Research ArticleDevelopment of Design Methodology for a Small Solar-PoweredUnmanned Aerial Vehicle

Parvathy Rajendran 1 and Howard Smith2

1School of Aerospace Engineering, Universiti Sains Malaysia, Penang, Malaysia2Aircraft Design Group, School of Aerospace, Transport and Manufacture Engineering, Cranfield University, Cranfield, UK

Correspondence should be addressed to Parvathy Rajendran; aeparvathy@usm.my

Received 7 August 2017; Revised 4 January 2018; Accepted 14 January 2018; Published 27 March 2018

Academic Editor: Mauro Pontani

Copyright © 2018 Parvathy Rajendran and Howard Smith. This is an open access article distributed under the Creative CommonsAttribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work isproperly cited.

Existing mathematical design models for small solar-powered electric unmanned aerial vehicles (UAVs) only focus on mass,performance, and aerodynamic analyses. Presently, UAV designs have low endurance. The current study aims to improve theshortcomings of existing UAV design models. Three new design aspects (i.e., electric propulsion, sensitivity, and trend analysis),three improved design properties (i.e., mass, aerodynamics, and mission profile), and a design feature (i.e., solar irradiance) areincorporated to enhance the existing small solar UAV design model. A design validation experiment established that the use ofthe proposed mathematical design model may at least improve power consumption-to-take-off mass ratio by 25% than that ofpreviously designed UAVs. UAVs powered by solar (solar and battery) and nonsolar (battery-only) energy were also compared,showing that nonsolar UAVs can generally carry more payloads at a particular time and place than solar UAVs with sufficientendurance requirement. The investigation also identified that the payload results in the highest effect on the maximum take-offweight, followed by the battery, structure, and propulsion weight with the three new design aspects (i.e., electric propulsion,sensitivity, and trend analysis) for sizing consideration to optimize UAV designs.

1. Introduction

In recent years, awareness of the potential of unmannedaerial vehicles (UAVs) for multiple missions has increasedthe level of research into improving UAV design and tech-nologies. Studies found that electric- or fossil fuel-poweredUAVs have inadequate flying hours for various tasks.Increased focus has been placed recently on developinghybrid-powered electric UAVs, especially with the combina-tion of solar energy and battery or fuel cells in a battery-powered system. The current study considers the pros andcons of various power systems [1–6] and examines the designand development of solar cells in battery-powered UAVs.

Formore than a decade, small solar- and battery-poweredelectric UAVs were the subject of research and development[1, 5–19]. Seven small solar UAVs (i.e., So Long, Sky-Sailor,Sun-Sailor, Sun Surfer, AtlantikSolar AS-2, University ofMinnesota’s SUAV, and Cranfield University’s Solar UAV)weighing less than 20 kg have been developed to date. As a

prominent researcher in this field, Noth [14, 20] developeda mathematical design model for sizing solar-powered elec-tric UAVs. However, this mathematical design model focusesonly on the conceptual design stage in estimating the generalsizing of UAV configurations.

Noth [14, 20] focused on the 3 sizing elements of mass,aerodynamics, and performance. Design characteristics,such as aircraft power consumption and cruise speed, mustalso be optimized to ensure that the required long endur-ance of the UAVs for 24 hours of real surveillance andmapping applications is achieved. The current study aimsto improve the design properties, which requires furtherin-depth work, such as solar irradiance, mission profile,and aerodynamic characteristics. Research gaps in electricpropulsion sizing, sensitivity studies, and trend analysishave also been identified.

A synthesized UAV mathematical design model suitablefor sizing and configuration design of small solar-poweredand battery-powered electric systems was developed to fulfill

HindawiInternational Journal of Aerospace EngineeringVolume 2018, Article ID 2820717, 10 pageshttps://doi.org/10.1155/2018/2820717

the previously mentioned requirements. This comprehensiveexpansion work, based on various studies [21–23], considersnine design properties (i.e., mass sizing, aerodynamics, per-formance, stability and control, mission profile, solar irradi-ance, electric propulsion, sensitivity studies, and trendanalysis) to improve the current design model.

