from the leading edge. The width of the flow domain was set at 5m.
At low velocities, the conditions represent an acceptable separation, sufficient to negate any adverse effects
associated with reversed/reflected flow to the wing23.
Solutions were obtained using a Reynolds averaged Navier Stokes (RANS) solver with a combined Spalart –
Allmaras turbulence model24 the selected solver and post processor in the form of ANSYS Fluent provided an
accurate, time effective solution and was accepted as the principal method of operation for the HFA.
IV. Results of Preliminary Design and Analysis
A. Aircraft Sizing and Layout
Figure 7 Design freeze showing vehicle architechture prior to optimisation
Table 2 Aircraft geometric details
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Table 3 Aircraft geometric details
Wing span
Weight (max)
1.6m
3kg
Length
Payload
1.2m
0.5kg
The original (non-optimized) aircraft layout is shown in Figure 7 with Table 2 showing the major geometric
measurements.
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B. Low Fidelity Results
The outputs of the LFA provide input data for several aspects of the aircraft design process including,
aerodynamic analysis, aircraft stability, power and system sizing and most importantly, structural analysis and
optimization.
C. Low Fidelity Results – Aerodynamic Analysis
Prior to the selection of the NACA 6414, the LFA was performed using VLM, however the introduction of
airfoil camber and a requirement to more clearly understand the pressure distribution over the airfoil, led to the use
of the PM for all subsequent analyses. In order to provide bespoke data for two distinct aspects of the design
process either a complete aircraft model or the wing in isolation were assessed for aerodynamic performance.
Analysis of aircraft performance and in flight stability was performed using a complete aircraft model. A
combination of the PM/VLMs were applied subjectively to various aspects of the aircraft model using the XFLR5
aerodynamic simulation tool. Due to the inherent difficulties of representative modeling of the fuselage-wing
interaction PM was applied to the wing and fuselage with VLM applied to (symmetric) empennage, thereby
providing a timely and efficient solution path.
The LFA aerodynamic analysis first extracted and compared the derived values for CL and CD over a range
of wing incidence angles (i) and at varying angles of attack (α) (Figure 8) finding that a wing incidence angle
of 2° at an angle of attack of 0° produced the best CL/CD ratio at operational speeds of ~18ms-1. (
)
Figure 8 L/D ratios at varying angles of attack (left) and flow patterns at high velocities/angle of attack (right)
Table 4 Wing only figures from the LFA
CL
0.422
L/D
12.78
Α
0°
CD
0.033
V
18ms-1
Flap
0°
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D. Low Fidelity Results – Stability Analysis
The aircraft stability analysis (ASA) used the outputs of preliminary sizing for component locations and weights
along with control surface definitions for locations, sizing and maximum deflection25. The results of the ASA
showed that while the aircraft is largely dynamically stable, a -2° change in the V-Tail incidence angle was required
to aid longitudinal stability over both the phugoid and short period modes. However, the aircraft has slight lateral
instability in the spiral mode and would require special programming or control input in order to counteract the
effects.
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E. Low Fidelity Results – Power Sizing
Having found the lift and drag coefficients for the UAV at cruise and take off through use of the LFA, it was
subsequently possible to calculate the power requirements and system dependencies. Using (1 Cruise power
was calculated using to a value of ~66W using the values in
and an assumption of 80% propeller efficiency
√
(
⁄
(1)
√ ⁄
)
⁄
(
⁄
(2)
)
Equation (2) was then used to determine the maximum power required during climb conditions, assuming a climb
rate of 1ms-1, the power required is ~73W giving a time to climb of approximately 10 minutes.
A power required during climb (2) of 72 watts was calculated. When combined with mission endurance of 2
hours, the resultant total energy required is approximately 700KJ. If Li-Po batteries were to be used in the aircraft
two 9000mAh batteries would be required with a combined mass of 0.75kg.
Using a mSOFC system, 22 cells would be placed in a series stack to create an 11v PSU. With seven of these
stacks the system would be able to produce 77W which is sufficient to supply the UAV with enough power for
sustained cruise flight. A battery would be required for takeoff and climb as seen in the system designed by
Turner15. As 72W is required during ascent, a single 1300mAh Li-Po battery would be used, which if fully charged,
would provide enough power for 10 minutes at maximum output, sufficient for climbing flight. Use of an mSOFC
based power system would reduce the system weight by almost 30% while increasing endurance by 150%.
