January 2015 

NASA/TM–2015-218677 

High Altitude Long Endurance UAV Analysis 

Model Development and Application Study 

Comparing Solar Powered Airplane and Airship 

Station-Keeping Capabilities 

Thomas A. Ozoroski 

Analytical Mechanics Associates, Hampton, Virginia 

Craig L. Nickol and Mark D. Guynn 

Langley Research Center, Hampton, Virginia 
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January 2015 

NASA/TM–2015-218677 

High Altitude Long Endurance UAV Analysis 

Model Development and Application Study 

Comparing Solar Powered Airplane and Airship 

Station-Keeping Capabilities 

Thomas A. Ozoroski 

Analytical Mechanics Associates, Hampton, Virginia 

Craig L. Nickol and Mark D. Guynn 

Langley Research Center, Hampton, Virginia 
Available from: 

NASA STI Program / Mail Stop 148 

NASA Langley Research Center 

Hampton, VA 23681-2199 

Fax: 757-864-6500 

Acknowledgments 

The authors would like to thank the leadership of the Systems Analysis and Concepts 

Directorate for their enduring support of this technology area. The authors would also 

like to thank Mr. Jerry Smith, Mr. Michael Marcolini, and Dr. Robert Moses for their 

valuable insights and contributions during the publication phase of this study. 

The use of trademarks or names of manufacturers in this report is for accurate reporting and does not constitute an 

official endorsement, either expressed or implied, of such products or manufacturers by the National Aeronautics and 

Space Administration. 
 

 iii 

Contents 

List of Tables ............................................................................................................................................... iv 

List of Figures ............................................................................................................................................... v 

1.0 Introduction ............................................................................................................................................. 1 

2.0 Symbols................................................................................................................................................... 2 

3.0 SolFlyte Modeling Scope and Theory .................................................................................................... 3 

3.1 Features and Utilities ........................................................................................................................ 5 

3.1.1 Mission Site and Time Inputs ................................................................................................. 5 

3.1.2 Winds and Atmosphere Model ............................................................................................... 6 

3.1.3 On-Site Operational Radius Limits and Positioning Strategies .............................................. 7 

3.1.4 Persistent and Recoverable Flight Patterns ............................................................................. 8 

3.1.5 Solar Energy Calculations ....................................................................................................... 9 

3.1.6 SolFlyte Shadowing Assessment Utility ............................................................................... 12 

3.1.7 Climbing, Descending, and Altitude Variations ................................................................... 14 

3.1.8 SolFlyte-LTA Analysis Model and Sizing Utility ................................................................ 15 

3.2 Simulation Overview ...................................................................................................................... 16 

3.2.1 Mission Operations ............................................................................................................... 16 

3.2.2 Vehicle Design and Analysis Applications ........................................................................... 17 

3.2.3 Output Description ................................................................................................................ 18 

4.0 Study Approach .................................................................................................................................... 20 

4.1 Mission Requirements .................................................................................................................... 20 

4.2 UAV Design Parameters and Assumptions .................................................................................... 21 

4.3 Heavier-Than-Air (HTA-450) Airplane Concept ........................................................................... 22 

4.4 Lighter-Than-Air (LTA-1080 and LTA-570) Airship Concepts .................................................... 27 

5.0 Mission Simulation Results and Discussion ......................................................................................... 40 

5.1 Mission Descriptions ...................................................................................................................... 41 

5.2 Mission Simulation Results and Discussion ................................................................................... 42 

6.0 Conclusions ........................................................................................................................................... 50 
 

 iv 

List of Tables

Table 1. Energy Floor and Energy Availability Metric, AvEnrg%.............................................. 19 

Table 2. Comparable Mission Operational Concept Assumptions............................................... 21 

Table 3. Comparable HTA and LTA Design Concept Assumptions............................................. 22 

Table 4. Key HTA-450 Design Parameters......................................................................... 27 

Table 5. Key LTA-570 and LTA-1080 Design Parameters....................................................... 39 

Table 6. Site and Mission Details.................................................................................... 41 

Table 7. Mission Time-Integrated NCDC Wind Data Suitability Metric, WindData%...................... 43 

Table 8. Mission Time-Integrated Energy Availability Metric, AvEnrg%..................................... 44 

Table 9. Mission Time-Integrated Power-Radius Capability Metric, PwrRad%.............................. 45 

Table 10. Vehicle and Mission Summary........................................................................... 51 
 

 v 

List of Figures

Figure 1. SE-HALE UAV model components and interactions. .................................................. 4 

Figure 2. On-Site radius limits and definitions. ..................................................................... 7 

Figure 3. Recoverable flight patterns. ................................................................................... 9 

Figure 4. Solar orientation model....................................................................................... 10 

Figure 5. Angle Definitions............................................................................................ 11 

Figure 6. Solar cell array surface transmission model. ............................................................. 12 

Figure 7. Shadowing geometry and features. ....................................................................... 13 

Figure 8. Features of the solar energy collection factor. ...........................................................14 

Figure 9. HTA altitude variations model. ............................................................................. 15 

Figure 10. ESS stored energy variations. ............................................................................... 18 

Figure 11. HTA-1b solar energy collection contours. ............................................................. 24 

Figure 12. HTA-450 solar energy collection contours. ........................................................... 24 

Figure 13. HTA-450 solar energy collection contours with Sun track and headings, wind=0 m/s. ........ 26 

Figure 14. HTA-450 solar energy collection contours with Sun track and headings, wind=20 m/s. ....... 26 

Figure 15. HTA-450 top and isometric views. ..................................................................... 27 

Figure 16. LTA-1080 parametric size variation as a function of maximum speed. ........................... 28 

Figure 17. LTA-1080 parametric size variation as a function of energy usage speed. ........................ .29 

Figure 18. LTA-1080 parametric size variation as a function of energy usage time. .......................... 29 

Figure 19. LTA-1080 parametric size variation as a function of solar energy collection time. ............. 30 

Figure 20. LTA-1080 bottom view schematic: propulsion and payload (green) pods. ....................... 30 

Figure 21. LTA-1080 isometric, top, and front views. ............................................................. 31 

Figure 22. LTA solar energy collection contours: Santa Teresa, array mount angle = 90o. ................. 33 

Figure 23. LTA solar energy collection contours: Santa Teresa, array mount angle = 80o. ................. 33 

Figure 24. LTA solar energy collection contours: Santa Teresa, array mount angle = 70o. ................. 34 

Figure 25. LTA solar energy collection contours: Santa Teresa, array mount angle = 65o. ................. 34 

Figure 26. LTA solar energy collection contours: Santa Teresa, array mount angle = 60o. ................. 35 

Figure 27. LTA solar energy collection contours: Santa Teresa, array mount angle = 50o. ................. 35 

Figure 28. LTA solar energy collection contours: Santa Teresa, array mount angle = 40o. ................. 36 

Figure 29. LTA solar energy collection contours: Santa Teresa, array mount angle = 30o. ................. 36 

Figure 30. LTA normalized solar energy collection: Santa Teresa, December 21. ........................... 37 

Figure 31. LTA normalized solar energy collection: Santa Teresa, March 21. ............................... 37 

Figure 32. LTA normalized solar energy collection: Santa Teresa, June 21. ................................. 38 

Figure 33. LTA time-integrated normalized solar energy collection: Santa Teresa. ......................... 38 

Figure 34. Scaled top-views of three HALE UAV concepts compared to NASA Langley Hangar. ....... 40 

Figure 35. Scaled side-view of LTA-1080 outside NASA Langley Hangar. .................................. 40 
 

 vi 

Figure 36. Mission sites map. ........................................................................................ 41 

Figure 37. NCDC wind data suitability metric per mission site. ................................................ 43 

Figure 38. Energy availability metric per mission site. .......................................................... 44 

Figure 39. Power-radius capability metric per mission site. ..................................................... 45 

Figure 40. HTA-450 orientations to the Sun, 30-day Santa Teresa and Wallops Island missions........... 48 

Figure 41. LTA-1080 orientations to the Sun, 30-day Santa Teresa and Wallops Island missions. ........ 49 

 
 

1 

Abstract 

There have been ongoing efforts in the Aeronautics Systems Analysis 

Branch at NASA Langley Research Center to develop a suite of integrated 

physics-based computational utilities suitable for modeling and analyzing 

extended-duration missions carried out using solar powered aircraft. 

From these efforts, SolFlyte has emerged as a state-of-the-art vehicle 

analysis and mission simulation tool capable of modeling both heavierthan-air (HTA) and lighter-than-air (LTA) vehicle concepts. This study 

compares solar powered airplane and airship station-keeping capability 

during a variety of high altitude missions, using SolFlyte as the primary 

analysis component. Three Unmanned Aerial Vehicle (UAV) concepts 

were designed for this study: an airplane (Operating Empty Weight 

(OEW) = 3285 kg, span = 127 m, array area = 450 m2

), a small airship 

(OEW = 3790 kg, length = 115 m, array area = 570 m2

), and a large 

airship (OEW = 6250 kg, length = 135 m, array area = 1080 m2). All the 

vehicles were sized for payload weight and power requirements of 454 kg 

and 5 kW, respectively. Seven mission sites distributed throughout the 

United States were selected to provide a basis for assessing the vehicle 

energy budgets and site-persistent operational availability. Seasonal, 30-

day duration missions were simulated at each of the sites during March, 

June, September, and December; one-year duration missions were 

simulated at three of the sites. Atmospheric conditions during the 

simulated missions were correlated to National Climatic Data Center 

(NCDC) historical data measurements at each mission site, at four flight 

levels. Unique features of the SolFlyte model are described, including 

methods for calculating recoverable and energy-optimal flight trajectories 

and the effects of shadows on solar energy collection. Results of this study 

indicate that: 1) the airplane concept attained longer periods of on-site 

capability than either airship concept, and 2) the airship concepts can 

attain higher levels of energy collection and storage than the airplane 

concept; however, attaining these energy benefits requires adverse design 

trades of reduced performance (small airship) or excessive solar array 

area (large airship). 

1.0 Introduction

The lineage of this study traces to the early 2000’s as the latest of several related research efforts 

undertaken by the Aeronautics Systems Analysis Branch (ASAB) at NASA Langley Research Center. In 

2006, for example, a significant research effort investigated a wide range of potential Solar-Electric (SE) 

Unmanned Aerial Vehicle (UAV) types and technology candidates for High Altitude Long Endurance 

(HALE) missions. The results of the study were published in 2007 in a detailed 111-page report entitled, 

“High Altitude Long Endurance UAV Analysis of Alternatives and Technology Requirements 

Development” (NASA/TP-2007-214861, ref. 1). Other examples include the feasibility study for the UAV 

sector under the NASA Vehicle Systems Program, the ICESat mission quick-look study for the NASA 

Langley Systems Engineering Directorate, the hurricane hunter mission study for Langley’s Science 

Directorate and, most recently, the Vulture mission analysis for DARPA. 

 
 

2 

As a by-product of supporting these past studies, research-level SE-HALE UAV analysis procedures 

had been developed within the ASAB; but inherent limitations indicated a strong need for new analysis 

capabilities. For example, the Airship Design and Analysis Code (ADAC, ref. 2) provided a narrowly 

focused capability for analyzing airship concepts; but ADAC could not be generalized or modified for use 

in most applications of interest. Other modeling deficiencies emerged when balancing conflicting fidelity 

levels or when implementing proprietary, restrictive, or stand-alone utilities within the analysis pathway. 

Overcoming these obstacles required adapting existing tools and procedures (sometimes with significant 

effort) to facilitate smooth interactions between those components suitable for the task at hand. 

Shaped by the lessons learned from previous efforts, a computational analysis model was envisioned 

for meeting the unique vehicle analysis and mission simulation requirements of SE-HALE UAV concepts. 

Key applications of the model were projected to include the ability to analyze existing configurations, assess 

design sensitivities, conduct technology trades, and to simulate missions within time-variant solar and 

atmospheric operational environments. The model framework would provide coupled and recursive 

interactions between the analysis components to create the complexity needed to complete such studies. 

Furthermore, the model development would follow a spiraling pathway such that discrete, functioning 

versions would be completed, and then built upon, to create new versions with ever-increasing capability. 

Thorough consideration of these requirements culminated in a concept summary document which 

prioritized modeling objectives, outlined the analysis framework, and mapped a general path toward 

completion. Within this context, a new analysis model was created and named SolFlyte, derived from the 

brief description, "Solar-electric Flight, including the effects of Energy balance, Time, and the 

Environment”. Two separate analysis models were eventually completed, “SolFlyte-HTA” and “SolFlyteLTA,” for the analysis of heavier-than-air (HTA) and lighter-than-air (LTA) concepts, respectively. More 

details about the applicability, theory, and unique features of the SolFlyte analysis models are described in 

Section 3, including methods for calculating recoverable and energy-optimal flight trajectories and the 

effects of vehicle shadows on solar array energy collection. 