The proposed mathematical model was initially quasi-validated using existing UAV data via rigorous computermodeling and analyses. The validity of the mathematicalmodel was deemed satisfactory after achieving a lower errorin the model’s conceptual design than in the actual UAVdesign using the empirical database. An actual solar-powered electric UAV [1] was also designed, developed,and flight-tested to further quasi-validate this model. Thecomprehensive design model is developed in this study tofurther minimize assumptions and simplify previous designs.

2. Mathematical Design Model

The developed solar-powered electric UAV mathematicaldesign model and its algorithm flowchart are illustrated inFigures 1 and 2, respectively. The model contains the ninedesign components mentioned earlier. Three design compo-nents, namely, mass estimation, aerodynamic estimation,and performance analysis, were initially developed by Noth[14]. Performance analysis is the only design componentthat was maintained based on the specifications developedby Noth.

In the mass estimation, the study by Mueller et al. [22]was further adopted. The component mass was divided intothe following basic elements, namely, structure, battery,solar, electric propulsion, control system, and payload, asshown in (1), respectively. The aircraft’s total take-off weightWTOmax may be expressed as a combination of the emptyweight and payload weight, as shown in (2), respectively,

because a pure electric UAV does not have a variable weightduring flight.

WTOmax =WStruct +WBatt +WSolar +WElectric

+WCtrl +WPay Max,1

WTOmax =WEmpty +WPay Max 2

The relevant coefficient in predicting the empty weight ofan electric UAV that weighs less than 15 kg is given in (3).This equation is determined using regression analysis bycollecting all possible measurements of 83 small electricUAVs. These 83 small UAVs, including solar-, battery-, fuelcell-, and hydrogen-powered electric UAVs, weigh less than14 kg. The parameters gathered include weights, wing area(S), wing span (b), aspect ratio (AR), height, total length,root, and tip chord length of both the wing and tail surfaces.

Designmodel

Solarirradiance

Electricpropulsion

Stability& control

Sensitivitystudies

Trendanalysis

Performanceanalysis

Aerodynamicestimation

Massestimation

Missionprofile

Figure 1: Solar-powered electric UAV design element.

Start UAV design program

Solar irradiance (9)

Aircraft parameters

Parameter initializationAltitude = 0 meterVelocity = 2 km/hr

AOA & tail incidence angle = 0°Maximum take‐off mass = 3 kgTotal power guess = 10 Watts

Structure mass-to-wing area ratio = 0.34 kg/m2

Standard atmosphere

Outputs: various data in tables

UAV design program ends

Difference between initializedand estimated ≤ 10%?

No

Yes

Iteration ofinitializedparameters

Airfoil section

Aerodynamics ((4)—(8))

Stability & control ((12)—(15))

Performance ((10) and (23)—(32))

Mission profile (11)

Mass estimation ((1)—(3))

Electric propulsion ((17)—(22))

Figure 2: UAV design algorithm flowchart.

2 International Journal of Aerospace Engineering

WEmpty = 0 79 × b18 9012S−9 4755AR−9 4558W0 99TOmax 3

Similarly, in aerodynamic estimation, specific lift anddrag coefficient estimation was performed based on variouswing and horizontal tail airfoil characteristics [21]. However,the fuselage and vertical tail characteristics have yet to beincorporated in this study. Additional power is incorporatedinto the mission power requirement, specifically by defin-ing the maximum power consumption during maneuverPManeuver at total mission power, compensating for the dragincrement that remains unaccounted due to the fuselage andvertical tail.

The Reynolds number (RN) is a major factor in aerody-namic analysis. The RN range for an aircraft depends onthe mean aerodynamic chord length (c), airspeed (V), airdensity ρ , and viscosity μ . Equation (4) [21] defines therelation between these parameters.

RN =Vcρμ

4

The aerodynamic submodel also predicts the lift coeffi-cient by using a well-known theoretical estimation [21], asshown in (5). In level flight, the aircraft lift is equal to thetake-off weight. Because the air density, airspeed, and wingarea are known, the lift coefficient, CL, may be estimated.

