(HALE) Unmanned Aerial Vehicle (UAV)

AIAA 2008-168

46th AIAA Aerospace Sciences Meeting and Exhibit 7 - 10 January 2008, Reno, Nevada

First Order Effects of New Technology on a High Altitude Long Endurance (HALE) Unmanned Aerial Vehicle (UAV) Jeremy S. Agte1 U.S. Air Force Academy, Colorado Springs, CO 80841

Downloaded by UNIVERSITY OF CINCINNATI on November 25, 2014 | http://arc.aiaa.org | DOI: 10.2514/6.2008-168

Kelly Cohen2 University of Cincinnati, Cincinnati, OH 45221

This paper is a continuation of work performed in the capstone aircraft design course at the United States Air Force Academy. The task for this course was to design a high altitude long endurance UAV capable of a staying aloft for 48 hours at an altitude of 65K ft and a standoff distance of 3000 NM. By comparison, the current technological benchmark is the Global Hawk, advertised at an endurance of 24 hours at 65K ft and a standoff distance of 1200 NM. As the scope of the design project and the course’s educational goals were quite extensive, this paper focuses on only one important outcome of the follow-on results, namely that achieving the desired 48 hour endurance and 3000 NM standoff distance will require the aggressive use of cutting edge technology, likely available in the next five to ten years, as well as advanced flight profiles.

Nomenclature BSFC GTOW Hp/Wto L/D Re

= = = = =

brake-specific-fuel-consumption, lb/hp/hr Gross Take-Off Weight, lb horsepower-to-weight ratio lift-to-drag ratio Reynold’s number

S TSFC T T/Wto Wto/S

= = = = =

wing planform area, ft2 thrust-specific-fuel-consumption, 1/hr thrust, lb thrust-to-weight ratio wing loading, lb/ft2

I. Introduction

O

mnipresence in the near space envelope is a key component of persistent Intelligent Surveillance and Reconnaissance (ISR). An unmanned aerial vehicle having augmented performance, namely ultra long endurance, can lend itself flexibly to many effective concepts of operation, such as communications relay on the military side or disaster management on the civilian side. For example, during the recent (October 2007) California wildfires, both the Predator based Ikhana UAV as well as the Global Hawk UAV were utilized. Such diverse applications have led UAV development along two very distinct and opposite paths, namely the micro and the mega. Micro UAVs having a wingspan of less than six inches were developed to be portable and travel to places where humans cannot go. In contrast, the trend of mega UAVs is mainly for increased time aloft attempting to extend the endurance of UAVs from one day to about five years as exemplified by DARPA’s Vulture UAV program.1 A more conventional method of extending aircraft endurance is airborne refueling. In 2007, DARPA completed its Autonomous Airborne Refueling Demonstration program. This endeavor included over ten test flights using an autonomous F/A-18 refueled by a 707-300 tanker.2 However, unmanned aerial refueling is technically complex and may cause limited operational flexibility. An alternative approach to increasing the time-on-station calls for the incorporation of system and subsystem level technological enhancements. One of the UAV’s most desired attributes is persistence, as demonstrated in the Global Hawk (RQ-4A/B)3, which has been engineered for extreme range and altitude, making it quite large. There is a growing requirement to further 1

Instructor, Dept. of Aeronautics, Aircraft Design, Major, USAF, [email protected], AIAA Member. Associate Professor, Dept. of Aerospace Eng. and Eng. Mechanics, [email protected], AIAA Associate Fellow. 1 American Institute of Aeronautics and Astronautics 2

010908 This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States.

increase endurance levels, made even more critical by the success of current UAV platforms such as the Global Hawk, and the resulting increase in their demand. At the same time, budget constraints force the reduction in their overall size, weight, and cost. As a first step, the authors consider the effort to double the endurance time to 48 hours, believing that recent technological developments in the fields of aerodynamics, structures, controls, propulsion, and multi-disciplinary optimization can be leveraged to permit UAVs to have an augmented impact on U.S. Air Force missions. Table 1, based on a survey by Tsach,4 summarizes the trends concerning technological enhancements in HALE UAVS. Using an effective multi-disciplinary approach, several of these technologies can be integrated into a single viable aircraft design. Table 1: Technological Enablers for HALE UAVs4