This study compares solar powered airplane and airship station-keeping capability during a variety of 

high altitude missions, using SolFlyte as the primary analysis and simulation component. Three UAV 

concepts were designed for this study: an airplane (OEW = 3285 kg, span = 127 m, array area = 450 m2

), a 

small airship (OEW = 3790 kg, length = 115 m, array area = 570 m2

), and a large airship (OEW = 6250 kg, 

length = 135 m, array area = 1080 m2

). All the vehicles were sized for payload weight and power 

requirements of 454 kg and 5 kW, respectively. Seven mission sites distributed throughout the United States 

were selected to provide a basis for assessing the vehicle energy budgets and site-persistent operational 

availability. Seasonal, 30-day duration missions were simulated at each of the sites during March, June, 

September, and December; one-year duration missions were simulated at three of the sites. Atmospheric 

conditions during the simulated missions were correlated to National Climatic Data Center (NCDC) 

historical data measurements at each mission site, at four flight levels. The atmospheric data were corrected 

for measurement gaps based on adjustments to average measurements obtained during the specific 

simulation month and year at the mission site. Details of the HTA and LTA vehicle specifications, mission 

definitions, and assumptions utilized in this investigation are described in Section 4. An overview of the 

parametric design capabilities enabled by the SolFlyte solar energy collection analysis capabilities is also 

provided in that section. 

2.0 Symbols

Sw – Wing Area 
 

3 

Vw – Wind Speed 

Abbreviations

ADAC – Airship Design and Analysis Code 

Alt% – Mission Altitude Metric 

ASAB – Aeronautics Systems Analysis Branch 

AvEnrg% – Mission Energy Availability Metric 

deg – Degrees 

E – East 

ESS – Energy Storage System 

HALE – High Altitude Long Endurance 

H or HTA – Heavier-Than-Air 

L or LTA – Lighter-Than-Air 

Lat – Latitude 

Lon – Longitude 

N – North 

NCDC – National Climatic Data Center 

OEW – Operating Empty Weight 

PwrRad% – Mission Power Capability Metric 

S – South 

SE – Solar Electric 

TOGW –Takeoff Gross Weight 

UAV – Unmanned Aerial Vehicle 

WindData% – Mission Wind Data Suitability Metric 

W – West 

3.0 SolFlyte Modeling Scope and Theory

Developing a Complex Model 

A meaningful first step towards a qualitative interpretation of the SolFlyte modeling approach is to 

describe differences between what might be termed causal and complex analysis models. Both models 

typically contain numerous interconnected components, of related mathematical and physical elements, that 

influence other components of the particular model. A causal model is also procedural, comprised of 

determinate components and determinate connectivity, with influences propagated sequentially and 

predictably from one component to the next. The analysis progresses in order from the first component to 

the last component unless feedback pathways were implemented in accordance with precisely known 

conditional operators. Results obtained from causal analysis models often can be improved by improving 

the fidelity of individual components and by enhancing the amount of information transferred between 
 

4 

components. In contrast, a complex model is ambiguous, comprised of various determinate, inexact, or 

unknown components. The connectivity is strongly coupled and influences are propagated throughout the 

model either sequentially or through similarly ambiguous feedback mechanisms, producing a reordered 

analysis sequence with indistinct start and end points. Existing connectivity and influence ambiguities block 

attempts to improve analysis model results by improving the fidelity of individual components. 

Within this context, a causal model might be considered as the eventual limit of a complex model in 

which all components and connections have been clarified, but that transition is not always possible. For 

example, at the outset of SolFlyte development, it was evident that the vehicle geometry and performance 

combined with variable solar energy levels and uncertain weather conditions to generate complexity both 

within and between model components. As research and development progressed, other ambiguities 

appeared when considering: 

x If, or how should, flight patterns be determined, connected, and influenced? 

x What rules should be permitted to influence operations and therefore, results? 

x Which analysis components are most important, and which can be omitted? 

While attentive to complex modeling challenges, SolFlyte development necessarily expanded to 

address related concerns, such as clarifying vehicle and mission requirements definitions, identifying 

meaningful results metrics, and creating techniques for visualizing new types of results. The finalized 

SolFlyte concepts, capabilities, and assessement strategies now provide comprehensive guidance for future 

SE-HALE UAV vehicle and mission concept studies. A schematic diagram of the primary components and 

interactions of the SolFlyte analysis models is shown in Figure 1. 

Figure 1. SE-HALE UAV model components and interactions. 

SolFlyte Implementation and Practical Solutions 

The SolFlyte tool is an integrated suite of functional utilities intended for concept-level analyses of 

solar-electric air vehicle and mission concepts, utilizing PHX ModelCenter® v9.0 as the integration framework. 

Within this particular framework, separate files, executable codes, and scripts are “wrapped” for use as 

analysis “components” and then linked to create an overall analysis “process model”. There are two 
 

5 

separately applicable, but similar, process models for evaluating either airplane (SolFlyte-HTA) or airship 

(SolFlyte-LTA) concepts. These models also provide the functionality to simulate missions and assess the 

mission performance of the respective HTA and LTA concepts. Computational and procedural schemes 

were implemented to ensure that both analysis models would execute rapidly while retaining the ability to 

integrate the continually varying vehicle performance, energy levels, solar flux, and atmospheric conditions 

during each simulated mission time step. Addressing these considerations was enabled, in part, by creating 

three external modules: the “Wind Data Processing Model,” the “Shadowing Assessment Utility,” and the 

“Lighter-Than-Air Sizing Utility.”

The Wind Data Processing Model is a separate process model designed for execution once at the outset 

of a study to automatically generate all formatted wind data files needed for subsequent mission analyses. 

The stand-alone Shadowing Assessment Utility is executed only once per vehicle geometry in order to 

generate a table of shadowing effects, caused by vehicle shadows cast upon the solar arrays, at roughly 

36,000 Sun-vehicle orientations. The Shadowing Assessment Utility also provides a secondary capability 

to quickly calculate the shadowing effects at a single user-specified Sun-vehicle orientation. Finally, the 

Lighter-Than-Air Sizing Utility dually functions as a stand-alone airship design tool used prior to mission 

analyses, and then as an integrated, wrapped analysis component of the SolFlyte-LTA process model. The 

first application provides the capability to determine the vehicle size and system specifications, whereas 

the second application ensures that the specifications are correctly associated with the results of a particular 

mission simulation. 

The analysis model execution time for the “Spokane, March, 30-Day, HTA-450” simulation presented 

in this report was 5 minutes, 11 seconds (64-bit operating system, with a 2.50 GHz processor, and 16.0 GB 

RAM). For this case, vehicle power requirements, shadowing, solar energy collection, and atmospheric 

conditions were determined at four flight levels, for 36 outbound headings, for a range of speeds 

incremented from stall to maximum, during each 15-minute simulated time step. The Wind Data Processing 

model can parse and format atmospheric data measurements from approximately 100 NCDC sites in about 

30 minutes; the Shadowing Assessment Utility can generate a fully populated shadow table overnight, in 

about 9 hours; and the Lighter-Than-Air Sizing Utility can parametrically size an airship in about 1 second. 

The SolFlyte analysis models are configured to instantly access and extract information from the formatted 

wind data files and shadow tables during each simulation time step. 

3.1 Features and Utilities 

Principal features and concepts comprising the SolFlyte mission simulation and vehicle performance 

models are summarized in Sections 3.1.1 to 3.1.9. The descriptions of these features are intended to provide 

an overview of the flexibility, depth, and applicability of the SolFlyte models. Note that the functionality 

of the analysis capabilities described in these sections do not directly relate to individual components of the 

PHX ModelCenter® v9.0 process model framework. 

3.1.1 Mission Site and Time Inputs 

SolFlyte provides flexible input options to define and modify mission concepts. The mission start date, 

time of day, time step increment, and one or multiple mission sites should be selected to initiate each 

SolFlyte simulation. Site geography details stored in a wrapped Excel component are automatically 

accessed and updated by SolFlyte as the mission site counter is updated. Similarly, mission time progresses 

incrementally as the mission day and time-step counters are updated. A wide range of parameters are time 

dependent; to provide consistency during every time step of the simulated mission, all relevant 

astronomical, atmospheric, geographic, and vehicle status parameters are automatically updated or 

recalculated, as necessary. In addition, the numerical values of certain performance metrics are saved at 
 

6 

every time step for summation over the course of each simulated day and mission. Output is automatically 

saved as a separately named Excel file for each mission site. 

3.1.2 Winds and Atmosphere Model 

Prior to use in the SolFlyte analysis model, mission site details and mission atmospheric data during 

the mission time must be provided and saved as an Excel file. A separate “wind data” file is required for 

each mission site and the rows of data in the file must be specifically formatted and listed sequentially in 

time, by descending atmospheric pressure. Historical data files of validated atmospheric measurements can 

be downloaded from the National Climatic Data Center (NCDC) website and automatically processed by 

the Wind Data Processing utilities for assimilation with SolFlyte. More information about the NCDC wind 

data measurements and a list of approximately 1500 global sites can be found at the web address: 

http://www1.ncdc.noaa.gov/pub/data/igra/ (accessed December, 2014) 

The required and properly formatted atmospheric data input can originate from other sources, such as 

statistical models, or the user can choose to specify desired conditions. As described on the NCDC website, 

each row of processed mission wind data is ordered by columns as: wind year, month, day, hour, wind 

speed (m/s), wind direction (degrees clockwise, N = 0o

), altitude (m), pressure (mb x 100), temperature 

(Kelvin), and density (kg/m3

 x 104). Note the mission start year and the wind data year are required mission 

simulation inputs, but they are independent parameters so numerical equality is not mandatory. Whether 

the data are supplied by the user or supplied automatically, atmospheric conditions associated with pressure 

altitudes of 100 mb, 70 mb, 50 mb, and 30 mb must be input for each year, month, and day. For a given 

day, the atmospheric data are listed sequentially, according to the data acquisition hour. 

The Wind Data Processing model also extracts subsets of formatted NCDC site wind data to improve 

the analysis model execution time by narrowing data searches to a limited number of years. To further 

reduce analysis model execution time, SolFlyte creates smaller temporary files from the formatted subsets, 

viable only at the given mission site during the simulated mission time period. As a result, high-fidelity 

simulated atmospheric data is rapidly accessible for use in the SolFlyte analysis model during each mission 

time step at up to four different flight levels. The Wind Data Processing model is an external component of 

the SolFlyte model, requiring less than one hour to format about 100 NCDC data sites; a single execution 

creates the formatted wind data files required for all subsequent mission analyses. 

Scrutiny of the NCDC historical atmospheric data can reveal data acquisition gaps or the existence of 

randomly occurring spurious measurement values. As a result, suitable historical data are not always 

available for all days and time steps of a desired mission time period. For this study, mission sites and wind 

data years were selected judiciously to minimize the possibility that data gaps could influence the results. 

For a few sites, however, a limited number of data gaps were identified during the desired mission wind 

data year and month. The data deficiencies were satisfactorily eliminated utilizing approximated median 

atmospheric values derived from measurements acquired at the site during other days of the mission year 

and month. For example, if the mission site atmospheric data were suitable for all but one day of a 30-day 

mission, the data gap was eliminated using approximated median conditions derived from measurements 

acquired during the other 29 days. 

When historical wind data inputs are selected to initiate a mission simulation, the SolFlyte analysis 

model automatically identifies data deficiencies and inserts approximated median conditions unique to the 

mission site, month, and operational altitude. The analyst can select to refine or further substantiate the 

nominal tabulated values of approximated median conditions by analyzing measurements obtained during 

that same month, in different years. 
 

7 

3.1.3 On-Site Operational Radius Limits and Positioning Strategies 

Station-keeping is a mission operational requirement that constrains the vehicle to execute persistent 

and recoverable flight patterns within a prescribed radial distance from a mission site. If the vehicle position 

at a given mission time is determined as within that radial distance, it is deemed as operating on-site; 

otherwise, it is deemed as operating off-site. The power-radius performance capability metric, PwrRad%, 

is the fraction of total mission time that the vehicle concept was determined as operating on-site, and it is 

calculated by summing values of the on-site status parameter during each time step of the simulated mission. 

The PwrRad% metric depends mostly on the vehicle maximum speed and the capability to withstand 

occasional strong winds; thus, it reflects the recoverable aspect of station-keeping. 

To assess whether the vehicle is, in fact, operating on-site at a given mission time, concentric radial 

limits were defined as shown in Figure 2. In order of increasing distance from the origin, the operating 

regions bounded by these limits are shaded light green, dark green, and gray within an external red 

boundary. The mission site is depicted as a black disc at the origin of these circles. The inner-most light 

green region indicates satisfactory on-site positioning, whether or not the vehicle is operating upwind or 

downwind relative to the mission site. The darker green region extends beyond the inner region to indicate 

on-site positional energy reserves when operating upwind, and on-site positional energy deficits when 

operating downwind. The gray region indicates a vehicle radial position status that is temporarily 

unsatisfactory, but which could improve if the strong winds subside and if the vehicle can recover to an onsite region. The red boundary is the mission “cut-off” radius and identifies that the vehicle has been blown 

sufficiently far off-site that the mission would be aborted. During any time step, credit is awarded to the 

on-site status parameter only if the vehicle operating position is within either green region. 