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F. Low Fidelity Results - Structural Analysis Inputs
Induction of gust loading conditions in-line with regulatory safety conditions led to a load factor of ~ 5.24 times
the initial aircraft weight. In order to create pressure distributions similar to those induced by the regulatory
conditions, simulations were ran using combinations of higher angles of attack and greater velocity (Figure 8). The
resultant pressure distributions obtained from the solver were detailed enough to provide the structural inputs
required for the first stage of the optimization routine.
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G. High Fidelity Results – Aerodynamic Analysis
Forming part of the MFAA, the HFA was created to establish a baseline for aerodynamic performance, and
provide initial inputs and for the aerodynamic optimization routine. In addition, the results of the HFA were used to
validate the accuracy of the LFA through comparison of CL data.
Figure 9 – Volume and surface mesh as used in the GSS of the HFA
Prior to direct use, a series of grid studies were undertaken in order to demonstrate mesh independence for the
aerodynamic analysis. The grid sensitivity study (GSS) utilized only the aircraft wing (Figure 9), 6 grids of varying
density were created using a base 2 octree methodology with maximum computational capability defined by the
upper system boundary. Testing with horizontal flow revealed that the system limits were reached prior to
demonstration of grid independence. Fortunately, the study always intended to use a sub-independent grid for
analysis in order to decrease the required computational time. Once completed, correction/scaling factors would
then be used to directly relate the results to the more accurate study. Figure 10 shows that grids densities in the
region 0.5m-4m cells demonstrate similar aerodynamic performance indicators despite an almost 30 times increase
in required CPU time. As such, a grid density close to 0.5m cells was chosen in order to allow for rapid analysis and
optimization.
Figure 10 Mesh density vs CL (Left) and α vs CL for both the LFA and the HFA
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A comparison of the UAV lift curve slopes (wing only) produced by both FLUENT (0.5m cell count) and
XFLR5 is shown in Figure 10. The data compares well showing a zero lift angle of approximately -6°. up
until the point of at which the maximum capabilities of the LFA were reached (~10 degrees) The results
also demonstrate that the stall angle of the UAV is 18° with a maximum lift coefficient of 1.5 without the
flaperons deployed. It was calculated that at a mass of 3kg, the UAV will stall at 10.5m/s, giving it a stall margin of
7.5 m/s.
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V. Aerodynamic Optimization Problem Formulation
A. Aerodynamic Optimization (AO)
Aerodynamic optimization (in one form or another) is routinely used as part of the design process for the vast
majority of large aircraft programs. The use of aerodynamic optimization on smaller products such as semidisposable UAVs is less common due to the perceived benefits being outweighed by increased design cost and
manufacturing complexity.
The objective of the AO was to maximize the L/D ratio of the UAV airfoil within the limits of a number of
constraints, without the incurrence of additional costs in either design time or manufacture. The principal design
constraints for development of the analysis toolset related to the minimum chord thickness as defined by systems
inclusions, and the wing semi-span as defined by geometric manufacturing constraints.
In order to elicit an improvement in aerodynamic performance without direct alteration of airfoil chord profiles,
research suggested that alteration of lift superposition and wing twist could be used to optimized the lift distribution
of the airfoil toward that of an elliptical profile.
B. Aerodynamic Optimization Routine (AOR)
In order to reduce design time and cost, an automated method of aerodynamic optimization was required.
Using baseline input data taken directly from the results of the GSS, followed by carefully controlled mesh
deformations26, a series of scripted parallel processing operations were used to obtain data spreads for a range
of deformations using a series of batch runs. The data was then used to determine the relationship between
design variables and responses in order to create the final optimization study. The flow-process for the AOR is
depicted in Figure 11
Figure 11 Aerodynamic optimisation flowcharts
C. Mesh Parameterization and Deformation
In order to induce suitable variations in wing twist, Matlab was used to numerically deform the
aerodynamic mesh as outputted from the GSS. Twist was induced into the elements of the grid through an
angle of incidence which varied from root to tip. The angular distortion was applied according to a weighting
factor (Φ) varying from a value of 1 (full deformation) to 0 (no deformation). Figure 12 shows how the angular
change was applied around the airfoil quarter chord point (C0). The circle of radius Ri (Φ=1) and is slightly
larger than the chord length of the airfoil. The radius Rii corresponds to the nearest boundary within the flow
domain where the deformation must return to initial values.