Discipline Aerodynamics

Propulsion system

Structure/Control

Description of Technology – Potential Improvement • Advanced laminar wing design • Variable camber • Design for the unique Re and CL requirements • Active flow control potential • High Aspect Ratio wing • Improved fuel consumption (SFC) 25% - 30% • Obtained by weight reduction (T/W or Hp/W) 20% - 30% • Advanced technologies and innovative approach to Piston / Diesel, 20% - 30% (Hp/W) • Advanced technologies and innovative approach to Turboprops, 20% - 30% (Hp/W) • Advanced technologies and innovative approach to Turbofan 20% - 30% (T/W) • Weight reduction 15% - 25% • Extensive use of composites • Reduced number of parts • Optimized production process • Higher strength materials • Smart structures • Aeroelastic tailoring

II. Scope Although there are certainly numerous breakthroughs on the horizon that could have positive impact on the performance of HALE aircraft, several of which are indicated in Table 1, here the authors constrain their analysis to only four such technological impacts in order to limit the scope and demonstrate the potential benefits of what are perhaps the ‘nearest-term’ advances. The choice of which impacts to apply was driven in part by the authors’ particular backgrounds and in part by data made available from partners located in the area local to the U.S. Air Force Academy. These technological impacts are outlined in Table 2. Table 2: Technological Impacts Applied to HALE UAVs Technology Propulsion System

a. b. c. a. b. c. a. b. a. b. a. b.

Advanced Airfoils

Active Structural Control (ASC) Advanced Fuel Efficient Flight Profiles Synthesis of Technologies

Compared Configurations Baseline Turbofan Engine Advanced Turbo-prop Engine Supercharged Turbo-diesel Engine Delft DU89-134/14 Global Hawk Airfoil IAI LRT-17.5 Baseline Wing 20% Lighter Wing due to ASC Baseline Profile “Dolphin” + “Sawtooth” Profile Baseline Aircraft/Profile Best Mix of Technologies

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Several assumptions were made in order to adapt some of the specific techniques to the conceptual level design software used for this first order analysis. For instance, the application of Active Structural Control (ASC) techniques, such as maneuver load control, gust load alleviation, and active flutter suppression, was assumed to reduce overall wing weight by a goal of 20%, based upon the argument for its feasibility documented previously in Tsach.4 Additionally, a fully coupled aerodynamic analysis of each wing configuration with the three respective airfoil shapes would have been prohibitively complex and time consuming, given that the wing planform changes with nearly every design iteration. To simplify, the Global Hawk and Israeli Aerospace Industry (IAI) airfoils were assumed to result in L/D improvements of 14 and 21 percent over the baseline airfoil (DU89), an approximation derived from results in Goraj et.al.,5 Steinbuch,6 and the mission requirements package.7 These improvements were applied directly to the aerodynamic coefficients in the design software described in Section IV.


III. Methodology For the USAF Academy capstone design exercise, the design team considered an enhanced Global Hawk mission, illustrated in Fig. 1, taken from the mission requirements document.7 The main change from the specified mission for the Global Hawk was the endurance, which was doubled to 48 hours.

Figure 1: UAV Objective Mission7 Procedurally, a strict method was followed in order to assess the impact of each individual advancement, and ultimately apply several of them to the same configuration. While the characteristics in the right hand column of Table 2 represent the defining aspects of each aircraft analyzed in the trade studies, baseline aircraft geometry, as shown in Fig. 2, remained the same for each configuration as the technology was applied to the model. For example, although the end result for the sized aircraft using the baseline turboprop engine may have had a wing area and weight two-thirds that of the sized turbofan aircraft, both started with the same planform, and parameters such as wing sweep, aspect ratio, and taper ratio remained the same.