CL =L

1/2ρV2S=

WTOmax1/2ρV2S

5

The drag coefficient CD can be estimated using (6) [21],where the aircraft zero-lift-drag coefficient CDo can beobtained from the airfoil polar data at each RN. A linearinterpolation is performed on these airfoil data to obtainCDo for a specific RN, angle of attack, and airspeed of an air-craft. Again, the vertical tail and fuselage drag componentwere not accounted since most high-endurance and light-weight UAVs have narrow fuselage. In addition, most ofthese UAVs are hand-launched, thus without a landing gear.

CD = CDo W + CDi W + CDo HT + CDi HT 6

Then, with the CL calculated at each RN using (4) earlier,the lift and drag may also be determined using

L =12ρV2SCL, 7

D =12ρV2SCD 8

Apart from the mass and aerodynamic estimation, thesolar irradiance and mission profile were also incorporated.Integrations of these two design components have beendetailed. Solar irradiance estimates include details such asspecific time of the day, date of the year, and latitude and lon-gitude coordinates [24] instead of a simplified average valueof solar irradiance data for a particular day in a nearby majorcity. This estimation has enabled the prediction of the usabil-ity of a solar-powered aircraft in various parts of the worldand its operation duration throughout the year.

The amount of solar irradiance Irmax that strikes theEarth, as given in (9), is critical in designing a solar-powered UAV. The power available through the solarmodule facilitates the sufficiency of the solar energy har-nessed for the power required. The equation is adopted from[23–27], which begins by determining the input parameters,including the day of the year, latitude, longitude, and altitudeof the place of interest.

Irmax =1 + 0 033cos 0 017203DN π/180 sin SOLALT π/180

3 6 1000/4 8708

9

No previous work has estimated in detail the UAV solarpower usage of the mission profile (i.e., not only cruise butalso the other mission profile phases, such as climb, loiter/maneuver, and descent). The effects of gust wind speed anddirection were disregarded. The power required PRequiredfor a level flight determined by the definition of the wing areais shown in (10) [21]. This calculation has not taken into con-sideration the flight path angle, the efficiency of the electricpropulsion system, and the additional power consumptionof the aircraft from the control system and payload.

PRequired =CD

CL

2AR WTOmax3

ρCLb2 10

The power consumption of each mission profile phase isdetermined based on the flight path angle, as proposed bySherwin [28]. Detailed mission profile power sizing is crucialespecially when designing an aircraft for high endurance. Thepercentage of time spent on a 24-hour mission during cruisemay be higher in comparison with that on a one-hour mis-sion. Regardless of mission endurance, the climb and descentphase is similar. Thus, an appropriate ratio portion of eachmission profile phase has been considered which includesclimb, cruise, maneuver, and descent, as shown in (11) inrespective order, instead of assuming that the aircraft onlyflies in level flight. The time fraction defined is intended fora specific mission study. This mathematical design model isassumed to be applicable only for an unmanned aircraftwithout a landing gear to achieve maximum endurance orrange by flying, primarily, in level flight.

PTotal‐Mission =0 160

PClimb +59 560

PCruise

+0 360

PManeuver +0 160

PDescent

11

The stability and control analysis measures the tail planeand boom length to ensure that the UAV is statically stable.This design property ensures the viability of the overallaircraft design-sizing simulation results. Determining thestability and controllability of the designed UAV plays a sig-nificant role in developing a well-designed UAV. Only staticstability and control have been considered in this research.No work has yet been done on aircraft dynamics, which isrecommended for further studies.

3International Journal of Aerospace Engineering

In the proposed method, the parameters evaluated are thecenter of gravity (i.e., cg) range, trimmed elevator deflectionangle, and trimmed angle of attack. The airfoil moment coef-ficient data are obtained by linear interpolation, as performedin the aerodynamic estimation, to determine these parame-ters. With these values and tail area SHT and tail momentarm length lHT , the tail volume ratio (VHT) is estimatedusing (12). Also, the tail zero-lift-pitching moment coeffi-cient CMo HT is then estimated using (13) [21], whereCLα HT is the lift curve slope of the tail, εo is the downwashangle at zero lift, iW is the wing incidence angle, and iHT isthe tail incidence angle.