Figure 2. On-Site radius limits and definitions. 
 

8 

Although forecasted atmospheric conditions are assumed to be unknown for all missions of this study, 

it would be expected that forecast information could be utilized at any point during the mission to revise 

standard operational rules. One such example would be positioning the HTA vehicle upwind and on-site 

during light wind conditions to store potential (i.e., positional) energy, prior to the onset of strong winds. 

The strategy would also provide a means to adjust the timing, direction, and duration of impending off-site 

operations. Upwind energy positions are categorized as (+) and downwind energy positions are categorized 

as (–); both are inspected by the SolFlyte analysis model to determine appropriate standard operational 

strategies during the next time step. 

Wind speeds are separated into categories that are defined differently for HTA and LTA vehicle 

concepts. For HTA concepts, light winds are defined as less than the vehicle stall speed, moderate winds 

are defined as greater than stall speed but less than maximum vehicle speed, and strong winds are defined 

as greater than the maximum vehicle speed. For LTA concepts, there are four wind speed category limits; 

the slowest speed limit is zero, followed by light and then moderate speed limits which are applied to input 

values greater than zero and less than the maximum vehicle speed, and the final speed limit is the maximum 

vehicle speed. Light and moderate LTA wind speeds are delineated by a user-supplied input variable that 

is typically set to a value less than about 5 m/s. 

The AvEnrg% energy availability metric is the fraction of mission time that the vehicle concept 

maintains access to some combination of direct or stored energy availability. Thus, AvEnrg% reflects the 

persistent aspect of station-keeping, and is calculated by summing values of the energy status parameter 

during each time step of the simulated mission. Experience gained from various research efforts has proven 

the effectiveness of separately considering the PwrRad% and AvEnrg% metrics for all design, analysis, and 

simulation purposes. 

For HTA vehicles operating during the lightest winds (< several m/s), energy can be stored more 

efficiently by re-positioning the vehicle than by climbing or re-charging the Energy Storage System (ESS). 

Re-positioning upwind at night will not adversely affect energy storage because the vehicle stall speed is 

substantially greater than the light wind speed, by definition. Re-positioning during the daytime also will 

not adversely affect energy collection because light winds enable almost the full range of Sun-tracking

flight patterns. Because LTA vehicles can operate at zero flight speed, however; the ability to position 

upwind at no additional cost to the energy system is not possible. Thus, maintaining on-site operations at a 

flight speed equal to the wind speed is usually the most efficient option for LTA vehicles. 

During moderate to strong winds, re-positioning HTA vehicles upwind is an inefficient method to store 

energy. It is strongly limited by the vehicle performance and requires significant energy expenditure. 

Similar to upwind re-positioning, the energy necessary for recovery-to-site during periods of moderate to 

high winds is extremely high. Although prompt recovery-to-site might be desirable, the most efficient 

recovery strategies exploit off-site climbing and ESS re-charging as first steps toward recovery. Repositioning and recovery-to-site depend strongly on the relative speed of the vehicle and wind, whether the 

vehicle is on-site or off-site, and whether it is day or night. Energy expenditure is therefore guided by 

operational strategies (decisions) derived from a few rules linking the vehicle performance and energy 

characteristics to the on-site radius, time of day, and atmospheric conditions during a time step. All of these 

input parameters can be modified by the user to define the various operational strategies. 

3.1.4 Persistent and Recoverable Flight Patterns 

 The analysis model includes a mission-level utility to identify persistent and recoverable (i.e., stationkeeping) flight patterns. The model is based on a mathematical representation of the vehicle and wind speed 

vectors during each time step for points within the site radius limits. A set of discrete outbound vectors is 
 

9 

generated by combining the wind speed vector with the range of possible flight speeds and headings 

according to user-specified intervals. Subsequent calculations provide a means to identify the return vectors 

which allow persistent out-and-back flight patterns. These important constrained patterns are called 

recoverable flight patterns and they are illustrated in Figure 3. 

Figure 3. Recoverable flight patterns. 

The methodology considers the possibility that a vehicle could depart upwind relative to the ground, 

against the wind vector and return by: 1) turning and flying downwind relative to the ground, assisted by 

the wind vector, or 2) maintaining an upwind heading and flying against the wind vector while drifting 

downwind relative to the ground. Given a specific recoverable flight pattern, it is possible to calculate the 

outbound flight time, the return flight time, and the relative vehicle-Earth-Sun orientations during the 

outbound and return segments. Combining recoverable flight patterns with solar energy collection utilities 

enables the unique capability to trade flight path vehicle power requirements and vehicle energy collection 

capabilities for each recoverable pattern. Only recoverable flight headings are evaluated by the SolFlyte 

analysis model when determining the optimal vehicle-Sun orientations for collecting energy. 

3.1.5 Solar Energy Calculations 

Determining the available solar energy flux at the operating altitude involves modeling external 

physics, solar array efficiencies, vehicle surface geometry, internal electronic efficiencies, vehicle-to-Sun 

orientations, and shadowing. The solar energy flux, including variations due to orbital parameters, time of 

day, time of year, flight altitude, and mission site latitude and longitude is calculated in SolFlyte using an 

algorithm developed from information provided on the Naval Observatory Astronomical Publication 

website: 

http://aa.usno.navy.mil/faq/docs/Alt_Az.php (accessed December, 2014). 

The solar energy lost to atmospheric absorption is calculated at the operational altitude based on the 

simulated atmospheric conditions during the time step. The resulting attenuated solar energy flux is the 

maximum energy available for collection by a solar array oriented normal to the Sun. This section describes 
 

10 

the methods used to calculate the time-varying solar energy available for collection by solar arrays that are 

installed on a solar electric vehicle concept, at a generalized orientation to the Sun. 

Solar Flux at the Vehicle Position 

Calculating the relative orientations of the Sun to the vehicle solar arrays begins with calculating the 

Earth-to-Sun distance, and the solar altitude and azimuth at the given mission site and time. The values are 

used to calculate the solar energy flux and the solar incidence angle to each solar panel of the vehicle. 

Calculating these solar flux vector components in the Earth-based reference frame is relatively 

straightforward, but transforming the components to the vehicle flight reference frame and then to the 

installed arrays is substantially more complicated, particularly for general flight orientations. By assuming 

the vehicle flight orientation is level, the vehicle z-axis aligns with the Earth z-axis, thereby simplifying the 

coordinate transformation from the Earth to the vehicle. Calculating the solar flux vector components to the 

mounted solar arrays also requires a vector transformation from the vehicle reference frame to the array 

reference frame. This vector transformation is known from the design geometry and is implemented prior 

to the analysis by mathematically describing the array panel corner coordinates as an oriented array surface 

in the vehicle body axes. The parameters and an overview of several reference coordinate systems included 

in the solar flux vector calculations are illustrated in Figure 4. 

Figure 4. Solar orientation model. 

Although the coordinate systems of the Sun, Earth, UAV, and arrays can be defined to simplify 

subsequent calculations, the coordinate transformations must also reflect the conventional use and 

orientation of these axes. For instance, the wind blows from, the vehicle points towards, and the flight heads 

towards, a direction measured clockwise from North = 0o

. On the other hand, the solar azimuth is measured 

clockwise from East = 0o

, as viewed from the Earth to the Sun, and the solar altitude never exceeds a 

magnitude of 90o

. Within the reference frame of the trigonometric functions, angles are measured counterclockwise from the + x-axis. Therefore, it was important to ensure all such conventions were smoothly 

integrated within and among the coordinate systems to avoid introducing phase, sign, or transformational 

errors. The directions and conventions used for defining some of these important orientations are illustrated 

in Figure 5. 
 

11 

Figure 5. Angle definitions. 

A reasonable inference might be that the level flight assumption restricts vehicle orientations and 

therefore eliminates the ability to model the effects of pitch or roll on solar energy collection. However, 

level flight does not restrict the range of possible Sun-to-Earth orientations or Sun-to-vehicle orientations. 

By referencing the vehicle body so the nose faces east and the right wing faces south, the Sun-to-Earth 

orientations could be transformed to the Sun-to-vehicle axes by adding the bank angle to the tabulated solar 

altitude. Although the entire simulation would include the constant bank angle offset, the method would be 

sound and useful for evaluating short periods of the mission. The approach would provide insight for 

assessing designs utilizing highly banked flight orientations at sunrise in order to quickly re-charge the 

ESS. The SolFlyte solar energy collection tables will be described in Section 3.1.6. 

Solar Cell Transmission 

 The atmospheric attenuation factor decreases the available solar energy flux as a function of the mission 

altitude and the Sun-to-Earth incidence angle. The attenuated solar energy flux is then considered as the 

maximum available flux for that mission time step. Neglecting reflection at grazing incidence angles for an 

uncoated array surface, the solar energy illuminating a solar cell varies as the solar flux vector component 

normal to the solar cell. When functioning as a vehicle system however, protective and anti-reflective 

surface coatings modify the amount of solar energy transmitted onto the solar cell depending on the indices 

of refraction of the air, coatings, and solar cell surfaces. The variation of solar energy transmission with 

incidence angle, as used for this study, is shown as a red line in Figure 6. 
 

12 

Figure 6. Solar cell array surface transmission model. 

Solar Cell Efficiency and Energy Collection 

A fraction of the solar energy transmitted to the solar array surface will be converted to electrical energy 

depending on the array temperature, cell array packing efficiency, and vehicle subsystem efficiencies. In 

addition, the nominal solar cell efficiency decreases with time due to cell degradation. Each of these 

parameters is considered when calculating the net energy availability during a particular mission time step. 

3.1.6 SolFlyte Shadowing Assessment Utility 

Background 

In addition to the physics governing the solar energy available for illuminating an arbitrary solar array, 

the amount of solar energy collected and converted to electricity also depends on the vehicle geometry, 

solar array placement, shadows cast upon the solar arrays, and the Sun-to-vehicle orientation. To address 

these factors, the SolFlyte shadowing utility was developed as a mathematically rigorous tool for 

determining the effects of vehicle shadows on solar array energy collection for any Sun-to-vehicle 

orientation. The utility consists of two versions; a “single-point” orientation version used for initial 

analyses, and a “multi-point” orientation version used to generate tabulated output accessible during the 

SolFlyte mission simulation process. The multi-point version is executed once per vehicle configuration 

(i.e., the external geometry), for all Sun-to-vehicle orientations prior to use in the SolFlyte analysis model. 

Vector and Geometry Modeling 

The array geometry of each analysis vehicle configuration is approximated by a set of oriented panels 

with four co-planar corners in the vehicle body axis coordinate system. The (x,y,z) coordinates of these 

corners and the fraction of solar array coverage on each panel must be specified. For each panel, SolFlyte 

determines the mathematical vectors describing all edge orientations, spatial positions, and array areas. 

When executing the utility, opposite sides of each panel are modeled and output as two separate 1-sided 

panels, identified as facing toward or away from the Sun. The identification list provides feedback to ensure 

that the directionally oriented panels were properly defined in the vehicle coordinate system. Because the 
 

13 

energy transmission parameters vary with incidence angle, it is necessary to combine these factors with the 

calculations of the vehicle sunlit array area. Thus, the sunlit array area is best defined as an “effective energy 

collection area” which combines energy transmission and shadowing effects. Once the factors relating the 

Sun to the solar cell outer surface have been determined, the time-varying solar cell efficiency can be 

applied to calculate the electrical energy generated during the mission time step. 

Figure 7 illustrates that shadows cast from separate panels upon an array target produce an overlapping 

shadow pattern (depicted as blue, red, and mixed areas). Each shadow pattern depends on the relative 

arrangement of every shadow-casting cast panel with respect to every other cast panel and the shadow 

target panel. Standard operations from vector calculus are used to determine the intersection points of sun 

rays cast through the four corner points of each cast panel to infinite planes containing each target panel. 

Each target panel region in the shade plane is considered as a series of strip vectors of differential width, 

segmented into bins, which, when integrated, yields the target panel area. The intersection points define 

oriented shadows associated with each cast panel within every infinite target plane facing toward the Sun, 

whether or not the shadow intersection points are within the array target region of the infinite target plane. 

All shadows in the solar array target region, are also parameterized as a series of strip vectors of differential 

width, segmented into bins. After intermediate calculations, the strip vectors can be summed to determine 

the shadowed area of each target array. 