5.6° 111’ 28’ 8.3’

63’

GTOW = 13200 lb Swing = 506 ft2 ARwing = 25 Taper ratio = 0.21 Sv.stab = 70 ft2 DU89 Airfoil Max Fuselage Diameter = 4 ft

1.9’

22 March 2007, DID-4, CDR(T)

22’

Figure 2: Baseline UAV Configuration8 3 American Institute of Aeronautics and Astronautics 010908

8

Once the appropriate changes were made to the model, the optimizer varied thrust (T), Gross Takeoff Weight (GTOW or Wtakeoff), and wing area (S) to size the aircraft such that the right- and left-hand sides of the sizing equation, Eq. (1), were equal to within a certain percentage of error, while minimizing GTOW and satisfying all performance constraints.

S

W S


W

W

W

S

(1)

W S

S

During this process, the only parameter in Eq. (1) that remained constant was the payload weight, since the weight of the airframe changed with variations in S, engine weight was tied to the thrust, and fuel weight was a function of the size and shape of the aircraft. Although the optimizer treated GTOW as an independent variable, this stemmed from the fact that Wfuel was calculated as the difference of the GTOW minus the payload, airframe, and engine weights. Thus, the optimizer was forced to ensure GTOW was large enough to allow an adequate amount of fuel for the mission in Fig. 1. A further explanation of the optimization tool is found in Section IV, B.

IV. Tools A. Analysis Preliminary analysis was performed using traditional methodologies of conceptual design, executed through the use of a medium-fidelity Microsoft® Excel spreadsheet, entitled JET, developed over the past ten years by students and faculty at the U.S. Air Force Academy. The spreadsheet includes modules for aerodynamics, propulsion, weight, mission and constraint analysis, geometry, and cost, as well as various routines for performing trade studies and optimization. Results of a particular design are realized through a constraint diagram, whereby each of the constraints are measures of performance formulated as functions of thrust-to-weight ratio (T/W) and wing loading (W/S). The ultimate goal, in the traditional sense, is then to minimize overall GTOW and cost, while meeting each of those performance constraints.

Figure 3: JET Spreadsheet Front Page Fig. 3 shows the front page of the JET spreadsheet, where one inputs the majority of the parameters for mission and constraint analysis, geometry, and aerodynamics. Each of these inputs is used in several of fourteen different spreadsheet pages that analyze various aspects of the design, and changes show up instantly on the front page in the 3-view, constraint, thrust, and cross-sectional area diagrams. 4 American Institute of Aeronautics and Astronautics 010908


Of special interest for the HALE UAV analysis are the cost and engine models. The cost model is a derivative of the RAND Corporation’s Development and Procurement Costs of Aircraft model (DAPCA IV), as found in Raymer9. As the model was originally developed for fighter, bomber, and transport aircraft, several changes were made in order to accurately predict costs for an unmanned aerial vehicle, using more recent trends for UAV costs found in both Tsach4 and Moire.10 These latter two references deal mostly with civilian UAV applications, therefore were not adopted wholly for the models of this military HALE aircraft. Rather, they were used to provide insight to the most significant changes made to the DAPCA IV model, those pertaining to adjustment of maintenance man hours, crew support, and human systems cost factors. Additionally, all monetary values were updated from 1999 to 2007 dollars. The basis for the engine models in JET comes from Mattingly.11 Since the fidelity of Mattingly’s Aircraft Engine Design software (AEDsys) is somewhat better than that of the equations originally used in JET, the baseline turbofan and the turpoprop engines were designed in AEDsys using the same HALE mission profile as that entered into the Excel based software. Thrust and thrust-specific-fuel-consumption (TSFC) results from each mission leg in AEDsys were then matched to the corresponding mission legs in JET’s engine model page. The engine data for the diesel powered turboprop was entered in much the same way, but came from preliminary analysis performed by Sturman™ Industries, in Woodland Park, CO, based on technological advances that this company deems possible within the next five to ten years. The exact improvements in horsepower and TSFC are proprietary, but rough estimates regarding this type of technology can be found in Tasch (see Table 1).4 B. Optimization The optimization page is a recent addition that was developed in an attempt to provide students with a tool for understanding the basic concepts of optimization while still designing at the undergraduate conceptual level.* It is macro based and uses the ‘solver’ function of Excel to minimize or maximize the desired objective cell. The macros are depicted as buttons, as shown in Fig. 4, which can be used for weight, range, or endurance optimizations. If the user desires more flexibility in terms of setting up a different type of optimization problem, it is possible call up the ‘solver’ dialogue box and enter in one’s own objective or constraint cells.