VHT =lHTc

SHTS

, 12

CMo HT =VHTCLα HT εo + iW + iHT 13

The aircraft’s trimmed angles of attack αtrim and elevatordeflection angles elevtrim are estimated. The aircraft’s zero-lift-pitching moment coefficient CMo is first determinedusing (14) [21], where CMo W is the wing zero-lift-pitchingmoment coefficient. The trimmed angle of attack and eleva-tor deflection angle can be calculated using (15) and (16)[21], where CMcgα is the moment coefficient curve slope.Determining the aircraft’s trimmed angle of attack at variouselevator deflection angles gives a guideline to the requiredelevator deflection angle range that is within the allowableairplane angle of attack. Determining the elevator deflectionrange guarantees controllability by avoiding excessive pitchup (which can lead to stall) and pitch down.

CMo = CMo W + CMo HT, 14

αtrim =CMoCMcgα

, 15

elevtrim =CMo + CMcgααWVHTCLα HT

16

The current practices of propulsion system selection forcommon electrically driven UAVs are imprecise. This pro-pulsion sizing process gets more complex in a solar-augmented UAV. Yet, UAV designers commonly use thestandard combination sets of the electric motor, gearbox,electronic speed controller, propeller, and battery recom-mended by model aircraft part manufacturers or suppliersfor a specific UAV weight range. This process typically yieldsa margin of approximately 25% between the lower and upperweight for a given standard combination set.

Multiple combination sets are recommended, clearlyindicating the wide range of flexibility and combinationapplicable to a specific unmanned aircraft. Thus, preevalu-ated sets of electric propulsion systems are obviously subop-timal options for aircraft designers. Electric propulsionanalysis has been developed to optimize aircraft power andcombination selection (i.e., battery, electronic speed control-ler, and electric motor and propeller sizes) of the propulsionsystem based on the desired aircraft performance.

In the current work, the electric propulsion system sizingis given by [29]. The electric motor efficiency ef fMotor canbe estimated using (17) where PMotor‐Out is the power out ofthe motor and PMotor‐In is the power into the motor fromthe battery. Then, the power from the electric motor to thepropeller PProp‐In can be estimated using the motor voltageVMotor and motor current IMotor as shown in (18). Mean-while, the required power from the propeller PProp‐Reqbased on the simulated propeller diameter Diam and pitchPitch size and propeller revolution per minute RPM canbe evaluated as given in (19).

ef fMotor =PMotor‐OutPMotor‐In

, 17

PProp‐In = ef fMotorVMotorIMotor, 18

PProp‐Req =0 5ρg

RPM60

30 0254Diam 4 0 0254Pitch

19

Here, the error difference between the input and requiredpropeller power can be determined, where within ±10% ispreferred. This error difference limits and ensures that a suit-able range of the propeller is obtained. In addition, the idealperformance occurs when the pitching velocity of the propel-ler VProp‐Pitch given in (20) should be within 2.5 and threetimes the aircraft stall speed [29]. The thrust of the propellerThrustPr op estimated using (21) must be more than 25% ofthe maximum take-off mass of the UAV to have reasonableclimb and acceleration capabilities.

VProp‐Pitch =RPM60

0 0254Pitch , 20

ThrustProp =PProp‐Req

gVProp‐Pitch21

Once a list of a simulated propeller’s diameter and pitchsize combination has been produced with the criteria listedabove, the optimized propeller size is chosen based on theleast propeller tip static velocity VProp‐Tip‐Static . This veloc-ity, estimated using (22), can provide the optimum propellersize combination for a better UAV performance in terms ofhigh endurance and long range.

VProp‐Tip‐Static = πRPM60

0 0254Diam 22

UAV performance analysis may be conducted to predictthe effectiveness of the to-be-designed aircraft. The mainperformance criteria that are considered are velocity, stallvelocity VStall , required thrust TRequired , available energyEAvailable , and required power PRequired . The VmaxCell isthe maximum voltage of a battery cell, SCell is the numberof battery cells in series, PCell is the number of battery cellsin parallel, and QCell is the capacity of each battery cell. Theseperformance analyses are computed using (23), (24), (25),(26), and (27) in respective order.