The shadow-to-target cast distances are also determined, measured from each shadow-casting corner 

to each infinite shadow-target plane, in the direction of the sunlight ray vector. Shadow darkness variations 

from atmospheric scattering can be related to these distances and used to weight the bins of each shadow 

strip vector. The shadowed area on each solar array, as weighted by the cumulative shadow strengths from 

the strip vectors of all cast panels, can be determined by summing all target panel shadow strips and bins. 

The shadowing effects of all panel combinations are integrated to obtain the vehicle solar array shadowing 

fraction at the specific vehicle-to-Sun orientation. 

Figure 7. Shadowing geometry and features. 

The Shadowing Assessment Utility can generate a table of normalized energy collection areas for the 

range of relative Sun-to-vehicle orientations between altitude angles of -10o

 to 90o and azimuth angles of 

0o

 to 360o in 1o increments (36,461 unique orientations). Because execution time is approximately one 

second per orientation, sun tables are generated overnight in about 9 hours, only once per vehicle geometry. 
 

14 

During mission simulations, the SolFlyte analysis model extracts values from the sun tables almost 

instantaneously. As a result, SolFlyte mission simulations freely explore and identify energy rich flight 

headings and speeds. Figure 8 lists solar energy collection factors and illustrates example outputs of the 

single orientation version and multiple orientations version of the SolFlyte Shadowing Utility. 

A final point of clarification is necessary to define the “Effective Normal Solar Array Collection Area”. 

The term “Effective” indicates that the solar energy collection area contour values are normalized by the 

fraction of maximum viewable solar array area, not the total installed solar array area. For example, a 

double-sided array of area 10 m2

 per side would provide a maximum viewable area of 10 m2, as viewed 

normal to the surface, neglecting all other energy collection efficiencies. The total installed solar array area 

would be 20 m2

. The maximum viewable solar array area of the HTA-450 utilized in this study is 260 m2, 

as viewed from an orientation of 68o altitude, and at 90o and 270o azimuth, including all other energy 

collection efficiencies. The total installed solar array area is 450 m2

. 

Figure 8. Features of the solar energy collection factor. 

3.1.7 Climbing, Descending, and Altitude Variations 

Climb and descent physics differ for HTA and LTA vehicle types. To account for these differences, 

SolFlyte incorporates two distinct models for simulating the altitude variations characteristic of HTA and 

LTA vehicles. During every time step, the analysis model integrates the specific climb and descent physics 

with all applicable influences already described in Sections 3.1.1 to 3.1.6. 

Lighter-Than-Air 

For LTA vehicles, the buoyancy force and neutral buoyancy level (density altitude) are calculated based 

on the atmospheric conditions, vehicle weight, volume, and lifting gas. At the start of a mission simulation, 

this information is used to determine the ascent rate and the ascent time to an initial operational density 

altitude. The operational density altitude is then mapped to two contiguous pressure altitudes of 100 mb, 

70 mb, 50 mb, or 30 mb. The geometric altitude associated with the lower of the two selected pressures 

defines the upper altitude limit whereas the geometric altitude associated with the higher of the two selected 

pressures defines the lower altitude limit. To allow some flexibility to the LTA mission, it is assumed that 

the vehicle design enables operation within an altitude band defined by these two altitude limits. To 

decrease simulation times, the two remaining pressure altitude levels are eliminated from further 

consideration. 
 

15 

Heavier-Than-Air 

HTA vehicles climb as a function of performance capabilities and atmospheric conditions, in 

accordance with operational rules. In the SolFlyte model, the vehicle operational rate-of-climb ceiling and 

the absolute ceiling are determined at the maximum power setting, based on an input rate of climb parameter 

and on a power-limited rate of climb equal to 0 m/s, respectively. Both ceiling parameters vary due to the 

atmospheric conditions at each time step, so the design ceiling, operational rate-of-climb ceiling, and 

absolute ceiling generally refer to different, varying altitudes, as shown in Figure 9.

Figure 9. HTA altitude variations model. 

Easily attainable potential energy can be stored by climbing during daytime periods when the usable 

solar power exceeds the vehicle power and energy replenishment requirements. The power setting and 

attainable climb rate are determined by considering operational parameters, vehicle performance, and the 

atmospheric conditions. If the operations are defined to enable daytime climbing for the purpose of storing 

potential energy, the climb power setting is utilized until the vehicle reaches the absolute ceiling, at which 

point the power setting is reset to a cruise power setting. 

As power settings vary, the vehicle will continue to climb and descend near the absolute ceiling as solar 

flux levels permit. Sinking to recover potential energy is allowed only during simulated periods when the 

usable solar power is less than all vehicle power requirements, provided the vehicle is operating above the 

altitude floor. The attainable sink rate per time step is a function of the vehicle drag-to-lift ratio and the 

difference between the available and required power. 

Storing and using altitude potential energy can significantly offset the demands on the energy storage 

and power systems, but the allocation of this energy depends on operational choices. To avoid influencing 

the simulation results as a consequence of pre-selected choices, the potential energy parameter is considered 

hypothetical and thus remains independent of all energy storage system and power system calculations. 

However, values of the hypothetical altitude and potential energy are listed as output parameters during 

each time step for consideration when interpreting results. 

3.1.8 SolFlyte-LTA Analysis Model and Sizing Utility 

ADAC was the product of a short-term effort within the ASAB during 2002 to provide some capability 

to design and analyze LTA UAV concepts, and to predict station-keeping capabilities during operations in 

time-variant winds (ref. 2). The ADAC architecture did not permit mission or analysis flexibility, and the 

user interface prevented straightforward assimilation with the envisioned modeling capabilities of 2010. 

However, elements of the sizing methodology, input and output selections, design considerations, and 
 

16 

mission simulation procedures were fundamentally sound and relevant to the new development efforts. The 

contrast of severe limitations and promising attributes within the ADAC analysis tool provided practical 

feedback to substantiate the approach needed to achieve the desired LTA design methodology and modeling 

capabilities. 

The development of the SolFlyte-LTA model was an evolutionary process initiated with modifications 

to the functioning, completed SolFlyte-HTA model. Input parameter definitions within the SolFlyte-HTA 

“vehicle specifications” analysis component were revised, as needed, to accommodate differences 

associated with LTA vehicle concepts. Once the revisions were implemented, it was noted that many of the 

LTA specification parameters matched or shared common applicability to design parameters pertaining to 

airship design and analysis. To avoid redundancy, and to maintain consistency with the architecture of the 

SolFlyte-HTA process model, the common LTA vehicle specifications and the airship design capability 

were combined in one file and wrapped to create one analysis model component. Ultimately, the SolFlyteLTA “vehicle specifications” component evolved to function as both the stand-alone LTA Sizing Utility 

and the means to insert LTA vehicle specifications to the analysis model. This characteristic adds sizing 

and parametric design capability to the SolFlyte-LTA model that is not currently available in the SolFlyteHTA model. 

The completed, enhanced lighter-than-air sizing utility developed for use in the SolFlyte-LTA model 

provides robust, general, rapid sizing estimates and iterative parametric design capability. Sizing input 

parameters and values are entered on the LTA Sizing Utility input spreadsheet. The required analysis 

parameter values are generated and saved separately and prior to executing SolFlyte-LTA. After the sizing 

is complete, the Excel file, input, and the output parameter values serve as the LTA specifications 

component in the SolFlyte-LTA analysis model. The SolFlyte-LTA model then accesses and links any 

sizing updates for use in subsequent simulations. The PHX ModelCenter® v9.0 interface allows users to overwrite the LTA sizing outputs in order to execute the SolFlyte-LTA simulation model with vehicle 

specifications derived from other sources. 

Extensive code modifications were required to capture the operational and performance differences of 

the LTA and HTA vehicles. Defining two consistent sets of operational rules for the LTA and HTA concepts 

was complicated by the unexpected implications of only a few factors. These factors include: possible LTA 

flight speeds = 0 m/s, required HTA flight speeds > stall speed, and determining the appropriate LTA 

operational speed during certain nighttime conditions. 

3.2 Simulation Overview 

The analyst controls the expression of the integrated SolFlyte modeling functionality by modifying 

various operational, mission, and vehicle input options. This section illustrates how certain inputs might 

influence the overall course of an analysis or the ability to complete the simulated mission objectives. A 

detailed description of all available analysis and specification input parameters is outside the scope of this 

study; however, information is available in the SolFlyte User’s Guide. 

3.2.1 Mission Operations 

For SE-HALE UAV concepts, mission success depends on not only whether or not a vehicle has the 

capability to meet the mission objectives, but on how the capabilities of that vehicle are expended to meet 

the mission objectives. For instance, the simple choice of a late afternoon start time risks energy depletion 

and failure before the first sunrise of the mission; however, climbing to the operational altitude after an 

early morning launch would likely provide time to replenish the energy storage system before the first 

sunset. Similar strategies can involve exploiting such resources as vehicle-site positioning, altitude changes, 

flight headings in strong winds, and Sun-vehicle orientations in order to meet the mission objectives. Often, 
 

17 

capturing the gains afforded by a particular strategy is constrained by the physics of the vehicle 

performance, the instantaneous energy balance, or the environmental and solar conditions. Although the 

general approach to developing the SolFlyte operations model was to minimize "human derived" rules and 

to focus on identifying and applying physics-based operations constraints, both types of operational rules 

were implemented, and both influence simulation results. 

All flight vehicle design concepts must include on-board systems that adequately meet the energy 

storage and power requirements of the vehicle and payload for the duration of the mission. In the particular 

realm of SE-HALE UAV analysis, the cyclical nature of energy collection, storage, and expenditure must 

be considered during the design of the vehicle, energy storage, and power systems. Furthermore, the 

instantaneous energy state of the SE-HALE UAV depends on temporal and environmental factors which 

vary during the mission, including: the flight latitude, time of year, time of day, wind speed, solar cell 

efficiency characteristics, and the Sun-to-vehicle orientation. These factors fundamentally alter the design 

and analysis of the vehicle, energy subsystems, and mission operations. Successfully integrating these 

factors to SolFlyte required identifying and creating unique analysis methodologies, metrics, and modeling 

parameters. 

3.2.2 Vehicle Design and Analysis Applications 

Energy Balancing 

Figure 10 schematically illustrates how the ESS stored energy might vary during a brief 5-day mission. 

When assessing energy variations (the energy balance), the analyst must consider how to proceed when it 

has been determined that the vehicle concept has depleted all on-board energy reserves, shown in Figure 

10 as beginning at time t=t1. If the goal had been to determine the mission success or failure, the answer, 

of course, would be known. However, it there had been a need to ascertain the extent or duration of the 

failure, the analyst might wish to continue the analysis by implementing a strategy to account for energy 

reserve debts. Using this approach, the reserve energy peak to trough difference could be calculated and 

used to determine how much energy storage capacity would have been required to prevent the energy 

depletion. Additionally, in this case, the analyst would be challenged to describe the running energy status 

as either “available” or “depleted” during re-charging periods beginning at the time of maximum debt, t=t2, 

and ending at the time of zero energy debt, t=t3. The ambiguities suggested the need to clarify the analysis 

procedure by defining an energy floor input parameter. 

SolFlyte enables users to set initial and minimum allowable on-board energy storage levels while also 

setting how the simulation proceeds if lower stored energy levels are calculated. For typical analysis 

applications, the energy floor value is set to +0 to enforce a realistic minimum stored energy level of 0 kWh 

(energy debt = zero). Changing the energy floor value to -1 removes the minimum energy limitation and 

permits an assessment of all energy storage levels less than 0 kWh. Selecting this option reveals peak-totrough energy storage differences and facilitates substantial design insight regarding the energy storage 

system capacity. Additionally, the option permits the analysis and simulation to proceed on a hypothetical 

course, given the assumption that energy storage capacity will be increased, by an amount equal to the 

maximum accumulated energy debt, during subsequent design iterations. During each time step, the energy 

status parameter is set to a value of +0 or +1 to indicate energy depletion or availability, respectively. The 

AvEnrg% mission performance metric is the fraction of mission time steps when energy had been available 

divided by the total number of mission time steps. 
 

18 

Figure 10. ESS stored energy variations. 

LTA Vehicle Concepts: Special Considerations 

For high speed wind conditions, airship design power and energy storage requirements can lead to size 

and weight growth factors that limit the practical feasibility of LTA concepts. The growth factors can be 

compounded during nighttime operations when available energy has been reduced by the round-trip 

efficiency of the energy storage system. A flexible set of input parameters was developed for sizing the 

power and energy storage systems. The power system is sized to ensure that the vehicle performance is 

capable of counteracting the highest anticipated site wind speeds, thereby enabling recoverable flight 

patterns. The maximum vehicle speed, and therefore the power system capability, is dependent on the 

mission site winds. The energy storage system is sized to ensure adequate energy availability for nighttime 

operations at the energy usage speed for a duration equal to the energy usage time, or for operations at the 

maximum speed for some other, likely shorter, duration. Similarly, the energy collection time is the 

specified period of time available for re-charging the ESS. The energy usage speed is generally set to a 

value less than the maximum speed to avoid vehicle sizes that exceed reasonable feasibility limits; and the 

energy usage time is generally set to the duration of the longest night at a mission site. The ESS sizing does 

depend on the maximum wind and vehicle speeds, but only as a consequence of providing available energy 

for powering the separately determined performance needs. 

