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Full Configuration Drag Estimation of Small-to-Medium Range UAVs
and its Impact on Initial Sizing Optimization
F. Götten, D.F. Finger, M. Havermann, C. Braun
Department of Aerospace Engineering, FH Aachen University of Applied Sciences, Aachen, Germany
goetten@fh-aachen.de, f.finger@fh-aachen.de, havermann@fh-aachen.de, c.braun@fh-aachen.de
M. Marino, C. Bil
School of Engineering, RMIT University, Melbourne, Australia
matthew.marino@rmit.edu.au, cees.bil@rmit.edu.au
Abstract
The paper presents the derivation of a new equivalent skin friction coefficient for estimating the parasitic drag of short-to-
medium range fixed-wing unmanned aircraft. The new coefficient is derived from an aerodynamic analysis of ten different
unmanned aircraft used on surveillance, reconnaissance, and search and rescue missions. The aircraft are simulated using
a validated unsteady Reynolds-averaged Navier Stokes approach. The UAV's parasitic drag is significantly influenced by
the presence of miscellaneous components like fixed landing gears or electro-optical sensor turrets. These components
are responsible for almost half of an unmanned aircraft's total parasitic drag. The new equivalent skin friction coefficient
accounts for these effects and is significantly higher compared to other aircraft categories. It is used to initially size an
unmanned aircraft for a typical reconnaissance mission. The improved parasitic drag estimation yields a much heavier
unmanned aircraft when compared to the sizing results using available drag data of manned aircraft.
Keywords
Parasitic drag, UAV, CFD, aircraft sizing
1. ABBREVIATIONS
AoA Angle of attack
AR Aspect ratio
CD Drag coefficient
CDmin Minimum drag coefficient
Cfeq. Equivalent skin friction coefficient
CL Lift coefficient
EARSM Explicit Algebraic Reynolds Stress
MTOM Maximum take-off mass
P/W Power-to-weight ratio
Re Reynolds number
SIMPLE Semi Implicit Method for Pressure
Linked Equations
SMR Short-to-medium range
Sref Aircraft reference area
SST Shear Stress Transport
Swet Aircraft wetted area
UAV Unmanned Aerial Vehicle
URANS Unsteady Reynolds Averaged Navier Stokes
W/S Wing loading
y+ normalized wall distance
2. INTRODUCTION
Conceptual aircraft design is a multidisciplinary optimiza-
tion problem. The design routine is divided into several sub-
models that are responsible for estimating the new aircraft's
properties, including weight, propulsion, cost, or aerody-
namics. The accuracy of such underlying models is of criti-
cal importance as their outcomes drive the design to
convergence. One of these critical models is responsible for
estimating the new aircraft's parasitic drag. The parasitic
drag has a direct effect on flight performance prediction and
propulsion system sizing. A well-known approach is to use
an equivalent skin friction method. In this method, the com-
plete parasitic drag of an aircraft is based on an equivalent
skin friction coefficient and its multiplication with the wetted
area of the airframe. The empirical skin friction coefficient
depends on the aircraft category to account for their specific
aspects [1]. A careful literature review revealed that neither
an equivalent skin friction factor nor wetted area correla-
tions have yet been derived for short-to-medium range
UAVs. These UAVs have take-off masses from 20 kg up to
about 600 kg, and they are often used on reconnaissance,
surveillance, and search and rescue missions. There is no
uniform classification for such UAVs, and multiple ones
have been proposed in the past [2]. They are sometimes
termed short-range (SR) to medium-range (MR) UAVs, but
also referred to as "tactical UAVs". For reasons of simplic-
ity, they are named SMR UAVs (short-to-medium range) in
this publication. Prominent examples of this aircraft cate-
gory are the Aeronautics Aerostar or the AAI Shadow 200.
SMR UAVs employed on reconnaissance, surveillance,
and search and rescue missions are often designed using
the twin-tail boom configuration with a pusher propeller. In
this configuration, the exhaust gases of the combustion en-
gine do not interfere with the optical sensors.