4 International Journal of Aerospace Engineering

V =WTOmax1/2 ρCLS

, 23

VStall =WTOmax

1/2 ρCLmax WS, 24

TRequired =WTOmaxCL/CD

, 25

EAvailable =VmaxCellSCellPCellQCell, 26

PRequired = TRequiredV =CD

CL

2WTOmax3

ρCLS27

Stall velocity estimation is important prior to flight test-ing to prevent the aircraft from stalling. Ensuring that theavailable power is greater than the required power is crucial,not only for the aircraft to take-off and/or climb easily but forthe anticipation of any gusts during flight. The excess avail-able power PExcess can be estimated using (28) where theinitial power of a mission PInitialise Guess is guessed. Thisinitial power will be iterated to ensure that the error differ-ence between the initial guess and actual mission power(11) is within ±10%.

PExcess = PInitialise Guess − PRequired 28

The rate of climb and rate of optimal glide sink evalua-tion give a rough estimation of the required time to achievethe desired altitude. These parameters can be calculated using(29) and (30) in respective order [21].

Rate of climb =PExcessWTOmax

, 29

Rate of optimal glide sink =VL/D

30

Finally, the aircraft’s endurance and range are deter-mined. These two variables of aircraft electric propulsioncapability for solar and battery flight are identified using anassumption of no wind and estimated using (31) and (32)[23–27]. Studies will be conducted using the performancecriteria trade and optimization. This process will improvethe aircraft design configuration and optimize its capabilities.

EnduranceS&B =EAvailablePRequired

+ 0 7tDaylight, 31

RangeS&B =V × enduranceS&B 32

In the mathematical design model, a new assessmentscheme is developed to analyze the sensitivity of variousparameters that exhibited the greatest and least effects onthe UAV design. Evaluating how the entire design changeswith parameter variations is important to ensure that thefidelity of specific models remains acceptable when changeis introduced. Technologies were identified to considerablyenhance the design or improve the system performance.

Many components are evidently becoming smaller,more powerful, and more highly efficient than previousones. A crucial approach is developed to assess the futuretrends in UAV designs. This approach evaluates the rate atwhich aircraft parts and component technologies aredeveloping and projects their achievable or potential per-formance. The UAV mathematical design model can be usedto identify the parts that have the most significant effectand that merit further research to yield the best prospectsin UAV development.

3. Result and Discussion

A general analysis work was conducted on the mathematicaldesign model that covers both solar (solar- and battery-pow-ered) and nonsolar (battery-powered) UAV sizing. Althoughthis design model can be used for design estimations for largeUAVs, all calculations were explicitly focused on small hand-launchable UAVs weighing less than 4 kg. The simulationwas also subject to UAVs that can perform a minimum of 9hours of flight operation beginning at 12 noon on 22 June2014 at Cranfield, United Kingdom, with an iteration sizingaccuracy of 1 cm or 1 g.

Figure 3 shows the analysis results for both solar andnonsolar UAV configurations. The UAV take-off mass canbe improved to sustain 9 hours of flight duration at a speci-fied payload. The take-off mass of both solar and nonsolarUAVs nonlinearly increased with the payload. These nonlin-ear curves are mainly due to the dependence of the mass ofthe aircraft structure on the corresponding wing aspect ratioand span.

In this study, the feasible UAV design was restricted tothe specified flight duration and payload. In this case, solarand nonsolar UAVs must have maximum take-off massesof 8.209 kg and 8.987 kg, respectively, and must be capableof supporting payloads weighing 1.258 kg and 3.04 kg,respectively, to achieve 9 hours of flight duration. Theflight duration must be decreased to increase the payloador vice versa.

The relationship between the wing span and the mass ofthe structure at a constant payload of 0.35 kg and an aspect

0

0.5

1

1.5

2

2.5

3

3.5

4

0

Maximumtake‐off

mass(kg)

Payload (kg)

Solar UAVNonsolar UAV

10.80.60.40.2

Figure 3: Payload versus maximum take-off mass of the solar andnonsolar UAVs.

5International Journal of Aerospace Engineering

ratio of 12 is plotted in Figure 4. These data also identifiedthat the wing span of 2.55m is suitable for both types offlights at an aspect ratio of 12. This innovative designapproach can be used to design and develop a single airframestructure suitable for both solar and nonsolar configurations.