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Figure 12 Method of mesh distortion for 2D aerofoil co-ordinates
3D Grid deformation was carried out using cosine (3) and exponential (4) decay and smoothed using radial basis
function (RBF)
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(3)
(4)
The deformation as a result of the use of both functions can be observed in Figure 13 and Figure 14 with both
images showing the maximum allowable twist through imposition of their respective rules.
Figure 13 Wing distortion under 15° cosine decay
Figure 14 Wing distortion under 15° exponential decay
CFD simulation was performed in Fluent using first level automation of the process in order to speed data
harvesting. Relevant force outputs were taken from the CFD analysis such as force and pressure distributions
which were subsequently utilized in both the optimization and structural analyses respectively. The
robustness of the automated deformation process was proven utilizing independent mesh deformations for
both angles of attack (α) and twist (Ω) which were subsequently and individually assessed.
In order to correctly parameterize the optimization definition, a series of analyses were undertaken in
order to determine the likely effects of wing twist undertaken using either the cosine or exponential decay.
Figure 15 show the results of this study and demonstrates the effectiveness of both morphing techniques in
reducing drag and increasing the L/D ratio.
Figure 15 Cl/CD for both rules an the control group
Figure 16 Direct comparission of L/D at twist angles
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D. Optimization Parameterization
To reduce the number of simulations used to find an optimum design, an optimization study was carried out based on
the initial experimental CFD data. A Genetic Algorithm was selected as the primary optimization algorithm in order to
enable solutions to be robustly determined within a single optimization cycle27 Relationships were derived by plotting
α and Ω for lift and drag. These relationships were used to enter responses into the optimizer for subsequent
alteration of design variables. The formal definition for the optimization problem is given in
.
Table 5 Optimization setup parameters
α, Ω
L/D
Lift > 15N
L/Dmax
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Variables
Responses
Constraints
Objective
The relationship between responses and variables were calculated based upon the outputs of the initial HFA
simulations (FIG) A trend-line was then plotted for each function and equated with its response given as a
function of the DVs. The derived optimization responses are show in
Table 6 Formulae for decay rules based on thrend line data
⁄
Straight Wing:
Exponential Decay:
⁄
⁄
Cosine Decay:
VI. Results of Aerodynamic Optimization
To reduce the number of simulations required to determine an optimal design, a primary optimization study was
created utilizing the initial experimental CFD data to derive relationships between α and Ω for both lift and drag.
These relationships were used as input data for the optimization program required for altering design variables.
Optimal wing twist was determined by the optimizer to be 5.096° for an Exponential twist decay; 4.98° for
Cosine twist decay and 2.7° α for the original wing. Repeat CFD simulations were undertaken with the altered
geometry showing a maximum 3% difference in L/D between predicted and obtained values. Following
verification of the results for optimum wing twist, the mesh was deformed to a 5° twist using both an Exponential
and Cosine decay and ran at varying angles of attack. The results and a direct comparison to the original wing are
depicted in Figure 17.
Figure 17 shows that introducing a 5° total twist using cosine decay, a reduction in drag for the wing can be
achieved across its operating cycle. Not only does this increase the aerodynamic efficiency of the wing, it also
creates a more robust wing design. The desired CL for cruise of 0.525 is achieved at a root α of 4° and a tip α of 1°. When comparing the optimum wing and original wing, a 24.8% increase in L/D is achieved for equivalent lift
in steady level flight.
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Figure 17 Direct comprisson of all techniques and approaches
VII. Problem Formulation - Structural Optimization
Structural optimization in aerospace applications is commonly used as a mass reduction post process once
structural design has been largely completed28. Whilst this does create an optimum structural design, it does so
for a heavily constrained design space.29 and does not maximize the potential of the available design space.
The approach demonstrated below uses structural optimization as a tool for design, maximizing the potential
for a largely unconstrained airfoil structural optimization.
A. Fluid Structure Interaction
In order to accurately assess the aircraft structure, the aerodynamic forces generated through computational
modeling in LFA were imported and applied to a Lagrangian finite element mesh (FEM) using a one way fluid
structure interface (FSI) The FSI allows for detailed modeling of structural components when under
aerodynamic loading, but presumes continuity of the flow profile throughout the analysis30.