The past development of SMR UAVs was very concerned
with data-link capabilities, autopilot functionality, and safety
aspects. Aerodynamic design was often subordinated,
even though SMR UAVs are very sensitive to aerodynamic
aspects, including parasitic drag [3]. Only very recently did
research interest also extend to the aerodynamic design
and optimization of SMR UAVs [4, 5].
Little information about the parasitic drag of SMR UAVs is
currently available [6]. UAV designers nowadays rely on
available data of other aircraft categories. As SMR UAVs
feature particular configurational aspects like solid spring-
type landing gears or EO/IR sensor turrets, their parasitic
drag might not be comparable to these aircraft categories.
In such, the drag and flight performance estimation of SMR
UAVs is subject to large uncertainties. This can have a sig-
nificant effect on the outcomes of a design process.
This publication, therefore, presents new aerodynamic par-
asitic drag data of 10 representative UAVs. The data is gen-
erated using a validated Unsteady Reynolds-averaged
Navier-Stokes (URANS) approach. The UAVs are chosen
as representative for a variety of configurations. The aero-
dynamic data is used to derive a novel equivalent skin fric-
tion coefficient that allows a full-configuration drag
estimation of SMR UAVs. The new coefficient can be used
in combination with geometrical data of an in-house data-
base to directly estimate the drag of a new UAV.
In a parallel research effort, a tool for the initial sizing of
general aviation aircraft has been developed [7]. It allows
the sizing of conventional, fully-electric, and hybrid-electric
aircraft. Recently, the tool has also been expanded to allow
the sizing of SMR UAVs. The newly derived drag estimation
technique for those aircraft is used in the aerodynamic
model of this tool to increase its capabilities. The paper will
further show the impact of the enhanced drag estimation
accuracy based on the sizing results of an SMR UAV.
3. METHODOLOGY
3.1. Equivalent Skin Friction Approach
The equivalent skin friction method models an aircraft's par-
asitic drag with a skin friction coefficient in analogy to a flat
plate. This equivalent skin friction coefficient is multiplied
with the aircraft's total wetted area, which is assumed to be
proportional to the total parasitic drag. The basic formula-
tion of the method is given in Eq. (1).
=
.∙
(1)
The application of the method requires an equivalent skin
friction coefficient and the ratio of the aircraft's wetted area
to reference (wing) area as inputs. Some wetted area esti-
mates depending on the aircraft class are given in Ray-
mer [1]. However, no data for UAVs is available.
The equivalent skin friction coefficient strongly depends on
the aircraft category for two distinctive reasons: First, the
size and flight speed of the aircraft category dictates its
Reynolds number range. Larger aircraft at higher speeds
show significantly larger Reynolds numbers than small, low-
speed aircraft. Generally, an increase in Reynolds number
reduces the turbulent skin friction coefficient and, therefore,
also the equivalent skin friction coefficient.
Second, the equivalent skin friction coefficient has to repre-
sent the complete parasitic drag of an aircraft. However,
only a portion of the aircraft's parasitic drag is friction drag
and thus proportional to the wetted area. A large amount of
aircraft parasitic drag can be pressure drag caused by flow
separation or bluff body flow. Pressure drag is not propor-
tional to the aircraft's wetted area, and aircraft with signifi-
cant parasitic pressure drag must have higher equivalent
skin friction coefficients to account for this. The amount of
parasitic pressure drag depends on the aerodynamic
smoothness of the aircraft and, thereby, on the aircraft cat-
egory.
An overview of equivalent skin friction coefficients for vari-
ous aircraft categories is found in Ref. [1], and here shown
in Table 1. To date, no range of equivalent skin friction co-
efficients for SMR UAVs is available.
Table 1 Equivalent skin friction coefficients for various aircraft
Aircraft Type Cfeq.