The relationship between the wing span and aspect ratioat a constant payload of 0.35 kg is shown in Figure 5. Thesolar UAV configuration showed that the wing spanincreased more steeply than that of the nonsolar UAV con-figuration. This result is a clear sign of the structure rigidityissue. This issue is associated with the increase in wing spanand decrease in wing chord to maintain the wing area. In thisanalysis criterion, the aspect ratio suitable for solar and non-solar UAV configuration is 15.2. This value is fairly close tothe aspect ratio of 16.4 that is suitable for the actual devel-oped UAV [1], which was designed to carry a payload of0.54 kg or 18% of the maximum take-off mass.

On a clear sunny day, approximately 55.5W of thesolar module power can be obtained per square meterwing area using an off-the-shelf 6% efficiency PowerfilmMP3-37 flexible solar cell specification at a constant aspect

ratio of 12 (Figure 6). However, only some of the pointswere close to the maximum achievable solar power (blue linein Figure 6). The issue may be resolved by performing furthersimulations with different aspect ratios to optimize the solarmodule power achieved in solar UAV sizing.

The relationship between the mass of the payload andbattery of both solar and nonsolar UAVs (Figure 7) indi-cates that the mass of the battery gradually increases withpayload. This result can be attributed to the minimum sizeof the battery pack, which comes in multiples of 150 g±10%. This finding proves another advantage of applyingthe electric propulsion modeling as part of this designmodeling, where sizing small UAVs provides an accurateweight prediction.

The curve also shows that at a fixed wing area, the batterymass of the solar UAV is almost a third larger than that of thenonsolar UAV at 0.9 kg payload. This clearly shows that at0.9 kg payload, the solar UAV wing is fully covered by thesolar module. Therefore, the mass of the battery has to beincreased to compensate the limitation with the area avail-able for solar module placement and its weight addition tothe overall maximum take-off mass.

Similarly, Figure 8 shows the relationship between thepayload and the mass of the propulsion for both solar andnonsolar UAVs. In this figure, the curve shows that the pro-pulsion mass of the solar UAV is higher by almost a thirdthan that of the nonsolar UAV (0.9 kg payload). This resultclearly signifies the effect of propulsion mass which includesthe propeller, electric motor, electronic speed controller, gearbox, and propeller adapter at a specified payload.

The required power (total mission) for both solar andnonsolar UAVs as a function of maximum take-off mass isillustrated in Figure 9. Although the effect of the minimumbattery pack mass can be observed for the low UAV mass, asmoother correlation is observed as the values increased.Again, this result can be attributed to the minimum size ofthe high-power-density lithium polymer cell, which comesin multiples of 50 g± 10%. Since the system studied hereoperates at 3 battery cells in series, a minimum battery packmass comes in multiples of 0.15 kg. Thus at an aircraft mass

1.41.61.8

22.22.42.62.8

33.2 Wing

span(m)

2.55 m

0Mass of structure (kg)

Solar UAVNonsolar UAV

0.50.40.30.20.1

Figure 4: Mass of the structure versus wing span of the solar andnonsolar UAVs at 0.35 kg payload.

1.41.61.8

22.22.42.62.8

33.2 Wing

span(m)

0Aspect ratio

Solar UAVNonsolar UAV

252015105

15.2

Figure 5: Aspect ratio versus wing span of the solar and nonsolarUAVs at 0.35 kg payload.

0

10

20

30

40

50

60 Solarmodulepower(W)

Solar module power

Wing area (m2)

Solar module limit

0 10.80.60.40.2

Figure 6: Wing area versus solar module power of the solar UAVs.

6 International Journal of Aerospace Engineering

of 1.5 kg, the power consumption rises slowly due to theincrease in the control and propulsion system.

This UAV design model estimates that a nonsolar UAVcan only carry a maximum of 3 kg for a flight of 9 hours oronly 0.1 kg for a flight of almost 23 hours at an aspect ratioof 12. However, a solar UAV that operates in Cranfield,UK, during the summer can fly for 24 hours and 9 hours with0.1 and 1.3 kg payloads, respectively.

Currently, nonsolar UAVs can carry more payload atany particular time and place than solar UAVs, at a givenendurance requirement. However, the principal weaknessof nonsolar UAVs is their poor endurance. This weaknesscan be addressed using a solar UAV, which has a high-endurance capability.