Figure 4: JET Optimization Page *

One of the recommendations from the 2006 European-U.S. MDO Colloquium, the results of which are published in de Weck et. al.,12 was the movement of MDO education from the graduate level to the undergraduate level through the integration of optimization techniques into undergraduate conceptual design software. The primary author of this paper has seen very favorable results with the students through the use of the relatively simple optimization routine described in Section IV, B. 5 American Institute of Aeronautics and Astronautics 010908

Since the equations stemming from the objective cells manipulated by the optimizer are spread out across several different spreadsheets, it is difficult to specify any particular objective function in the traditional optimization sense. Constraints, on the other hand, are taken directly from the user’s inputs on the JET front page and nondimensionalized as shown in Eq. (2) to ensure proper weighting by the ‘solver’ algorithm. g

T/W

1

T/W

0

(2)

V. Results The optimization process described above results in a sized aircraft, minimized for weight, capable of meeting its corresponding mission requirements (Fig. 1) for each of the data points depicted in Figures 5 through 9. Each of the following figures corresponds to an application of the technology in the first column of Table 1, compared between the different configurations in the second column. A couple of notes are warranted regarding the results. First, the unit cost on the secondary y-axes is based upon a production quantity of 200 aircraft as specified in the requirements package. Although one might contest the exact accuracy of the cost model (or any of other analysis models, for that matter) due to the extreme number of variables depending on economical, political, or industrial factors, great care was taken to ensure that the same models were used for each of the analyses such that the trends from one trade study to the next would be comparable. Secondly, if not otherwise specified, the baseline engine for each of the trade studies was a medium bypass ratio turbofan engine. Finally, where the trend-lines stop before reaching the furthest values on the x-axes, the sized GTOW had become so large that a solution to Eq. (1) was no longer possible, given the aircraft geometry depicted in Fig. 2. Had the designer been allowed to change the shape of the wing, perhaps allowing a lighter wing weight by decreasing aspect ratio, solutions may have been found at these larger GTOWs. The usefulness of comparing these different geometries, however, is questionable.

GTOW and Cost vs. Endurance for Different Engines 45000

35 Turbofan GTOW

40000

Turboprop GTOW

35000

Turbodiesel GTOW

30

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30000

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Turboprop Cost Turbodiesel Cost

25000

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Unit Cost, Millions ($)

Gross Takeoff Weight, GTOW (lb)


Here, each of the performance constraints (i.e. maximum altitude, minimum required turn rate, etc.) has been formulated to output a required thrust-to-weight ratio (T/W) as a function of wing loading (W/S), based on the aircraft’s particular geometry, weight, and engine configuration. This allows the accurate depiction of the ‘solver’ optimization results on the traditional constraint diagram shown to the right in Fig. 4.

10000 5

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Endurance (hrs)

Figure 5: Weight and Cost Comparison for Various Engine Technologies Fig. 5 shows results indicating that significant weight and cost savings can be realized through the use of advanced engine technologies, especially when the mission requires ultra-long endurance times on the order of 48 hours and above. At an endurance time of 42 hours, the turboprop and diesel powered turboprop engines result in a 45% and 58% reduction in overall GTOW, respectively. The majority of this improvement is a result of the lower TSFC values at altitude for the turboprop engines; 0.27/hr for the turboprop and even lower for the turbodiesel, compared to .41/hr for the turbofan (Hp was converted to pounds of thrust at cruise conditions for purposes of 6 American Institute of Aeronautics and Astronautics 010908

comparison, i.e. these are all TSFC values and not BSFC for the prop engines). This advantage becomes disproportionately larger at the higher endurance levels, where the fuel fraction starts to increase more rapidly, thus making overall GTOW more sensitive to changes in fuel weight.