The modeling benefit of defining these three parameters is that the LTA power system design can be 

separated from the energy storage system design. Thus, the LTA vehicle concept power system is sized to 

satisfy the maximum speed requirement; whereas, the energy storage system capacity is sized as a function 

of the nighttime energy usage speed and energy usage time. By disentangling the power and energy system 

designs, the vehicle design concept is capable of speeds required for recoverable flight in the maximum 

wind conditions, whether or not energy would be available for powering all station-keeping operations. In 

other words, if the maximum wind speeds were encountered for an extended periods during a mission, the 

vehicle concept could possibly “run out of gas”, thereby causing the overall mission performance parameter 

to indicate mission failure; however, the vehicle would retain the design speed capability. As SolFlyte 

separately tracks the status of both the energy storage system and the power system at every mission time 

step, the specific nature of an overall mission failure can be identified. 

3.2.3 Output Description 

At the completion of an analysis, the SolFlyte results are saved as “Excel.xlsm” files, named 

automatically according to the vehicle concept type, mission site name, the mission simulation start month. 

A real-time stamp indicating the completed analysis time is appended to the name to establish a unique and 
 

19 

organized pattern of results. For example, the “Spokane, March, 30-Day, HTA-450” case presented in this 

report was named, “HTA-450_SPOKANE_Mar-09_8m18d10hr30min52s.xlsm.” Each file lists parameters 

such as the vehicle speed and heading, and the atmospheric and solar conditions at each time step. The 

status of mission operational status parameters related to the energy storage system, operational altitude, 

and radial distance to the mission site are listed for each time step and for the entire mission. 

The stored energy, altitude, and the vehicle-to-site distance are tracked during each simulated time step 

to calculate separate performance metrics during each simulated mission. The metrics are based on 

determining if the status of a given parameter would have enabled satisfactory operations during each time 

step. The summation of satisfactory operational times as a fraction of the mission duration provides insight 

to how individual parameters influence mission success or failure. For example, if direct or stored energy 

reserves were available during a given time step, the energy availability performance status would be 

credited +1. On the other hand, if the combined direct and stored energy reserves had been depleted during 

that time step, the energy availability status would not be credited (i.e., +0) and the overall mission 

performance would be set to failed. If energy had been available during four time steps and depleted during 

one time step, the energy availability summation as a fraction of all mission time steps would be 80%, thus 

demonstrating the AvEnrg% calculation. Although status parameter values can switch between +0 and +1, 

the overall mission performance flag cannot be reset once status parameter failure has been registered; 

however, the simulation will continue until the final time step is evaluated. 

The values of the vehicle-to-site distance, the vehicle altitude and the energy availability status 

parameters can be summed to obtain an overall mission performance metric. Table 1 utilizes the AvEnrg% 

metric as an example of how the various inputs and mission status values are calculated and considered 

when assessing the overall mission performance. To illustrate, the “overall mission performance” and the 

“mission energy availability” both depend on the energy availability level at each time step, but they also 

depend on the “energy floor” parameter. If the energy floor input parameter had been set to 0, and if the 

energy storage level were less than or equal to 0.0 kWh during the time step, the mission status and overall 

mission performance would be set to failed and the energy availability status would be set to +0. If the 

energy floor input parameter had been set to +0 and the energy storage level were greater than 0.0 kWh 

during the time step, then the overall mission performance would remain satisfactory and the energy 

availability status would be set to +1. However, if the energy floor input parameter value was input as -1, 

the energy availability status would be +0, and the overall mission performance would remain satisfactory. 

 Table 1. Energy Floor and Energy Availability Metric, AvEnrg% 
 

20 

Wind data measurement status indicators are listed during every mission time step to indicate the nature 

of the mission wind inputs during the specific mission simulation month and year. This factor provides 

essential feedback to the analyst for assessing the wind data quality, measurement gaps, and if the wind 

data input had caused operational anomalies. Other outputs listed during each time step include the time of 

day, solar orientation, atmospheric conditions, vehicle speed, heading, flight level, and the north/south and 

east/west radial distance components from the vehicle to the site. 

4.0 Study Approach

SolFlyte was utilized to compare and contrast the LTA and HTA station-keeping capabilities during 

various missions. Assuming benign wind conditions, the LTA concepts have the advantage of buoyant lift 

and can perform station-keeping operations with little energy. A second advantage is that the shape and 

relatively large surface area of the LTA concepts offers plentiful space for mounting solar arrays at optimal 

orientations. However, as winds increase, drag increases sharply as the LTA vehicle speed counteracts the 

wind speed in order to achieve persistent station-keeping. Thus, the LTA flight power required and energy 

collection needs increase substantially. 

The LTA drag increase is attributable to increased skin friction drag, profile drag, and a third factor 

peculiar to airships and balloons. The forward flight of an airship “pushes away” a mass of surrounding air, 

equal to the product of the atmospheric density and airship volume. The air momentum change is 

categorized as an “air mass” drag force that is dependent on the vehicle speed, fineness ratio, and volume. 

The air mass drag is a first-order consideration at moderate speeds and a dominant consideration at high 

speeds. Airship growth factors dependent on the air mass drag preclude designing SE-HALE airships 

capable of functioning at the maximum power during all conditions. Thus, realizing the operational design 

advantages of station-keeping airships, while also limiting the vehicle size, requires the ability to 

continuously adjust power settings as a function of the wind speed. These influences were combined with 

the previously described mission strategies and the vehicle performance and energy requirements. 

In contrast, during any wind conditions, HTA vehicle concepts must always operate at forward 

velocities greater than the stall speed in order to maintain flight. Therefore, station-keeping capabilities are 

inherent to HTA vehicle concepts for wind speeds below the stall speed threshold. In such conditions, the 

energy (power) required to persistently operate on-site is equivalent to the energy (power) to maintain level 

flight. However, collecting the solar energy necessary to generate the required power is restricted by the 

total surface area and the limited orientations available for mounting solar arrays. Because of these 

fundamental differences between the LTA and HTA concepts, it is difficult to perform a direct comparison 

of station-keeping performance. However, by utilizing the SolFlyte-HTA and SolFlyte-LTA analysis tools, 

these disparate concepts could be sized and then flown in identical simulated environments, with reasonably 

consistent operational methodologies to provide equitable comparisons. 

4.1 Mission Requirements 

On-site operations were defined as operating within a radius of 36 km from a centrally located 

surveillance site, and for the HTA vehicle only, an upwind re-positioning limit, extending an additional 24 

km, was defined and utilized as well. As discussed in Section 3.1.3, upwind re-positioning is not 

advantageous for LTA vehicles. The mission failure “cutoff radius” was set to 108 km for both the HTA 

and LTA vehicles. Weather forecasting could provide information to facilitate making critical choices 

during extreme conditions, but the SolFlyte operational rules assume that forecasts are not available. 
 

21 

All mission simulations examined 36 outbound flight headings ranging from 0o

 to 360o in 10o

increments. Speed increments of 2.0 m/s were used to generate a set of outbound speeds ranging from the 

stall speed to the maximum speed for the HTA simulations and from 0.1 m/s to the maximum design speed 

for the LTA simulations. An array of outbound flight segment candidates was generated to include all 

possible combinations of these outbound flight headings and speeds. Each outbound flight segment was 

assessed to determine the existence of either one or two corresponding return flight segments enabling a 

recoverable flight pattern. Note that during the day, optimal flight patterns are the occasional consequence 

of increased power expenditures to enable faster flight that ultimately leads to greater net energy increases. 

The flight time, Sun-vehicle-array orientations, and shadowing effects were also determined for the 

outbound and return segments of each recoverable flight pattern during each time simulated mission time 

step. The energy difference of the solar energy collected and the net operating energy consumed while 

completing both the outbound and return segments was also calculated. All net energy differences for all 

flight patterns were then evaluated to identify the most energy optimal flight pattern at the simulated time. 

The set of operational assumptions shown in Table 2 provide a reference to ensure consistency when 

comparing the HTA and LTA concepts. The design altitude parameter was set to provide flexibility to the 

LTA mission for operating within a narrow altitude band defined by contiguous pressure altitude levels, or 

by density variations at the operating pressure altitude level. This assumption proved to be somewhat 

generous and will be discussed in the results section. 

Table 2. Comparable Mission Operational Concept Assumptions 

4.2 UAV Design Parameters and Assumptions 

The design specifications of one HTA and two LTA concept vehicles were input to SolFlyte for analysis 

and assessement of mission station-keeping capabilities. As for the mission operational considerations, the 

HTA and LTA vehicle design assumptions were specified to be as consistent as possible. Selected design 

assumptions and values of the vehicle performance, propulsion, and ESS subsystems are shown in Table 3. 

The most similar requirements for the HTA and LTA concepts included the payload specifications, 

subsystem efficiencies, and the operational radius limits. The payload definition was specified as either a 

distributed or single-point mass of 454 kg (1,000 lb) requiring 5 kW of continuous power. The electrolyzer 

and fuel cell efficiencies were assumed to be 85% and 80%, respectively, leading to an ESS round trip 

efficiency of 68%. The nominal solar cell efficiency was assumed to be 25%, and the propeller efficiency 

was assumed to be 82% at all conditions. 
 

22 

Table 3. Comparable HTA and LTA Design Concept Assumptions

The energy storage system was sized for a longest night of 14.2 hours to approximate mid-latitude 

winter solstice design conditions. For the HTA-450 concept, the energy storage capacity of 500 kWh 

enabled overnight operations at maximum power, requiring the regeneration system to fully replenish the 

ESS during the ensuing 9.8 hour day. For the LTA vehicle concepts, the reserve energy usage time was set 

to 14.2 hours to enable overnight operations at speeds of 16 m/s and 20 m/s for the LTA-570 and LTA1080 concepts, respectively. Energy storage capacities of 500 kWh and 1000 kWh were designed for use 

on the LTA-570 and LTA-1080 concepts, respectively. The regeneration systems were both sized using the 

LTA Sizing Utility to fully replenish the ESS during the ensuing 9.8 hour day. 

Other specifications include the HTA speed and altitude parameters, minimum or stall speeds, design 

speed, maximum speed, and design altitude. The values were determined as reasonable for this type of 

vehicle based upon previous studies, whereas the LTA speed parameters were derived from a parametric 

study detailed below in Section 4.4. The minimum speed of the LTA concepts was set to a small positive 

value of 0.1 m/s, reflecting the LTA ability to float in one place. 

4.3 Heavier-Than-Air (HTA-450) Airplane Concept 

Completing station-keeping objectives during an extended mission requires that the vehicle design 

concept includes a capability to continually generate the power required to meet payload, subsystem, and 

flight demands. Generating and supplying that power depends on maintaining available energy sources; for 

the HTA-450, these energy sources include direct solar, ESS reserves, or any combination of the two. The 

cyclical variations of solar energy on a daily and seasonal period and of ESS energy during depletion and 

replenishment periods require balancing the HTA-450 power usage and energy availability throughout the 

mission. 

HTA Vehicle Concept: ESS Sizing 

During the design and sizing phase of this study, the ESS capacity was sized to provide sufficient 

reserve energy for all mission operations at the design cruise speed, for the duration of the longest night at 

a latitude of approximately 35o

 N. On the other hand, the power generation and propulsion systems were 

designed to enable station-keeping capabilities in high altitude wind speeds up to about 40 m/s, regardless 

of energy availability. By specifying the design parameters in this way, the mission energy availability and 

on-site power capability could be assessed separately. For the relative comparisons desired for this study, 

the specified design capacity was marginal to ensure that energy reserves could be depleted in extreme 
 

23 

conditions, but would not be depleted in moderate conditions. The capacity was determined iteratively 

based on analyzing results of multiple simulations at various sites and times of year. The value remained 

constant thereafter. In actual practice, the energy storage system would be designed to ensure that the energy 

reserves would not be depleted during extreme conditions. 

HTA Vehicle Concept: Array Sizing and Energy Collection 

The SolFlyte shadowing assessment utility, discussed in Section 3.1.6, was utilized to determine the 

solar array area and solar array orientations required to replenish the ESS in a time equal to the shortest day 

at the mission site. Figure 11 shows a typical output plot of energy collection contours showing the 

percentage of effective solar array area as a function of Sun position. Orange and yellow regions of the 

contour plots indicate good energy collection orientations; whereas gray and black regions indicate poor 

energy collection orientations. The yellow areas on the vehicle top-view graphic in the figure indicate 

horizontally oriented solar arrays, and the green areas indicate vertically oriented solar arrays. Initially, the 

HTA-1b vehicle configuration was selected for this study after evaluating mission simulation results 

utilizing various HTA design iterations. The HTA variants were of similar size with modified vehicle and 

array geometry. 