Typical cruise
wing-chord Re
Jet Bomber and Civil
Transport 0.0026-0.0030 80,000,000
Military Cargo 0.0035 80,000,000
Jet Fighter 0.0035 30,000,000
Light Aircraft –
Single Engine Prop 0.0055 8,000,000
Light Aircraft –
Twin Engine Prop
0.0045 12,000,000
3.2. UAV Geometry Selection and Modeling
The UAVs that were investigated in this study were chosen
according to the findings of an extensive in-house data-
base. This database contains geometry information of over
100 fixed-wing UAVs, whereby more than 80% of these
UAVs can be attributed to the SMR category. The database
provides a range of statistical data that includes general di-
mensions, detailed shapes, and flight performance param-
eters. Very little information on the detailed geometry of
SMR UAVs is openly available as most manufacturers keep
these a secret. Therefore, the information for the database
was taken from high-quality images or three-view drawings.
With basic geometry information, the dimensions of nearly
all components can be extrapolated from such images.
Both angular- and distortion corrections were used to im-
prove the accuracy of the data. Tests with available UAV
CAD models show that the accuracy for individual compo-
nent dimensions is within 5-10%.
A central part of the database is the estimation of a UAV's
wetted area as a direct input into the equivalent skin friction
drag estimation. The geometry is simplified and approxi-
mated by shapes for which analytical equations are availa-
ble. Depending on the component, multiple geometrical
representations are available, and the most realistic one
can be chosen by the user. The geometrical break-down of
the UAVs is detailed. Taking the data acquisition accuracy
into account, the overall accuracy for the total UAV wetted
area is about 10%-15%. This is considered adequate for the
desired purpose. Further information on the database can
be found in Ref. [8]
10 SMR UAVs included in the database were selected as
being representative, and their geometry remodeled using
NASA's OpenVSP [9]. The choice of the UAVs was further
influenced by the availability of three-view drawings and im-
ages that allowed accurate modeling. All UAVs feature re-
ciprocating engine propulsion systems. For simplification,
aero-propulsive effects were not considered, and the pro-
pulsion system was not included in the models. The limited
amount of information required some simplifications of the
airframe's shapes. The same EO/IR sensor turret shape is
attached to the lower fuselage side of each UAV. The EO/IR
turret geometry was designed based on another in-house
turret database and chosen as being representative for a
wide variety of turrets. The positioning and size of the indi-
vidual turret for each UAV follows the findings of the data-
base.
No information concerning the airfoils used on the wing and
tail surfaces were available. For the tail surfaces, symmetric
NACA 0010 or NACA 0012 airfoils were chosen depending
on the actual thickness of the UAV's tail surface. These air-
foils are characteristic for horizontal or vertical stabilizers on
a wide variety of aircraft [10].
The formulation of the equivalent skin friction coefficient
only includes the parasitic (lift-independent) part of the air-
craft's drag. Lift-induced drag is not covered and must be
excluded from the findings. This generally allowed two dif-
ferent approaches in this study. First, one could choose a
typical cambered airfoil for the UAVs' wings and trim the
wing incidence angle so that the desired lift coefficient is
reached. However, in this configuration, the influence of in-
duced drag is significant. To adequately account for the lift-
independent drag formulation, the wing's induced drag
must be subtracted from the wing's parasitic pressure drag.
This, however, is not trivial using a volume resolving CFD
method. Lift-induced drag is often calculated by wake sur-
veys that are prone to numerical diffusion effects. Further-
more, a strict distinction between the vortices causing lift-
induced drag of the wing and other flow protuberances in
the far-field is not directly possible in case a full aircraft con-
figuration is considered. Further information is provided in
Refs. [11, 12]. These factors significantly reduce the accu-
racy of the wing-induced drag calculation and therefore en-
hance the inaccuracy of the wing's parasitic pressure drag
computation.
It was therefore decided to employ the symmetric
NACA 0015 airfoil over the complete wingspan of each
UAV. The symmetric airfoil, in combination with zero wing
incidence, automatically enforces zero-lift conditions. The
difference in profile drag between a cambered airfoil and a
symmetric airfoil is introduced as a correction later on. All
analyzed UAVs are shown in Figure 3-1. This figure is not
to scale and trimmed for visualization purposes. A scaled
top-view of the UAVs is presented in Figure 3-2. Table 2
gives an overview of the UAVs' most important data (Reyn-
olds number based on mean wing-chord).
Table 2 General data of the analyzed UAVs
UAV No.