A program was developed to automatically import (from either the LFA or HFA) the upper and lower aerofoil
pressure profiles along with a sequential import of FEM data and grid connectivity. The twin import allows
for calculation of the elemental centers for direct application of pressure forces, whilst the earlier separation of
aerodynamic forces allows for simplification of the interpolation functions required to calculate the discretized
pressure distribution
The developed program internally compensates for discrepancies in CFD and FEM modeling data using a
tired series of interpolation functions. The primary approach is a linear interpolation allowing for only very
small changes in geometric details between the inputs. In the event of a failure to generate using the primary
method, the program defaults to the secondary approach, utilizing a nearest neighbor approximation to allocate
the closet point on the input pressure to the output element. Figure 18 demonstrates the process for generation
of the FEA input file.
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Figure 18 FSI control program and flowchart
B. Optimization Parameterization
Modeled in 2D, the mean camber line of the wing was used as the primary design space for topology
optimization. A non-designable region was assigned to the required servo mount in the center of the wing,
with the model constrained rigidly at two fixed attachment points on the root of the wing. The flaperon was
included as a secondary design space and attached to the wing using 5 rigid beam elements, equally spaced along its
edge. A further rigid beam element joins the servo mounting point to the mid chord of the flap in order to represent
the servo linkage arm. Constraints were applied to all beam elements suitable to represent their intended purpose.
Three maneuver load cases were applied to the model using a mono-directional F S I pr o gra m t o a ppl y
pre ss ur e distributions obtained during the aerodynamic analysis from XFLR5. Material properties
commensurate with ScAlMalloy31 were applied to the model as requested by EADS IW along with the properties
of EOS PEEK32.
C. Manufactruring Constraints for Topology Optimization
While ALM removes a great many constraints from the manufacturing process, it also introduces several
complexities of its own particularly when used in conjunction with TO. All additive processes have a minimum
feature size to which they adhere. Any features below this size will emerge from the build substantially larger than
required. This feature size is related to both the process type EBM, DMLS, SLS, etc and to the particulate size in
the deposited material33,34. The values in provide a cross comparison of material and process types with their
corresponding feature sizes. Largely however, and unless dense material is used, the feature depth is approximate
~1mm.
Table 7 Geometric capability data for meachines based at EADS IW UK
Material
Titanium 6/4
Titanium 6/4
17-4 Steel
AlSi10
Nylon 12
EOS PEEK
Process Type
EBM
DMLS
DMLS
DMLS
SLS
SLS
Minimum Feature
0.8-1mm
0.25-3mm
0.2mm
0.8-1mm
0.75-1mm
0.75-1mm
As such, the minimum feature size was initially set at 1mm for the optimization thereby allowing the greatest
amount of structural complexity and by association, the most optimal design.
Additional design constraints stem from the requirements for support structure for geometric profiles which
exceed the angular limits for processing, or those features whose inclusion begins at any non-zero Z height. Finally,
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the provision for powder removal from as built parts must be considered. This is covered in greater depth in the
design for manufacture section.
D. Structural Optimization Objective
After demonstration of grid independence, three load cases were applied to the structural model relating to
the highest in plane loadings for flight and high speed cruise35. The model was analyzed using a variety of
optimization strategies, with a compliance based optimization algorithm eventually selected in order to
maximize wing stiffness and maintain the aerodynamic profiles created as part of the AOP. A displacement
and rotational constraint was applied to the LE and TE tip coordinates in order to assure adherence to initial
aero. Finally, a weighting factor was applied to the major load cases in order to favor the cruise condition
while still accounting for regulatory constraints.
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VIII.
Results of Structural Topology Optimization
A. Structural Layout
Variation in topology optimization parameters led to a number of material distribution models being developed
and compared. Figures 19-21 show how changes in defined member size, volume fraction have significant effects
on structural layout while still meeting the imposed constraints of the optimization parameters. In Figures 19-21,
blue represent zero material density (material removed), with red representing 100% material density (all material
retained).
Figure 19 30% volume fraction
Figure 20 10% volume fraction
Figure 21 15% volume fraction
Ultimately, pattern 3 (Figure 21) was selected as the primary structural layout for the UAV as it demonstrated
the most defined structural members with the best performance results.