The choice of the electrically powered UAV typeshould be based on the technical requirements specified bythe customer. A solar- and battery-powered electric UAV is

preferred on missions that require flight durations of morethan 24 hours regardless of the amount of payload it cancarry. Otherwise, a battery-powered UAV may be opted for.

The needs of a solar-augmented battery-powered vehi-cle rather than of a battery-only flight vehicle must beevaluated carefully in terms of location and date of opera-tion in the year. Determining the number of days adesigned solar UAV engages in full operation is necessaryfor selecting the appropriate type of electrically poweredflight vehicle. This simulation was conducted specificallyfor a UAV to be operated on a summer day in Cranfield.The developed solar irradiance submodel offers an efficientdesign optimization and customization process for variousUAV operating locations.

The validation results of this study involve design model-ing based on the three new and three improvised designproperties that substantially improved the final UAV design

.

.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0

Mass of battery(kg)

Payload (kg)10.80.60.40.2

Solar UAVNonsolar UAV

Figure 7: Mass of the payload versus battery of the solar and nonsolar UAVs.

0

0.025

0.05

0.075

0.1

0.125

0.15

0.175

0.2

0.225

0.25 Mass ofpropulsion

(kg)

0Payload (kg)

10.80.60.40.2

Solar UAVNonsolar UAV

Figure 8: Mass of the payload versus propulsion of the solar and nonsolar UAVs.

7International Journal of Aerospace Engineering

characteristics and performance. Table 1 shows the compar-ison of all small solar-powered UAVs to the UAV designedusing the current mathematical design model.

The UAV developed using the current design modelhad a 25% smaller ratio between power consumption andtake-off mass than its closest competitor. Nevertheless, theAtlantikSolar AS-2 UAV design is a well-designed solar-powered UAV, yet the proposed design methodology maybe used to further improve the design. This is becausethe power-to-mass ratio of the Cranfield Solar UAV isslightly better than that of AtlantikSolar AS-2 UAV. Adetailed aircraft specification of the Cranfield Solar UAV isspecified in Table 2.

The comparison further affirms the importance and needof the three new and three improvised design properties tooptimize the UAV final design. The optimization of powerand the propulsion system in UAVs based on the desired air-craft performance indicates that the new electric propulsiondesign properties are main contributors to the 25% reductionin the power consumption-to-take-offmass ratio. The overallenhancement of the developed mathematical design modelensures that the final UAV design is improved in all aspectsof design optimization.

4. Conclusion

A comprehensive UAV mathematical design model withnine design properties was developed. This design modelconsists of three new aspects of design consideration andthree improved design properties. This work substantiallyenhanced the final UAV design characteristics and perfor-mance for a high-endurance mission. Design validationshowed a 25% smaller ratio between power consumptionand take-off mass in the UAV designed using the currentmathematical design model than in previous UAVs. Solar-(solar and battery) and nonsolar- (battery-only) poweredUAVs were also compared. The nonsolar UAV can carrymore payload at any particular time and place than a solarUAV, at a given endurance requirement. However, theendurance of a nonsolar UAV is its principal limitation.This feature can be addressed by using a solar UAV witha high-endurance performance. Payload has the highesteffect on the maximum take-off mass, followed by the bat-tery, structure, and propulsion mass. Ultimately, the typeof an electrically powered vehicle to be selected will bein accordance with the technical requirements specifiedby the customer.

0

2

4

6

8

10

12

14

0

Requiredpower(W)

Maximum take‐off mass (kg)

Solar UAVNonsolar UAV

43.532.521.510.5

Figure 9: Maximum take-off mass versus required power of the solar and nonsolar UAVs.

Table 1: Comparison of current and previous small solar-powered UAV designs.