MTOM, kg
Sref,
m²
Wingspan, m
AR
Rex10
6
1
25
1.00
3.3
10.9
0.5
2
50
1.32
3.6
9.8
0.6
3
75
2.34
4.2
7.5
1.6
4
130
2.63
6.0
13.5
1.1
5
150
3.04
4.9
7.9
1.5
6
170
2.37
4.3
7.8
1.2
7
182
3.13
5.2
8.6
1.4
8
205
2.82
5.1
9.4
1.1
9
230
4.74
8.7
16.0
1.2
10
630
8.70
12
16.5
1.8
UAV 1
UAV 2
UAV 3
UAV 4
UAV 5
UAV 6
UAV 7
UAV 8
UAV 9
UAV 10
Figure 3-1 Isometric views of the analyzed UAVs (not to scale)
Figure 3-2 Top-view of all analyzed UAVs; 1-10 from top left to bottom right (figure to scale)
3.3. Numerical Simulation Approach
The analyses were performed using the computational fluid
dynamics software StarCCM+ v15.02. This finite volume
solver is well-known in both industry and academia and has
been validated for a variety of cases [13]. The Unsteady
Reynolds-averaged Navier-Stokes (URANS) equations are
solved with a SIMPLE (Semi Implicit Method for Pressure
Linked Equations) algorithm assuming incompressibility
due to the low Mach number regime (see [14]). Both the
physical and numerical approach follows the guidelines pre-
sented in Ref. [15].
The numerical grid in the free volume was of an unstruc-
tured Cartesian type that allows the discretization of arbi-
trarily complex geometries. The near-wall flow was resolved
with a dedicated prismatic boundary layer mesh. The nu-
merical solution was second-order accurate in both space
and time. It employed upwind schemes for discretizing con-
vective fluxes, while diffusive ones were approximated with
central differences. Menter's Shear Stress Transport (SST)
model closed the RANS equations combined with a cubic
non-linear constitutive option. This Explicit Algebraic Reyn-
old Stress Model (EARSM) enhances the prediction capa-
bilities of anisotropy of turbulence [16, 17]. It is valuable for
secondary flows caused by swirls or separation. The
freestream turbulence intensity at the flow inlet was set to
1.0% in combination with a turbulent viscosity ratio of 5.
Sustainment terms added to the transport equations of the
turbulence model guaranteed that both turbulent kinetic en-
ergy and specific dissipation rate did not dissipate in the
free flow [18].
All UAVs were analyzed at zero degrees angle of attack,
assuming free atmospheric flight conditions. Therefore, a
large flow domain was used that extended 30 times the
maximum body length in flow direction and 15 times per-
pendicular to the flow.
At the inlet boundary conditions, velocity, ambient pressure,
and turbulence variables were fixed. The inlet velocity cor-
responded to the given Reynolds number in Table 2, as-
suming mean sea level conditions. The pressure at the
outlet boundary condition was set to the ambient reference
pressure, while the velocity was extrapolated from the ad-
jacent cells in the domain. A cut through the 3D domain is
shown in Figure 3-3.
Figure 3-3 Simulation domain on the symmetry plane (not to scale)
The UAV surfaces were discretized with quadratic cells fol-
lowing guidelines given in Ref. [15]. At least 80 cells were
used in the chord-wise direction of the lifting surfaces with
Velocity inlet
Pressure outlet
additional refinements at leading and trailing edges. The
near-wall region was discretized with 30 prismatic cell lay-
ers, whereby the first cell height ensured having y+ values
on the order of 0.5-0.8 for all cases. At least 10 cell layers
discretized the viscous sublayer. The transition between the
boundary layer mesh and core mesh was smooth, with cell
sizes being very similar. The surface mesh of UAV 9 is
shown in Figure 3-4, together with a close-up of the bound-
ary layer and volume mesh.
Figure 3-4 Level of surface discretization and boundary layer mesh
The overall cell count of each simulation depends on the
individual UAV and its complexity. It varies between 55 and
80 million hexahedral cells. Grid dependency studies were
conducted for all UAV configurations ensuring grid-inde-
pendent results for the total drag and the drag of individual
components. Exemplary grid dependency studies of UAVs
1 and 10 showing total drag and turret drag are presented
in Figure 3-5.