B. Embodiment Design for Optimized UAV Aerofoil
The outputs of the 2D topology the results were embodied into the 3D geometry of the wing, as defined by the
optimized aerodynamic profile. In order to adhere to the original design brief of construction by ALM, and by
having selected ScAlMalloy as the main construction material, the geometric constraints of the DMLS
processmeant that the main wing must be sectioned for manufacture.
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Spars were created on the intersection of the main wing trusses at the wing root Figure 22 allowing for an
almost seamless integration of the wing root connector into the wing whilst maintaining the cross section profile
of the beams. Wing attachment is completed using twin, tool free dowels that lock seamlessly into the upper
and lower surfaces of the wing allowing for rapid assembly and modularity.
The main wing was sectioned into three parts, the inboard section which houses the servo, detaches from the
outboard section by two sliding connectors with an integral self-locking feature. The wing tip utilizing the same
connector design completes the aerofoil. The detachable tip allows the hinge pin for flaperon to be inserted and
locked in place once the tip is attached to the rest of the assembly. The servo is accessed by removing a weather
proofed hatch cover in the upper surface of the wing.
Figure 22 Final internal layout of the UAV wing and pattern for wing segmentation (right)
Holes were strategically placed within the internal structure to allow powder removal, post manufacture. The
minimum skin thickness for the wing was limited to 1mm due to manufacturing tolerances and will be polished
to approximately 0.5mm, again post manufacture42.
Figure 23 Method/direction of powder removal for all sections
Structural Validation
Linear static and buckling analysis was undertaken on a simplified 3D FEM which dispenses with the tip and
coupling plane, with the flaperon attached to the wing using kinematic couplings constrained to match hinge
movement. Using pressure distributions for the maximum maneuver loads of 5.24g and -3.25g; the design
was validated (Table 8) for both ScAlMalloy31 and the lightweight polymer, EOS PEEK HP332.
The results of the analyses show that the structurally optimized design proved to be viable was valid
throughout the predicted flight envelope. If constructed from ScAlMalloy, peak stresses of 21.3% of the max
yield were recorded. The design demonstrated low tip deflections, but substantial rotation (~0.9°) about the
tip quarter chord. Final Wing mass was ~0.4g. PEEK proved to be a more risky alternative, operating within
1% of the yield and showing. The lowest eigenvalue to be within 3.7% of the critical safety factors (1.15)
However, the resultant mass saving through use of PEEK was ~0.2kg.
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Table 8 Stress validation data for the UAV when constructed from either ScAlMalloy or PEEK
Material
Load Factor (g)
Tip Deflection
(mm)
Tip Twist (deg)
Peak Stress
(Mpa)
1st Eigenvalue
-3.25
0.56
-0.03
47
11.98
5.24
0.78
0.89
74.7
23.94
-3.25
9.92
-0.56
46.5
1.39
5.24
14.04
1.6
89.8
1.19
Scalmalloy
EOS PEEK
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X.
Conclusion
The aerodynamic and structural optimization loops performed as part of this investigation demonstrate that
even in low cost systems, optimization can yield substantial benefits if paired with relatively unconstrained and
inexpensive methods of manufacturing the resultant complexity 36. The aerodynamic analysis eventually
demonstrated a considerable improvement in excess of 24% across the entire flight regime, with structural
optimization decreasing weight by as much as 30% over a conventional layout design. In isolation these results
are impressive, but when matched with a means of rapid manufacturing they are truly ground-breaking. The
optimization and analyses loops above can be performed sequentially and repeatedly with new mission
requirements. The new design can then be rapidly constructed with no required tooling and little human
interface in order to produce a bespoke design for a new mission using only the modular fuselage and
empennage from the original design. The total time required to completely redesign and remanufacture is
approximately 140 hours. Indeed several sets of wings, each different from the next can be constructed in a
single batch process with little influence on total cost.
The completed Static Prototype UAV shown in Figure 24 was constructed in a single build and assembled
along with the bought in systems components in less than a week. This research demonstrates the advantages
of pairing multidisciplinary optimization, process automation and ALM in order to create truly optimized
structural designs previously considered to be either impossible or economically non-viable.
Figure 24 Completed UAV prototype on the EADS stand at the Farnborough airshow 2012
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