Research UAV Aircraft Mass (kg) Wing span (m) Endurance (hr)Cruise

power (W)Power/take-offmass (W/kg)

AcPropulsion So Long 12.8 4.75 48 95 7.42

Technion Sun-Sailor 3.6 4.2 11 40 11.11

Swiss Federal Institute ofTechnology of Zurich

Sky-Sailor 2.4 3.2 27 15 6.25

Swiss Federal Institute ofTechnology of Zurich

Sun Surfer 0.12 1 — 1.77 14.75

Swiss Federal Institute ofTechnology of Zurich [10, 11]

AtlantikSolar AS-2 UAV 6.93 5.65 28 39.7 5.73

University of Minnesota [7, 8] SUAV:Q hybrid 3.364 4 12 30 8.92

Cranfield University Current work 3 3.7 23.0 15 5.00

8 International Journal of Aerospace Engineering

Nomenclature

WTOmax: Maximum take-off weight (N)WStruct: Structure weight (N)WEmpty: Empty weight (N)WSolar: Solar power system weight (N)WBatt: Battery weight (N)WCtrl: Control system weight (N)WPay Max: Maximum payload weight (N)ef fElectric: Electric propulsion system efficiency (%)b: Wing span (m)c: Mean aerodynamic chord length (m)CLmax W: Wing maximum lift coefficientCMo HT: Tail zero-lift-pitching moment coefficientSCell: Number of battery cells in series in a battery

packQCell: Battery cell’s capacity (A/hr)PCell: Number of battery cells in parallel in a battery

packVmaxCell: Battery cell’s maximum voltage (V)tDaylight: Flight duration with daylight (hr)DN: Day numberCMo: Aircraft zero-lift-moment coefficientCMo W: Wing zero-lift-moment coefficientV : Airspeed (m/s)lHT: Tail moment arm (m)

SHT: Tail area (m2)P: Power (W)iW: Wing incidence angle (°)iHT: Tail incidence angle (°)ρ: Air density (kg/m3)g: Gravitational acceleration at the chosen

altitude (m/s2)μ: Viscosity (kg/ms)S: Wing area (m2)CDo W: Wing zero-lift-drag coefficientCDi W: Wing induced drag coefficientAR: Aspect ratioPProp‐In: Power input to the propeller (W)PProp‐Out: Power output to the propeller (W)PMotor‐In: Power input in the electric motor (W)PMotor‐Out: Power output in the electric motor (W)PProp−Req: Power required from the propeller (W)EAvailable: Energy available (W/hr)SOLALT: Solar altitudeαtrim: Trimmed aircraft’s angle of attack (°)elevtrim: Trimmed elevator deflection (°)VMotor: Electric motor’s operational voltage (V)RPM: Electric motor’s shaft rotation per minute

(rpm)IMotor: Electric motor’s current (A)Diam: Propeller’s diameter (in)Pitch: Propeller’s pitch (in)VProp‐Tip‐Static: Propeller tip static velocity (m/s).

Conflicts of Interest

The authors declare no conflict of interest.

Authors’ Contributions

Parvathy Rajendran and Howard Smith conceived anddesigned the experiments, Parvathy Rajendran performedthe experiments, Parvathy Rajendran and Howard Smithanalyzed the data, and Parvathy Rajendran and HowardSmith wrote the paper.

Acknowledgments

This publication was supported by Universiti Sains MalaysiaGrant no. 304/PAERO/6315002.

References

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Table 2: Cranfield solar UAV specification.

Description Specification

Wing airfoil AG24, AG25, AG26

Tail airfoil HT21

Length (m) 1.8

Wing span (m) 3.7

Wing aspect ratio 16.4

Wing area (dm2) 83.6

PowerFilm MP3-37 solar module area (dm2) 79.0

Peak electric motor operating power (W) 500

Structure mass (kg) 0.986

Hacker X-40 Pro electronic speedcontroller mass (kg)

0.050

Hacker A30-14L electric motor &mounting mass (kg)

0.160

13′× 8′ folding propeller & propelleradapter mass (kg)

0.020

Eyecam 2.4 GHz micro wireless videocamera mass (kg)

0.050

LC-18650-JP-2600 lithium-ion polymerbattery pack mass (kg)

1.050

Futaba R617FS 7-channel 2.4GHz receivermass (kg)

0.010

Hitec HS-85MG Servos mass (kg) 0.044

Eagle Tree inboard system mass (kg) 0.200

Connector & wire mass (kg) 0.030

Total solar module mass (kg) 0.400

Take-off mass (kg) 3.0

9International Journal of Aerospace Engineering

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