Figure 3-5 Grid dependency study for UAVs 1 and 10
The time step was approximated with typical Strouhal num-
bers of spheres and cylinders for comparable Reynolds
numbers [19]. It was later adjusted by testing several time
step reductions, whereby a value of 2.5x10-4 s was found to
be optimal. After initial stabilization, the simulations were
run for at least two more seconds (8000 time-steps). This
corresponds to about 60-120 vortex oscillation periods of
spheres or cylinders at comparable Reynolds numbers.
3.4. Validation
The simulation approach is validated by comparing it to a
wind-tunnel study of an 0.4-scale model of the AAI RQ-2
Pioneer UAV presented by Bray in Ref. [20]. The Pioneer
UAV is a 205 kg surveillance and reconnaissance UAV em-
ployed by the United States and Israel. The model was
tested in the Low-Speed Wind-Tunnel (LSW T) at Wichita
State University. The tests were run at a chord-based Reyn-
olds number of about 1.06 million, which corresponds to the
full-scale free flight Reynolds number in 10,000 ft. The wing
of the wind-tunnel model has a NACA 4415 airfoil section
and an incidence of two degrees. In such, minimum drag
conditions are expected for positive lift. The model features
a dummy payload consisting of a hemisphere attached to
the lower fuselage. Further geometrical details of the model
are presented in Ref. [20]. An angle of attack sweep
from -8° up to 14° was simulated with the above presented
unsteady RANS approach. The results for both lift, drag,
and moment coefficients are given in Figure 3-6 and
Figure 3-7. Both lift, drag, and longitudinal moment match
to the wind-tunnel data within 5% for moderate angles of
attack. The simulation overpredicts the lift coefficient at
higher angles of attack, which is typical for RANS-based
approaches. However, stall conditions are achieved earlier
than in the wind-tunnel.
Figure 3-6 Pioneer lift and moment with data from Ref. [20]
Figure 3-7 Pioneer drag polar with data from Ref. [20]
0
20
40
60
80
100
120
140
0
100
200
300
400
500
600
700
020 40 60 80 100
C
D
turret, drag c ounts
C
D
total, drag c ounts
No. of cells , Million
UAV 1 CD total
UAV 10 CD total
UAV 1 CD turret
UAV 10 CD turret
UAV 1 C
D
total
UAV 10 C
D
total
UAV 1 C
D
turret
UAV 10 C
D
turret
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
-0.6
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
-10 010 20
C
M
C
L
AoA, deg
CL URANS - this study
CL wind-tunnel - Bray
CM URANS - this study
CM wind-tunnel - Bray
C
L
URANS
C
L
Wind-tunnel -Bray
C
M
URANS
C
M
Wind-tunnel -Bray
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
0.00 0.05 0.10 0.15 0.20
C
L
C
D
URANS
Wind-tunnel - Bray
The longitudinal moment matches to the wind-tunnel data
for moderate angles of attack and starts deviating when ap-
proaching stall conditions. Drag is accurately simulated up
to about 8 degrees angle of attack (CL=1.0, deviations be-
low 5%).
Higher lift conditions, show an underestimation of drag
along with an overprediction of lift. The validation highlights
that the chosen simulation approach is sufficiently accurate
for the desired analysis cases.
4. RESULTS
The following section describes the results of the numerical
analyses. A full aerodynamic investigation of the UAVs is
beyond the scope of this publication. Therefore, only an
overview of the pressure coefficient distribution on each of
the UAVs' surfaces is given in Figure 4-1. Further investi-
gations focus on the parasitic drag of the UAVs.
UAV 1
UAV 2
UAV 3
UAV 4
UAV 5
UAV 6
UAV 7
UAV8
UAV 9
UAV 10
Figure
4-1 Isometric views of pressure coefficient on UAV surface (UAVs not to scale)
Stagnation conditions are always found at the fuselage
noses, while only a moderate level of flow acceleration is
noted on the wing's surfaces. This is due to the chosen
symmetric airfoil and zero degrees angle of attack. Signifi-
cant flow acceleration and separation effects can be seen
for both the landing gears and EO/IR turrets.