GTOW and Cost vs. Endurance for Different Wing Weights 30

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Baseline GTOW 20% Wing Weight Reduction GTOW Baseline Cost 20% Wing Weight Reduction Cost

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Figure 6: Weight and Cost Comparison Assuming 20% Wing Weight Reduction due to Active Structural Control (ASC) Results in Fig. 6 indicate the benefits of applying Active Structural Control (ASC) techniques, such as maneuver load control, gust load alleviation and active flutter suppression, in order to reduce overall wing weight by a goal of 20%, and in turn overall gross takeoff weight and cost. Again, the greatest reductions in overall weight and cost occur at the higher durations of endurance. GTOW and Cost vs. Endurance for Various Airfoils 45000

30 DU89 GTOW

40000

Global Hawk Airfoil GTOW

35000

LRT-17.5/HASA GTOW

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Figure 7: Weight and Cost Comparison for Different HALE Airfoils Fig. 7 illustrates the effect of using different airfoils to improve the L/D characteristics by 14 and 21 percent over the baseline (DU89), respectively. Here, the DU89 is an airfoil developed at Delft Technical University in the Netherlands the LRT-17 is a HALE airfoil designed in Israel.6 Of interest is that the Global Hawk airfoil results closely match those of the supposedly improved LRT-17 at GTOWs up to nearly 25000 lbs, the approximate takeoff weight of Global Hawk, confirming a successful airfoil design for that particular platform and its required mission. The use of energy efficient flight profiles is another technique that shows promise in increasing endurance time. Although research performed under an Air Force Small Business Innovative Research (SBIR) contract has indicated improvements in flight time on the order of 200% or more, the estimates used in the study here (Fig. 8) are 7 American Institute of Aeronautics and Astronautics 010908

conservative at 25%. This is to demonstrate that even small improvements can have dramatic effects on the overall GTOW at the high endurance requirements. GTOW and Cost vs. Endurance for Energy Efficient Flight Profiles 30

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Figure 8: Weight and Cost Comparison assuming 25% increase in Endurance through use of Energy Efficient Flight Profiles These profiles (sawtooth and dolphin) are based on maneuvers familiar to many pilots of high performance gliders and motor-gliders. In the case of the sawtooth maneuver, the aircraft performs a series of high-powered climbs, each followed by a power-off glide with the propeller stowed. The desired end-effect is an improvement in range and endurance due to the fact that lift-to-drag ratio in the glide is higher than that in powered flight (drag is decreased because the propeller is stowed). Its success, of course, requires that the amount of fuel burned in the climbs is less than that burned in constant altitude flight over the duration of a non-sawtooth profile equal to that of the sawtooth profile duration. This can be coupled with the dolphin maneuver to further improve performance using the energy conservation tactics of speeding up through areas of ‘sink’ and slowing down through areas of ‘lift.’ Naval Research Laboratory (NRL) high altitude gust reports indicate that this maneuver could be quite effective at 65K ft. GTOW and Cost vs. Endurance for Best Mix of Advanced Technologies 30

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Figure 9: Weight and Cost Comparison using a Mix of Several Technologies Finally, if all of the technologies are combined, the projected savings in GTOW and cost are truly phenomenal. The analysis shown in Fig. 9 incorporates the diesel powered turboprop engine, a 20% wing weight reduction due to ASC, a 25% increase in endurance time due to energy efficient flight profiles, and a 14% improvement in L/D due 8 American Institute of Aeronautics and Astronautics 010908

to an improved airfoil. With these technologies, results indicate that endurance times of 60 hours and above are not unrealistic.