The energy collection objective is to size and orient the solar arrays to maximize energy collection at 

every Sun orientation, but clarifying how to meet that goal has not been possible. With the aid of solar 

energy collection contours generated by the SolFlyte Shadowing Assessment Utility, it is now possible to 

assess how vehicle geometry modifications will impact solar energy collection. For example, although the 

ESS capacity of the HTA-1b configuration was properly sized, the energy cycle did not quite close because 

the solar arrays could not fully replenish the system before night. Analysis of the energy collection contours 

indicated that selectively adding vertical array area to the outboard pylons and aft section of the tail boom 

would eliminate the HTA-1b energy collection deficit. Subsequent analyses confirmed this prediction and 

established the HTA-450 configuration as viable for the intended missions. 

The HTA-450 energy collection contour plot is shown in Figure 12. The increased energy collection at 

low solar altitude angles is represented by the significant expansion of the red areas, highlighted by yellow 

circles near Sun directions of 90o

 and 270o. The second-lightest gray contours also expand at low solar 

altitudes compared those of Figure 11, and the two darkest gray areas decrease, as indicted by the sizes of 

the yellow arrows in each figure. As a reference to facilitate comparisons, white lines were added to both 

figures at Sun altitude angles of 5o

 and 20o. (Note that all concepts used in this study are designated by the 

concept type and the total solar array area. In this case, the HTA-450 is an airplane concept with a total 

solar array area of 450 m2

.) 
 

24 

Figure 11. HTA-1b solar energy collection contours. 

Figure 12. HTA-450 solar energy collection contours. 
 

25 

The previous example demonstrates that the energy collection contours provide meaningful assessment 

capabilities. However, the assessments described to this point include the inherent assumption that all 

energy collection orientations are equally weighted over the course of the mission. The implications of this 

assumption necessarily require that the operational time, and the mission criticality during that operational 

time (a weighted operational time), is equivalent for all points of the contoured solution space. To clarify, 

suppose that mission objectives would somehow not require operations if the Sun altitude were less than 

20o

. In such a case, the design modification leading to the HTA-450 concept might have been unnecessary. 

It is possible to precisely identify optimal energy collection solutions which manifest operationally as 

energy optimal flight patterns. First, during the course of a day, the Sun follows a curvilinear path across 

the energy collection contour plot, as dictated by astronomical and geographical factors. This is depicted 

by the yellow dots in Figure 13. Second, wind speed increases relative to the vehicle speed will create 

unrecoverable flight patterns and operationally inaccessible regions of the energy contour plot. 

Furthermore, the individual “Sun” symbols located on the “Solar path” represent a specific time and Sun 

altitude angle at the mission site. As the Sun altitude angle is invariant at that instant in time, all possible 

Sun-vehicle-array orientations are therefore restricted to a single horizontal line passing through a selected 

“Sun” symbol. The recoverable flight headings are also restricted to that same horizontal line because of 

the alignment of the Earth and vehicle z-axes. Figure 13 (Vw= 0 m/s) and Figure 14 (Vw= 20 m/s) illustrate 

the results of a simulated 1-day test mission on September 21 at Santa Teresa, NM (~32o

 N) carried out 

utilizing the HTA-450 configuration. User-specified wind speeds were input to the simulation model for 

use throughout the mission at all flight altitudes. The blue dots in the figures indicate relative orientations 

from the vehicle to the Sun, whereas the red dots in the figures indicate the Earth-relative directional 

headings of the vehicle; both are measured counter-clockwise from the nose of the HTA-450. 

HTA Vehicle Concept: Sun-Tracking Flight Orientations and Headings 

The figures clearly show that energy-optimal Sun-tracking flight orientations trend toward 90o

 and 

270o

, corresponding to the right and left sides of the HTA-450 vehicle concept. As viewed within the Earthbased reference frame, the outbound and return flight headings would create a circular arc if plotted on a 

horizontally positioned compass (this arc would be circular only at dates near an equinox). The Sun-tracking 

flight orientations are shown to separate as wind speeds increase because the range of unrecoverable flight 

patterns increases with increasing wind speed. This effect forces deviations from the preferred 90o

 and 270o

relative orientations. The recoverable outbound flight headings would resemble the green boundary arc of 

Figure 3. The return headings would create a similar arc, but it would be offset by approximately 180o

, 

depending on the relative magnitude of the vehicle and wind speeds. If the entire pattern were then “filledin”, an asymmetric hourglass shape would be created. 

HTA-450 Vehicle Concept Geometry and Specifications 

The detailed structural and aerodynamic design of the vehicle was based primarily on modifications to 

proprietary data obtained from previous studies to obtain the final HTA-450 design concept. Key design 

specifications for the final HTA-450 configuration are shown in Table 4. Figure 15 shows top and isometric 

views of the HTA-450 concept, with solar array coverage identified by the darker regions. The solar arrays 

added to the HTA-1b configuration are visible on the most outboard pods and as increased coverage on 

both sides of each tail boom. 
 

26 

 Figure 13. HTA-450 solar energy collection contours with Sun track and headings, wind=0 m/s. 

 Figure 14. HTA-450 solar energy collection contours with Sun track and headings, wind=20 m/s. 

To create a favorable span loading, the payload was assumed to consist of five components, each with 

a mass of 90.9 kg (200 lb) contained in five separate wing pods. Two outboard pods on each wing house 

both the payload and one of four regenerative fuel cell subsystems, but the centerline pod contains only the 

payload. The top view indicates that the wing pods are separated uniformly, by a distance of 22 m (72 ft). 

The vehicle OEW mass of 3285 kg plus the fuel cell reactant mass of 115 kg yields a TOGW mass of 3854 

kg (8500 lb). The wingspan is 127 m (417 ft) and the total solar array area is 450 m2

. From the most outboard 

pods to the wing tips, a 14.5o

 dihedral angle was included to eliminate shadows on the vertical solar arrays 

mounted on the pods and facing toward the wing tips. At Sun-vehicle angles above 14.5o

, a wider range of 

orientations becomes available for energy collection.
 

27 

Table 4. Key HTA-450 Design Parameters 

Figure 15. HTA-450 top and isometric views. 

4.4 Lighter-Than-Air (LTA-1080 and LTA-570) Airship Concepts 

The LTA Sizing Utility described in Section 3.1.8 was utilized to parametrically design two airship 

concepts. For all LTA cases presented in this study, it is assumed that the lifting gas is helium and the array 

mounting angle, defined later in this section, is 65o

. To characterize the parametric design space of this 

study, the key design and size parameters of vehicle length, solar array area, power required, envelope 

volume, energy storage system capacity, and weight were plotted as a function of maximum speed, energy 
 

28 

usage speed, energy usage time, and energy collection time. The general approach to the design was initially 

constrained by the following assumptions and considerations: 

x energy and power availability during winter solstice nighttime at mid-latitudes, 

x volume feasibility of less than about 105 m3, 

x length similarity to the HTA wingspan of about 100 m, 

x maximum design speed suitable for high wind speed conditions, 

x and solar array area feasibility, estimated at < 1500 m2

. 

A final consideration was that the general character of the LTA design concept would be reasonably 

comparable to the HTA design concept. 

The top left graph of Figure 16 shows the relation between vehicle length and maximum speed. Airship 

length and volume increase dramatically with increasing speed as a result of the need to buoyantly lift 

heavier, more powerful propulsion and energy storage systems. The star indicates values associated with 

specifying a maximum design speed of 36 m/s; for this case, the resulting design length would be 135m. 

The resulting airship size and mass reveals the expense of specifying high speed performance capabilities 

for LTA design concepts. 

The top middle graph of Figure 16 shows a similar relation between the vehicle volume and maximum 

speed. The maximum speed of 36 m/s corresponds to a design lifting gas volume of 72,500 m3

. Two 

comparisons provide insight to the dimensional scale of these parameters: the Goodyear Airship Eagle 

volume is 5740 m3

, the length is 59 m, and the width is 15m; and the Hindenburg volume was 212,000 m3, 

the length was 245 m, and the width was 41 m. The bottom left graph shows that the required solar array 

area is 1080 m2

 for a maximum design speed of 36 m/s. The surface area of the LTA-1080 envelope is 

10,900 m2

, so the required solar array area covers only 10% of the total vehicle area. The bottom middle 

and bottom right graphs show the relation of energy storage system capacity and power required to achieve 

the maximum vehicle speed. The top right graph shows that doubling the design maximum speed from 

18 m/s to 36 m/s will roughly double the total weight, from a little less than 8,000 lb (the HTA TOGW = 

8,500 lb) to almost 16,000 lb. The design TOGW for the LTA-1080 is 15,100 lb. 

Figure 16. LTA-1080 parametric size variation as a function of maximum speed. 
 

29 

Figure 17 is similar to Figure 16, but the parameter sensitivities are shown as a function of stored 

energy usage speed instead of the maximum speed. Faster nighttime speeds ensure better on-site capability; 

however, as seen by the steep trends for all of the key vehicle parameters, additional nighttime speed can 

overwhelm other design considerations. The star shows the design points from specifying a 20 m/s energy 

usage speed capability for a nighttime duration of 14.2 hours. The sensitivity to the nighttime duration is 

also shown, with values of 12, 13, 14.2 and 15 hours. For this study, the energy usage time of 14.2 hours is 

approximately equal to the duration of the longest winter night at the mid-latitudes considered in this study. 

Figure 18 shows the sensitivity of key vehicle concept parameters to the complementary energy usage time. 

The sensitivities were evaluated at energy usage speeds of: 16 m/s, 20 m/s, 24 m/s, and 28 m/s. 

Figure 17. LTA-1080 parametric size variation as a function of energy usage speed. 

Figure 18. LTA-1080 parametric size variation as a function of energy usage time. 
 

30 

The final series of parametric sizing relationships is shown in Figure 19. The vehicle parameters are 

given as a function of solar energy collection time. A 9.8 hour shortest day was selected as the design point 

for determining the minimum time available to collect the energy needed to fully replenish the ESS. 

Figure 19. LTA-1080 parametric size variation as a function of solar energy collection time. 

The internal volume afforded by LTA vehicle concepts provides structural options to design hard points 

for attaching concentrated mass. For this study, the payload, ESS, and propulsion subsystems were all 

considered to be concentrated masses attached to the bottom of the LTA vehicle concept. The payload was 

enclosed within a centerline pod, the ESS was enclosed within two pods located forward of the payload 

pod, and the propulsion subsystem, including propellers, was located aft of the payload pod. The relative 

sizes and general layout of the LTA-1080 vehicle, payload, ESS, and propulsion components are illustrated 

in the schematic bottom view shown in Figure 20. Two gray-colored propulsion pods and two gray-colored 

ESS pods frame one central green-colored payload pod. The payload pod smoothly tapers to a point at each 

end, has a length of 9.15 m, a width of 1.52 m, a height of 1.22 m, and an internal volume of 10.2 m3

. 

Figure 20. LTA-1080 bottom view schematic: propulsion and payload (green) pods. 

The shadowing utility in SolFlyte was also utilized during the LTA sizing process. One of the key 

design tasks was to find the optimal array placement on the vehicle. Although artist’s conceptions of SE 

LTA vehicles usually depict solar arrays mounted to the top of the vehicle, such placement is suitable for a 
 

31 

limited variety of missions. For this study however, the 30-day missions were simulated during four seasons 

and the mission site latitudes range from about 30o

 N to 50o N. Thus, optimizing the solar array placement 

for a given mission site and season involves maximizing the total solar energy collection, while considering 

the daily and annual variations of the Sun-to-solar array orientation. 

To identify the optimal solar array mounting angle, two strips of solar arrays were modeled at locations 

on the top of the vehicle, extending from the nose to the tail. For each analysis case, the strips were separated 

symmetrically by increments of 10o

, as measured in a plane normal to the length axis, as viewed nose to 

tail. From this perspective, the array mounting angle is latitudinal, measured clockwise from horizontal to 

a terminal side which does not extend beyond the top of the vehicle. Thus, an array attached to the side of 

the vehicle, with an outwardly facing normal would be mounted at an angle of 0o

, and an array attached to 

the top of the vehicle with an upwardly facing normal would be mounted at an angle of 90o. 

Figure 21 schematically depicts how such solar array strips would appear as mounted on the LTA-1080 

vehicle concept. Each array strip area is 540 m2

, for a total of 1080 m2, mounted symmetrically on opposite 

sides of the vehicle. For shadowing analyses and simulation purposes, each array strip was modeled as 

seven distinct nose-to-tail panels. The panels were individually oriented to match the exterior slope of the 

vehicle at locations dictated by vehicle geometry and the solar array mounting angle. Seven similar vehicle 

configurations, characterized by array mounting angles of 30o

, 50o, 60o, 65o, 70o, 80o, and 90o, were assessed 

to determine the best array mounting angle for energy collection. To provide insight to the design 

methodology, and to produce a single LTA design, Santa Teresa, NM (32o

 N) was utilized as a 

representative design site. 