4.1. Parasitic drag analysis
The lift-independent pressure drag of the UAV's wings is
corrected for airfoil camber effects with the following ap-
proach: Airfoil drag generally consists of friction and pres-
sure drag. Assuming fully attached flow, the pressure drag
is referred to as boundary layer pressure drag. This drag is
essentially due to the way the boundary layer changes the
viscous pressure distribution of the airfoil compared to the
inviscid case. Cambered and symmetric airfoils obviously
have different inviscid pressure distributions. The presence
of the boundary layer changes their pressure distributions,
which yields larger boundary layer pressure drag for cam-
bered airfoils due to their shape and the associated higher
levels of flow acceleration [21]. This is valid for small and
moderate angles of attack. It increases the total airfoil drag
(often termed profile drag) of a cambered airfoil compared
to a symmetric one.
The increased profile drag of cambered airfoils manifests
itself in an increased lift-independent pressure drag of a 3D
wing compared to a wing with a symmetric airfoil. Based on
wind-tunnel data from Ref. [22] and own data of the authors,
a representative NACA 4415 airfoil used on several UAVs
shows 1.08 times higher profile drag compared to a
NACA 0015 airfoil assuming fully turbulent flow. The drag
of the UAV's wings is, therefore, increased by this factor.
The influence, however, is generally small and does not ex-
ceed 3% of the total drag. This minor influence also shows
the validity of choosing a symmetric airfoil for the UAVs
wings, given the desired analysis case.
Table 3 Equivalent skin friction coefficient of 10 SMR UAVs
UAV
C
Dmin,
drag counts
Swet, m2 Sref, m2 Cfeq.
1 516.4 4.0 1.0 0.0131
2 360.7 4.8 1.3 0.0102
3 302.2 8.1 2.3 0.0089
4 432.3 9.8 2.6 0.0118
5 470.9 11.5 3.0 0.0133
6 407.0 8.4 2.4 0.0118
7 428.4 14.4 3.1 0.0095
8 617.6 13.3 2.8 0.0133
9 373.7 17.2 4.7 0.0105
10 343.3 33.9 8.7 0.0090
Average 425.2 12.5 3.2 0.01115
The parasitic drag coefficients of all analyzed UAVs are
shown in Table 3. They are normalized to the UAV's refer-
ence wing area. The equivalent skin friction coefficients of
all UAVs are computed according to Eq. (1). The coeffi-
cients naturally scatter with the UAV configuration.
The distribution of the UAV's equivalent skin friction coeffi-
cients is additionally shown in the bar diagram plot of Fig-
ure 4-2 in comparison to the values for two other aircraft
categories given in Ref. [1]. The new SMR UAV equivalent
skin friction coefficient is more than double as high com-
pared to the light aircraft – single engine category and more
than four times higher compared to the civil transport jet
category.
Figure 4-2 Equivalent skin friction coefficient of 10 SMR UAVs
The numerical approach can calculate the drag force of
each UAV component individually via a local surface inte-
gration with every surface cell. To limit the amount of data,
only an averaged parasitic drag break-down is presented
here in Figure 4-3. This average drag break-down is com-
puted based on the drag break-downs of each UAV of this
study. About 50% of total parasitic drag is caused by mis-
cellaneous components, of which landing gear and EO/IR
turret hold the major share. The influence of tail booms and
antennas is generally small. Interestingly, about 65% of the
parasitic drag is pressure drag, while skin friction only con-
tributes to about 35% of the total drag share. This is very
different compared to larger aircraft categories, where fric-
tion drag is usually the driving factor of parasitic drag [1].
Figure 4-3 Average SMR UAV parasitic drag break-down
0.000
0.002
0.004
0.006
0.008
0.010
0.012
0.014
0.016
12345678910
C
f equivalent
UAV
Light aircraft single engine -Raymer
Civil transport jet-Raymer
Average SMR UAV -this study
Wing 27%
Tail 5%
Fuselage 17%
Tailbooms 2%
Landing gear 28%
Turret 19%
Antennas 2%
UAV average fully turbulent
The high level of parasitic drag and the significant influence
of miscellaneous components are the main reasons for the
high equivalent skin friction coefficient of SMR UAVs.