VI. Conclusion Limitations of this analysis are in large part due to the fidelity of the spreadsheet software used in obtaining the results. This is not to say the authors lack confidence in these tools. On the contrary, in terms of conceptual design software, the program used in this study is likely as high in fidelity as one could hope for without moving towards much more complicated and expensive commercial products. With the recent addiction of more robust optimization routines, it has allowed very rapid first order assessment of technologies applied to various aircraft platforms, some results of which are shown above. It should be noted that while the accuracy of the actual values demonstrated in the analysis may be marginal (i.e. actual unit cost of the baseline 36 hr endurance UAV in Fig. 6 probably varies significantly around the given value of $17M, as the amount of variables affecting cost is many more than are considered in this analysis and varies considerably with company and contract type), the accuracy of the trends and sensitivities shown is quite good, as the same models are used for each analysis. Furthermore, these trends quantify the very important fact that, in order to meet the objective of an ultra-long endurance time greater than 48 hours, aggressive use of advanced technologies are not only be beneficial, but required.

Acknowledgments The authors would like to acknowledge each of the students listed under Ref. 8, for their hours of hard work on the baseline aircraft design shown in Fig. 2. Additionally, we thank Mr. Ivan Jaszlics from Pathfinder Systems in Lakewood, CO, and Mr. Jeff Gardner from Sturman Industries in Woodland Park, CO, for their technical input to the herein described analyses.

References 1

Hockmuth, C.M., “Darpa's Nonstop Flight Time: Five Years”, Aviation Week, May 18, 2007. DARPA News Release, “DARPA Completes Autonomous Airborne Refueling Demonstration”, August 9, 2007. 3 Global Hawk RQ-4 Block 10 Tech Specs, URL: http://www.is.northropgrumman.com, Northrop Grumman Corporation, accessed 19 Dec 2007. 4 Tsach, S., Peled, A., Penn, D., and Touitou, D., “The CAPECON Program: Civil Applications and economical Effectivity of Potential UAV Configurations”, AIAA-2004-6327, AIAA 3rd "Unmanned Unlimited" Technical Conference, Workshop and Exhibit, Chicago, Illinois, Sep. 20-23, 2004. 5 Goraj, Z., Frydrychewicz, A., Switkiewicz, R., Hernik, B., Gadomski, J., Goetzendorf-Grabowski, T., Figat, M., Suchodolski, St., Chajec, W., “High Altitude Long Endurance Unmanned Aerial Vehicle of a New Generation – a Design Challenge for a Low Cost, Reliable, and High Performance Aircraft,” Bulletin of the Polish Academy of Sciences, Vol. 52, No. 3, 2004. 6 Steinbuch, M., Shepshelovich, M., “Development of High Altitude Long Endurance Airfoils,” AIAA-20041052, 42nd AIAA Aerospace Sciences Meeting and Exhibit, Jan. 5-8, 2004. 7 Requirements Package, “Advanced Technology Long-endurance Aerial Surveillance (ATLAS) UAV,” Dept. of Aeronautics, U.S. Air Force Academy, AE482, Spring 2007. 8 Rochelle N., Sibal A., Ouper D., Riling E., Updike T., Andrews M., Chan D., “Final Report on the Design of an Advanced Technology Long-endurance Aerial Surveillance UAV,” Dept. of Aeronautics, U.S. Air Force Academy, Spring 2007. 9 Raymer, D., Aircraft Design: a Conceptual Approach, AIAA Educational Series, 1999. 10 Moire Inc., “Cost and Business Model Analysis for Civilian UAV Missions,” prepared for the National Aeronautics and Space Administration, June 8, 2004. 11 Mattingly, J., Heiser, W., Pratt, D., Aircraft Engine Design, AIAA Educational Series, 2002. 12 de Weck, O., Agte, J., Sobieski, J., Arendsen, P., Morris, A., Spieck, M., "State-of-the-Art and Future Trends in Multidisciplinary Design Optimization", AIAA-2007-1905, 3rd AIAA Multidisciplinary Design Optimization Specialist Conference, April 2007. 2

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