Figure 21. LTA-1080 isometric, top, and front views. 

For most orientations, the LTA vehicle shape and the solar array placement shown in Figure 21 will 

allow simultaneous illumination of both array strips. The solar energy collection charts resulting from this 

arrangement are shown beginning with Figure 22 and ending with Figure 29. These figures were utilized 
 

32 

as a design tool for identifying and selecting optimal solar array mounting angles for the LTA vehicle 

concepts of this study. From left to right, at the top of each figure is the solar energy collection pattern for 

the side facing towards the Sun, for the side facing away from the Sun, and for both sides combined, each 

non-dimensionalized by the array area of one side. For the 90o

 case, the third contour is simply double the 

energy collection capability of each side. The large contour plot at the base of the figure is similar to the 

third plot, but the contours are non-dimensionalized by the array area of the entire vehicle. For the 90o

 case, 

the non-dimensional energy collection indicated by the fourth plot is equivalent to either side separately. 

The fourth figure is used as the basis for determining the energy collection efficiency within SolFlyte. 

As the mounting angle is decreased, the contour patterns of the two strips combine to create 

substantially different patterns, and it is clear that a particular mounting angle would provide optimal energy 

collection for a given mission. Figures 30, 31, and 32 indicate the relative magnitudes of these variations 

referenced to different parts of the day during the months of March, September, and December, 

respectively. The advantages of the various mounting angles is evident for different times of the day as a 

result of the solar altitude angle. The inset shows a comparison of the relative magnitude of these 

advantages. Early and late day cumulative energy collection is indicated as a blue line and the mid-day 

cumulative energy collection is indicated as a red line. Finally, both lines of the inset plots were summed 

separately for the months of March, September, and December and are illustrated in Figure 33. 

Figure 33 indicates the existence of an optimal mounting angle of approximately 50o

, but the selection 

of the mounting angle requires evaluation of the particular mission requirements. For example, if the vehicle 

were used to carry out missions during December at a northern latitude, specifying a low solar array mount 

angle would improve energy collection. However, if the vehicle design maximum speed enabled operations 

during only spring, summer, and fall wind conditions, collecting additional energy during December would 

be unnecessary because the vehicle would be blown off-site. A low mounting angle might reduce energy 

collection and compromise the ability to carry out missions during other parts of the year. SolFlyte provides 

the modeling capabilities to carry out these necessary design trades. 

 
 

33 

Figure 22. LTA solar energy collection contours: Santa Teresa, array mount angle = 90o

. 

Figure 23. LTA solar energy collection contours: Santa Teresa, array mount angle = 80o

. 
 

34 

Figure 24. LTA solar energy collection contours: Santa Teresa, array mount angle = 70o

. 

Figure 25. LTA solar energy collection contours: Santa Teresa, array mount angle = 65o

. 
 

35 

Figure 26. LTA solar energy collection contours: Santa Teresa, array mount angle = 60o

. 

Figure 27. LTA solar energy collection contours: Santa Teresa, array mount angle = 50o

. 
 

36 

Figure 28. LTA solar energy collection contours: Santa Teresa, array mount angle = 40o

. 

Figure 29. LTA solar energy collection contours: Santa Teresa, array mount angle = 30o

. 
 

37 

 Figure 30. LTA normalized solar energy collection: Santa Teresa, December 21. 

Figure 31. LTA normalized solar energy collection: Santa Teresa, March 21. 
 

38 

Figure 32. LTA normalized solar energy collection: Santa Teresa, June 21. 

Figure 33. LTA time-integrated normalized solar energy collection: Santa Teresa. 

Using the energy contours as a guide, both of the LTA concepts of this study utilized a moderate solar 

array mounting angle of 65o

 above the horizontal plane. At the completion of the LTA-1080 design, a 
 

39 

smaller LTA vehicle was designed. The LTA-1080 had been designed to provide mission performance 

capabilities similar to the HTA concept. To achieve the required performance, the size and weight of the 

LTA-1080 significantly exceeded the size and weight of the HTA-450. To enrich the overall comparison 

objectives of this study, the LTA-570 concept vehicle was designed to provide sizing similarities to the 

HTA concept; while the resulting mission performance capabilities were accepted as consequential. The 

modifications therefore diminished the LTA-570 performance and station-keeping capabilities relative to 

the LTA-1080.

To meet the LTA-570 size objective, the maximum design speed was reduced to 32 m/s, and the 

stored energy usage speed was reduced to 16 m/s. Both speeds are 4 m/s slower than the corresponding 

speeds of the LTA-1080; but, these relatively small speed differences led to the significant size and 

weight reductions evident in the LTA-570 design. The LTA-570 mass is 4318 kg, and the total solar array 

area is 570 m2

. The solar energy contours of the geometrically identical, but smaller-scale LTA-570, are 

identical to the HTA-1080 vehicle, within a constant value dependent on the total solar array area. Note 

that such scaling is possible only for conic geometry, as self-shadowing cannot be scaled for a generalized 

vehicle. Table 5 lists key parameters for the LTA-570, and the LTA-1080 designs. 

Table 5. Key LTA-570 and LTA-1080 Design Parameters 

Figure 34 is a scaled top-view showing the relative sizes of the vehicle concepts and the aircraft hangar 

facility at NASA Langley Research Center. The wingspan of the HTA concept and the lengths of both LTA 

concepts exceed the width and depth of the hangar; however, the diagonal length of the hangar floor is 

about 125 m, somewhat longer than the LTA-540 length and about equal to the HTA-450 wingspan. Figure 

35 provides a scaled side-view perspective of the LTA-1080, the same hangar facility, and a variety of other 

aircraft. This view indicates that neither airship concept would fit inside the hanger; as the height of the 

LTA-540 and the height of the LTA-1080 height exceed the height of the hangar roof by 7 m and 12 m, 

respectively. Because the height of the HTA-450 would not prohibit entry to the hangar, it is feasible that 

facilities of this size might accommodate appropriately configured HTA concepts. 
 

40 

Figure 34. Scaled top-views of three HALE UAV concepts compared to NASA Langley Hangar.

Figure 35. Scaled side-view of LTA-1080 outside NASA Langley Hangar. 

5.0 Mission Simulation Results and Discussion

This section describes the various station-keeping missions defined for this study, carried out using the 

HTA-450, LTA-1080, and LTA-570 vehicle concepts discussed in the previous section. The capability of 

each vehicle to complete the simulated mission objectives was determined based on a number of operational 

performance metrics, such as the energy availability and station-keeping persistence. The results are 

configured to facilitate comparisons of the specific capabilities and limitations of each vehicle concept. 

Results can be alternately interpreted to bound the operational suitability of a particular vehicle concept, or 

to identify which vehicle concept is most suitable for completing a particular mission. 
 

41 

5.1 Mission Descriptions 

Figure 36 is a map of the seven geographically distributed mission sites selected for this study. The 

sites were selected to provide variations in latitude and longitude from a group of candidate NCDC sites 

with plentiful long-term atmospheric measurements. At high altitudes, mission atmospheric conditions vary 

as a function of season, altitude, latitude, and longitude whereas mission solar flux varies primarily as a 

function of season and latitude. In the northern hemisphere, high altitude winds are strongest above the 

northeastern regions of Asia and North America. Seasonally, the strongest winds occur during the winter 

months, concurrent with the shortest length of day and lowest Sun angles, whereas the weakest winds occur 

during the summer months. Table 6 lists the site name, latitude, longitude and the approximate duration of 

the shortest day at the site latitude, and mission simulation details. 

Table 6. Site and Mission Details 

Figure 36. Mission sites map. 

High altitude missions with durations of 30 days were simulated at each site for all vehicle concepts. Due 

to variations of the atmospheric and solar environment throughout the year, four separate, seasonal 30-day 

missions were simulated at each site during the summer, fall, winter, and spring. Also, 1-year missions 
 

42 

starting on January 1, 2009 and ending on December 31, 2009 were simulated at Green Bay, Santa Teresa 

and Wallops Island for all vehicle concepts.

5.2 Mission Simulation Results and Discussion

The mission time-integrated WindData%, AvEnrg%, and PwrRad% metrics are listed for each 

simulated mission in Table 7, Table 8, and Table 9, respectively. For enhanced visualization, percentage 

values < 70% are shaded pink to indicate poor performance, values > 90% are shaded green to indicate 

good performance, and intermediate values are shaded yellow. Below each table, the WindData%, 

AvEnrg%, and PwrRad% metrics are also plotted per mission site as bar graphs in Figure 37, Figure 38, 

and Figure 39, respectively. These parameters are mission-time integrated outputs and are used as the 

primary metrics for assessing overall mission performance. 

The WindData% suitability metric is defined as the fraction of mission time steps utilizing NCDC 

historical data divided by the total number of mission time steps. The metric was developed to provide the 

feedback necessary to assess the extent to which either measured conditions or approximated median 

atmospheric conditions are incorporated to a given mission simulation. The WindData% suitability metric 

and the number of timesteps utilizing approximated median conditions are both listed in the SolFlyte output 

file. Relatively low WindData% values can indicate the need for in-depth review of the input mission wind 

data quality. The WindData% suitability metric also can vary for dissimilar vehicles operating at identical 

mission sites and times. This can occur because the vehicle performance dictates access to different 

operational altitudes, including those differences associated with the simulated mission wind data 

availability at those altitudes. In such cases, WindData% output values can indicate the need to review the 

interactions between mission operational constraints and vehicle performance capabilities. Table 7 indicates 

that quality historical wind data was accessible for most vehicle concepts and missions of this study. For 

the HTA-450 vehicle concept, WindData% values ranged from 94% to 100% for the 30-day missions and 

from 98% to 100% for the one-year missions. With the exception of four 30-day missions, WindData% 

values associated with all missions executed using the LTA-1080 vehicle concept range from 91% to 100%. 

The wind data quality for the 30-day Spokane mission in December was acceptable, but the data were 

sparse, thus causing the lowest WindData% value of 66%. The WindData% values are relatively low for 

six of the 30-day LTA-570 missions during December and March, but range from 92% to 100% for all 

other missions. The wind data quality for the 30-day Spokane mission in December was similar to the LTA1080 vehicle. Other moderately low WindData% values are caused by data gaps at the low operating 

altitudes of the LTA-570 that are not present at higher altitudes accessible to the LTA-1080 or HTA-450. 

The mission time-integrated AvEnrg% availability metric, discussed earlier, is the fraction of time steps 

that energy had been available to power all required mission operations, divided by the total number of 

mission time steps. Note that the power could be generated from either ESS reserves, direct solar-electric, 

or a combination of reserves and direct solar energy. For all time steps when the energy availability status 

parameter value (also described previously, in Table 1) was not greater than zero, the vehicle would have 

required additional energy availability, whether or not the vehicle had been on station. From a design 

standpoint, the vehicle design would require modifications to store or collect energy in order to generate 

power for the duration of the mission, provided other design factors were constant. 

 
 

43 

Table 7. Mission Time-Integrated NCDC Wind Data Suitability Metric, WindData%

Figure 37. NCDC wind data suitability metric per mission site. 
 

44 

Table 8. Mission Time-Integrated Energy Availability Metric, AvEnrg%

Figure 38. Energy availability metric per mission site. 
 

45 

Table 9. Mission Time-Integrated Power-Radius Capability Metric, PwrRad%

Figure 39. Power-radius capability metric per mission site. 

The PwrRad% capability metric indicates the percentage of the mission time the vehicle is within the 

specified radial distance of 36 km from the mission site and provides a measure of the capability of the 

vehicle to operate in the mission winds occurring during the simulated mission. Results indicate that the 
 

46 

HTA-450 AvEnrg% is only 30% during the December mission at Spokane; however, if the AvEnrg% 

metric had been equal to 100%, the HTA-450 would posess the capability for station-keeping during 100% 

of that mission. In contrast, the available solar energy collection and limited performance capabilities of 

the LTA-570 would provide a PwrRad% capability of 26% and an AvEnrg% availability of only 38% 

during a mission in December at Spokane. As a result, the combined effects would enable station-keeping 

during this mission for a maximum of 26% of the time. As the two metrics do not necessarily overlap as a 

function of mission time, the operational station-keeping would likely be less than 26%. This examplifies 

how station-keeping qualities can be separately considered, and points to the need to properly size the ESS 

capacity and solar arrays as the first step toward station-keeping functionality. 

Examination reveals a few clear trends; all of the vehicles struggle to close the energy cycle and 

maintain energy availability during the month of December, regardless of location. As winter solstice 

occurs on December 21, these days provide the least amount of daylight and solar energy relative to any 

other time of the year at these locations. Adding energy storage capacity leads to a short term delay toward 

energy depletion, as the underlying issue is the ability to collect sufficient solar energy during December. 