4.2. Wetted area regressions
The equivalent skin friction coefficient drag estimation re-
quires knowledge of the new aircraft's wetted area. De-
pending on the aircraft sizing algorithm and tool, the total
wetted area might be calculated based on simple geomet-
rical relationships that are available for the design. How-
ever, in case a total wetted area is not available in the
design algorithm, several correlations for wetted areas of
different aircraft categories are found in the literature
(Refs. [1, 23]). However, no relationships for SMR UAVs
are available. Therefore, a new correlation between total
UAV wetted area and maximum take-off mass is developed
using the in-house database. The data is shown in Fig-
ure 4-4, together with the formulation of a simple power-law
fit. The fit can describe the mean of the data; however, scat-
ter is considerable. Whenever more accurate estimations
are available, it is highly desirable to use them.
Figure 4-4 SMR UAV wetted area vs. MTOM
4.3. UAV Sizing Process
The newly derived equivalent skin friction coefficient for
SMR UAVs is used in the aerodynamic model of an in-
house aircraft design tool [7] to increase its capabilities. A
typical SMR UAV is initially sized to a classic design mis-
sion based on three different equivalent skin friction coeffi-
cients including the newly derived one. This allows for an
analysis of the impact of the new drag estimation coefficient
in UAV design processes.
4.3.1. Aircraft and Mission
A notional twin-boom reconnaissance SMR UAV is selected
for the sizing study. The concept features a single pusher
propeller and twin tail booms. A sketch of the notional air-
craft is provided in Figure 4-5. The UAV is designed for a
typical long-endurance surveillance mission, which is
shown in Figure 4-6. Six hours of loiter at best endurance
speed is required, as well as a quick 150 km dash at 65 m/s
into- and out of the target area. A 15 kg payload of surveil-
lance and communication equipment must be carried. Top-
level requirements and a mission description are shown in
Table 4.
Figure 4-5 Notional SMR UAV concept
Figure 4-6 Notional SMR UAV design mission
Table 4 Notional SMR UAV design parameters and mission
Parameter
Value
Parameter
Value
Take-off
Ground Roll, m 250
Sensor
Payload, kg 15
Rate of Climb
at MSL, m/s 3
Taxi and
Take-off at MSL
Stall Speed,
m/s 30
Climb to
Altitude 2000 m
Dash Speed,
m/s 65
Ingress to
Target Area 150 km
Loiter Speed,
m/s 45
Loiter over
Target Area for 6 h
Max. Lift Coef.
cL,max 1.30
Egress from
Target Area 150 km
Service
Ceiling, m 4600
Descend, Land,
and Taxi at MSL
Propulsion
System
4-Stroke
ICE
ICE best
BSFC, g/kg/h 315
4.3.2. Sizing Approach
The requirements above are used to size the aircraft and
perform the mass analysis. To assess the impact of the
novel equivalent skin friction factor, the sizing process is
performed three times. First, the authors' novel skin friction
coefficient is used. The sizing is then repeated using Ray-
mer's skin friction coefficient for general aviation aircraft,
0
5
10
15
20
25
30
35
40
0100 200 300 400 500 600 700
Swet m²
MTOM, kg
and once more using the highly optimistic values repre-
sentative of civil and military jet transports.
The authors expect that using these different drag factors
will decidedly change the sizing results.
The sizing process is supported by an optimization routine.
In particular, a global optimization scheme, the particle
swarm method, is employed to find an optimal design for
the given set of TLARs and constraints. It is used to select
the best possible combination of wing loading W/S, power-
to-weight ratio P/W, wing aspect ratio AR. These design
variables have been shown to be appropriate for the initial
design of subsonic aircraft [24].
The influence of W/S and AR on the induced drag of the
aircraft is captured, using methods from Ref. [1]. However,
the zero-lift drag coefficient is calculated using different Cfeq.
and the aircraft's wetted area. Further information on this
process can be found in Ref. [25].
A simplified illustration of the process is provided in Fig-
ure 4-7. Analysis steps of the sizing process are indicated
in blue boxes, while optimization steps are indicated in red
boxes.
Figure 4-7 Optimization process
Minimum MTOM is selected as the optimization's objective.
The minimization of MTOM is usually the goal during air-
craft design since it is widely acknowledged that "the light-
est aircraft that does the job is considered the best" [W. H.
Mason, "Modern Aircraft Design Techniques," in Handbook
of Transportation Engineering, McGraw-Hill, 2003, pp.
26.1-26.24]. Designers traditionally use aircraft weight to
predict cost, as cost scales almost linearly with aircraft
weight.
Another suitable measure for assessing aircraft is their en-
ergy consumption. The required energy for the design mis-
sion is readily available, as it is determined by the mission
analysis and then used to determine the aircraft's energy
mass fraction. This parameter will also be assessed in the
result's analysis.
4.3.3. Results of SMR UAV Sizing
The results of the sizing study are presented in Table 5. As
expected, using an improper Cfeq. will drastically impact the
sizing and optimization results.
Using the author's Cfeq. value of 0.011 resulted in a typical
SMR UAV layout: a highly loaded, high AR wing is used to
offset the high CDmin.
If designers were to assume typical Cfeq. values for light air-
craft, the result is driven towards such a geometric layout.
The optimal AR, as well as the optimal W/S is reduced.
Also, P/W is reduced, as a lot less aerodynamic drag needs
to be overcome. The MTOM is estimated more than 20%
too low, and energy consumption is off by 61%.
Finally, using a Cfeq. value of 0.0028, as is typical for jet
transports, drives down AR, W/S, and P/W even further.
The result has very little to do with the actual geometry
found for SMR UAVs. For this design, MTOM is off by 25%,
and energy consumption is 90% underestimated, com-
pared to the result that uses the author's Cfeq. value.
These results show that by using an inappropriate Cfeq.,
very large errors are introduced in the design process. Such
errors are very costly to correct in later design stages. If
drag is underestimated for the first sizing computations, a
redesign will have to follow once this issue is found. Using
the authors' novel Cfeq. value will prevent additional design
iterations.
Table 5 Sizing results - Notional SMR UAV
C
feq.
0.011
0.0055
0.0028
Parameter
UAVs [authors]
Light Aircraft
Jets
C
Dmin
0.0470
0.0236
0.0106
MTOM, kg
311
256
248
Energy, GJ
1.74
1.08
0.92
W/S, N/m²
714
588
334
P/W, W/kg
110
78
62
AR
15.0
9.7
6.5
Error relative to UAV C
feq.
of the authors
MTOM, %
-
21.5
25.4
Energy, %
-
61.1
89.1
A scaled illustration of the three different designs is pro-
vided in Figure 4-8. This makes it easy to appreciate the
differences in design that the optimization process selected.
It becomes quite clear that selecting a proper drag model
as early as possible in the design process is of utmost im-
portance to avoid costly iterations at later stages of a pro-
gram.
Figure 4-8 Top-view of the three sizing results Cfeq.=0.011; 0.0055;
0.0028 from left to right
5. CONCLUSION
The paper presents the derivation of a new equivalent skin
friction coefficient for estimating the parasitic drag of SMR
UAVs. Such UAVs are nowadays employed on surveil-
lance, reconnaissance, and search and rescue missions.
Their parasitic drag is much larger compared to other air-
craft categories. It is significantly influenced by the pres-
ence of miscellaneous components like fixed landing gears
and EO/IR sensor turrets. The new equivalent skin friction
coefficient is more than double as high as common coeffi-
cients found for small single-engine aircraft. The coefficient
might be employed with a new wetted area regression for
SMR UAVs that allows determining the wetted area based
on a UAVs maximum take-off mass.
The initial sizing of a novel SMR UAV is significantly af-
fected by the parasitic drag estimation. The new equivalent
skin friction coefficient has the potential to enhance drag
estimation in early conceptual design stages.
6. ACKNOWLEDGEMENT
The authors like to express their gratitude to Siemens PLM
Software for providing academic licenses of their software
StarCCM+.
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