On the contrary, all of the vehicles successfully completed the mission during the month of June, which 

encompasses the summer solstice and has the longest days of the year at these locations. March provides 

the most interesting results as the environmental conditions are challenging, but not overwhelming. 

Although the astronomical conditions are similar, spring wind speeds delineate March mission results from 

the almost summer-like September mission results. It is possible to note some latitudinal trends during 

March as overall performance of all the concepts improves from northern sites to southern sites. The LTA1080 demonstrates the best AvEnrg% performance, and is appropriately sized for mid- to low-latitude 

missions during the period March through September. The LTA-570 AvEnrg% availability drops slightly 

compared to the LTA-1080, and is more comparable to the HTA-450. In terms of on-station performance 

however (Figure 39), the HTA-450 consistently out-performs the LTA concepts because of the faster 

operational speeds, with the exception of the LTA-1080 mission at Jacksonville in December. This 

particular exception was investigated in more detail, as discussed below. 

Consider the PwrRad% capability associated with the HTA-450 and LTA-1080 missions during 

December at Jacksonville, shown in Table 9, as 74% and 100%, respectively. The result might seem 

counter-intuitive within the context of the operational capabilities of LTA and HTA concepts. As discussed 

previously, HTA vehicles are especially well-suited to climbing and descending in order to seek more 

advantageous atmospheric conditions, or to store potential energy during the daytime. Conversely, LTA 

vehicles are limited to a narrow band of operating altitudes just above and below the level of neutral 

buoyancy. However, operational rules can dictate when the simulation will access a particular altitude, 

assuming sufficient vehicle performance capabilities and energy reserves. 

During this mission, the wind speeds were quite strong at the 100 mb level, and were moderate at the 

70 mb and 50 mb levels. Although the HTA concept was capable of accessing all three flight levels, the 

maximum altitudes were limited to provide similarity to the LTA operational capability to access only the 

100 mb and 70 mb flight levels. As a result, the LTA concept operated at the 70 mb pressure altitude during 

all times of fast wind speeds at 100 mb. On the other hand, by enforcing an HTA altitude ceiling to provide 

fair comparisons, the HTA vehicle concept was not always able to access the higher altitudes for operations 

during fast wind speeds at a pressure altitude of 100 mb, although performance capabilities would have 

enabled such operations. The situation was such that the LTA concept could remain on-site for the entire 

mission. However, the HTA vehicle was capable of climbing to higher altitudes during the morning only if 

the net energy balance had been greater than 0 kWh. Thus, the altitude profile of the HTA-450 during 

periods of high winds followed the repeating pattern of: daytime climbing to 50 mb, early evening 

descending to 70 mb, nighttime continued descent to100 mb, and then early morning climbing to 70 mb. 

As a result of these two factors, the HTA vehicle remained on-site during some, but not all, of the time that 

the wind speeds were excessively fast at the 100 mb level. 
 

47 

The HTA-450 would exhibit 100% on-site capability at all mission sites and all times of year, except 

during December when the wind speeds increase. The LTA-1080 and LTA-570 struggle during the stronger 

winds that occur during winter and spring. In some cases, the LTA-570 is on-site for less than 30% of the 

total mission time. Finally, none of the concepts were capable of station-keeping for 100% of the time 

during the one-year duration mission simulations. The HTA-450 showed the best overall performance 

during the one-year missions, followed by the LTA-1080 and LTA-570. 

Mission Orientations for the HTA-450 and LTA-1080 Concepts

The relative orientations from the HTA-450 and the LTA-1080 vehicle concepts to the Sun are shown 

in Figure 40 for each of the 30-day seasonal missions simulated at Wallops Island, VA. Figure 41 shows 

the same information for the 30-day seasonal missions simulated at Santa Teresa, NM. Both figures include 

all outbound and return orientations for every mission time step when the solar altitude angle was calculated 

to be greater than approximately -10o

 (about 5o below the horizon at the mission altitudes). Seasonal 

variability is evident in the shapes of the energy-optimal orientations for both the HTA and LTA concepts 

at both mission sites shown here. Also, the shapes of the HTA and LTA relative orientations to the Sun are 

notably different. 

In all cases, the HTA orientations trend toward the preferred orientations of 90o

 and 270o. The faster 

wind speeds that were encountered during the winter (December) and spring (March) cause the vertical 

band of mission orientations to widen in accordance with the discussions regarding Figure 14. As would be 

expected, the vertical band stretches and contracts depending on the maximum solar altitude angle during 

the particular season. At low solar altitude angles, the width of the energy optimal orientations expands to 

avoid regions of localized energy collection minima. Also, because the absolute energy collection is low, 

the HTA-450 vehicle concept loses the option to expend energy overcoming even light winds, in order to 

access more energy efficient orientations. 

It is possible that some scatter might result from coarsely modeling the speed increment as 2 m/s and 

the heading increment as 10o

. It was hypothesized that a finer speed increment of perhaps 0.5 m/s and a 

finer heading increment of 3o

 might more precisely constrain the orientations. Several new tests cases were 

evaluated at the completion of this study to test the hypothesis. Initial analysis of the results indicated that 

using a vehicle speed increment of 0.5 m/s changed the optimal mission flight orientations, but not 

significantly. However, changing the heading increment to 3o

 resulted in noticeably more constrained 

patterns. This will be a consideration at the outset of future studies when the mission simulation parameters 

are defined. 

The LTA relative orientations are shaped by the vehicle and array geometry that leads to generally 

horizontal contours with less pronounced preferred orientations. Perhaps more importantly however, the 

SolFlyte-LTA model provides the capability to access solar orientations that are offset from the flight path. 

In other words, the LTA vehicle concept can rotate around the z-axis as it slowly drifts or floats above the 

mission site. Finally, during periods of high winds, the LTA concept is rapidly constrained to recoverable 

flight patterns that are directly aligned with the wind vector heading. This is most evident in the results of 

the 30-day December mission at Wallops Island, VA. 
 

48 

Figure 40. HTA-450 orientations to the Sun, 30-day Santa Teresa and Wallops Island missions. 
 

49 

Figure 41. LTA-1080 orientations to the Sun, 30-day Santa Teresa and Wallops Island missions. 
 

50 

Wind Speed Variations Throughout the Years

The atmospheric conditions of this study were based on high-altitude measurements obtained at 

selected NCDC stations featuring long-term observation programs and a particularly full complement 

of data from the calendar year 2009. If the station-keeping results of this study are to be deemed as 

applicable benchmarks for similar missions in other years, then the 2009 conditions must be proven as 

representative of the conditions in other years. As daily, seasonal, and annual unpredictability is a 

certainty of weather, using only 2009 atmospheric data suggests the need to enhance the analysis 

procedure when determining station-keeping capabilities during other years. 

Previous research within the ASAB indicates that wind speed can be modeled as a probablisitic 

Rayleigh distribution, but this neglects the importance of time dependence. Neglecting time removes 

the ability to determine how many hours light, average, or maximum winds will persist, or if the wind 

speeds and durations (at those speeds) are correlated. For an existing vehicle operating at the mission 

site, it might be possible to forecast how long winds will blow and at what speeds. However, when 

designing a new vehicle, this phenomenon must be modeled and somehow implemented during the 

concept development phase. Without such considerations, a stated design requirement for stationkeeping persistence during maximum wind speeds for maximum durations could risk burdening the 

design concept with constraints that might lead to over-design and excessive cost, or worse, a seemingly 

unachievable design objective. 

To generalize and extend the applicability of the PwrRad%, AvEnrg%, and any other similarly 

defined metric, missions should be simulated for multiple years at the same mission site, during the 

same mission period to obtain a range of values for each metric. The average value of each metric 

provides the final indication of station-keeping performance, including the effects of annual weather 

variations. 

6.0 Conclusions

A new and unique complex modeling capability has been developed that is suitable for the analysis of 

solar powered high altitude long endurance concepts, including both HTA and LTA configurations. The 

SolFlyte analysis tool includes the effects of winds aloft, solar position and flux, recoverable flight patterns, 

vehicle-to-Sun orientations, and vehicle self-shadowing, all as a function of time and mission location. This 

capability was utilized to investigate the design and performance trades required when comparing HTA and 

LTA concepts. Results indicate that: 

x The HTA concept attained longer periods of on-site persistence than either LTA concept, as 

indicated by the power-radius capability metric, PwrRad%. 

x The LTA concepts provided more reliable energy availability than the HTA concept, as 

indicated by the energy availability metric, AvEnrg%, but these energy benefits required 

prohibitive design trades of reduced performance (LTA-570) or excessive solar array area 

(LTA-1080). 

A summary of the design trade-off information is shown in Table 10. The LTA concepts are larger and 

heavier, and achieving the same performance requires substantially more solar array area (LTA-570 vs. 

LTA-1080). The LTA-1080 is of similar mass as the HTA-450 concept, yet the array area exceeds the array 

area of the HTA-450 concept. This would introduce a substantial cost component to the LTA vehicle 

concept selection. Although the wingspan of the HTA concept is roughly equivalent to the length of the 

LTA concepts, the volumes of the LTA concepts dictate the need for a larger hangar volume. New facility 
 

51 

construction costs or the availability of a larger hangar must be included when considering the selection of 

LTA concepts. As a result, it is likely that the LTA-1080 concept would be more costly to build and operate 

than the HTA concept. In contrast, during operational periods of light winds, LTA concepts can float in 

place with little power required to maintain position or altitude, thus reducing the burden of energy 

availability and storage. The advantages of utilizing an HTA or LTA vehicle concept are clearly indicated 

by the energy availability metric, AvEnrg%, and the power-radius capability metric, PwrRad%, results for 

the one-year duration missions summarized below.

Table 10. Vehicle and Mission Summary 

Selecting a preferred HTA or LTA vehicle concept depends on the mission objectives and constraints, 

particularly the desired levels of station-keeping persistence, the mission time of year, and the mission 

operational site. Large LTA concepts can host the required ESS capacity and solar array area with less 

impact to the design, and can provide the capability to float over targets for long periods of time; however, 

station-keeping becomes difficult when winds aloft increase. The HTA concept operates at faster speeds, 

greater than the stall speed of about 32 m/s, and inherently provides better station-keeping persistence, as 

indicated by higher values of the power-radius capability metric, PwrRad%. Both HTA and LTA vehicle 

concepts could prove useful as cost effective platforms for future HALE UAV missions. 

 
 

52 

References 

1. Nickol, C.; Guynn, M.; Kohout, L.; and Ozoroski, T.: High Altitude Long Endurance Analysis 

 of Alternatives and Technology Requirements Development. NASA/TP-2007-214861, March 2007. 

2. Ozoroski, T.; and Mas, K.: A PC-Based Design and Analysis System for Lighter-Than-Air 

 Unmanned Vehicles. AIAA-2003-6566, September 2003. 
REPORT DOCUMENTATION PAGE Form Approved

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Technical Memorandum

 4. TITLE AND SUBTITLE

High Altitude Long Endurance UAV Analysis Model Development and 

Application Study Comparing Solar Powered Airplane and Airship 

Station-Keeping Capabilities 

5a. CONTRACT NUMBER

 6. AUTHOR(S)

.

Ozoroski, Thomas A.; Nickol, Craig L.; Guynn, Mark D.

 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

NASA Langley Research Center

Hampton, VA 23681-2199

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14. ABSTRACT

There have been ongoing efforts in the Aeronautics Systems Analysis Branch at NASA Langley Research Center to develop a suite of integrated physics-based 

computational utilities suitable for modeling and analyzing extended-duration missions carried out using solar powered aircraft. From these efforts, SolFlyte 

has emerged as a state-of-the-art vehicle analysis and mission simulation tool capable of modeling both heavier-than-air (HTA) and lighter-than-air (LTA) 

vehicle concepts. This study compares solar powered airplane and airship station-keeping capability during a variety of high altitude missions, using SolFlyte 

as the primary analysis component. Three Unmanned Aerial Vehicle (UAV) concepts were designed for this study: an airplane (Operating Empty Weight 

(OEW) = 3285 kg, span = 127 m, array area = 450 m2), a small airship (OEW = 3790 kg, length = 115 m, array area = 570 m2), and a large airship (OEW = 

6250 kg, length = 135 m, array area = 1080 m2). All the vehicles were sized for payload weight and power requirements of 454 kg and 5 kW, respectively. 

Seven mission sites distributed throughout the United States were selected to provide a basis for assessing the vehicle energy budgets and site-persistent 

operational availability. Seasonal, 30-day duration missions were simulated at each of the sites during March, June, September, and December; one-year 

duration missions were simulated at three of the sites. Atmospheric conditions during the simulated missions were correlated to National Climatic Data Center 

(NCDC) historical data measurements at each mission site, at four flight levels. 

15. SUBJECT TERMS

Airship; Climatology; High altitude; Solar powered aircraft; System analysis

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