December 2014 

NASA/TM–2014-218671 

Artificial Intelligence Based Control Power 

Optimization on Tailless Aircraft 

ARMD Seedling Fund Phase I Final Report

Frank H. Gern and Dan D. Vicroy 

Langley Research Center, Hampton, Virginia 

Sameer B. Mulani, Rupanshi Chhabra, Rakesh K. Kapania, and Joseph A. Schetz 

Virginia Polytechnic Institute State and University, Blacksburg, Virginia 

Derrell Brown and Norman H. Princen 

Boeing Research and Technology, Huntington Beach, California 
NASA STI Program . . . in Profile

Since its founding, NASA has been dedicated to the 

advancement of aeronautics and space science. The 

NASA scientific and technical information (STI) 

program plays a key part in helping NASA maintain 

this important role. 

The NASA STI program operates under the auspices 

of the Agency Chief Information Officer. It collects, 

organizes, provides for archiving, and disseminates 

NASA’s STI. The NASA STI program provides access 

to the NTRS Registered and its public interface, the 

NASA Technical Reports Server, thus providing one 

of the largest collections of aeronautical and space 

science STI in the world. Results are published in both 

non-NASA channels and by NASA in the NASA STI 

Report Series, which includes the following report 

types: 

• TECHNICAL PUBLICATION. Reports of 

completed research or a major significant phase of 

research that present the results of NASA 

Programs and include extensive data or theoretical 

analysis. Includes compilations of significant 

scientific and technical data and information 

deemed to be of continuing reference value. 

NASA counter-part of peer-reviewed formal 

professional papers but has less stringent 

limitations on manuscript length and extent of 

graphic presentations. 

• TECHNICAL MEMORANDUM. 

Scientific and technical findings that are 

preliminary or of specialized interest, 

e.g., quick release reports, working 

papers, and bibliographies that contain minimal 

annotation. Does not contain extensive analysis. 

• CONTRACTOR REPORT. Scientific and 

technical findings by NASA-sponsored 

contractors and grantees. 

• CONFERENCE PUBLICATION. 

Collected papers from scientific and technical 

conferences, symposia, seminars, or other 

meetings sponsored or 

co-sponsored by NASA. 

• SPECIAL PUBLICATION. Scientific, 

technical, or historical information from NASA 

programs, projects, and missions, often 

concerned with subjects having substantial 

public interest. 

• TECHNICAL TRANSLATION. 

English-language translations of foreign 

scientific and technical material pertinent to 

NASA’s mission. 

Specialized services also include organizing 

and publishing research results, distributing 

specialized research announcements and feeds, 

providing information desk and personal search 

support, and enabling data exchange services. 

For more information about the NASA STI program, 

see the following: 

• Access the NASA STI program home page at 

http://www.sti.nasa.gov 

• E-mail your question to help@sti.nasa.gov 

• Phone the NASA STI Information Desk at 

757-864-9658 

• Write to: 

NASA STI Information Desk 

Mail Stop 148 

NASA Langley Research Center 

Hampton, VA 23681-2199 
National Aeronautics and 

Space Administration 

Langley Research Center 

Hampton, Virginia 23681-2199 

 

December 2014 

NASA/TM–2014-218671 

Artificial Intelligence Based Control Power 

Optimization on Tailless Aircraft 

ARMD Seedling Fund Phase I Final Report

Frank H. Gern and Dan D. Vicroy 

Langley Research Center, Hampton, Virginia 

Sameer B. Mulani, Rupanshi Chhabra, Rakesh K. Kapania, and Joseph A. Schetz 

Virginia Polytechnic Institute State and University, Blacksburg, Virginia 

Derrell Brown and Norman H. Princen 

Boeing Research and Technology, Huntington Beach, California 
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 NASA Aeronautics Research Mission Directorate 

(ARMD) for supporting this research through a Phase I ARMD Seedling Fund Award. 

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

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

Space Administration. 
3

Abstract 

Traditional methods of control allocation optimization have shown difficulties in exploiting the full 

potential of controlling large arrays of control devices on innovative air vehicles. Artificial neutral 

networks are inspired by biological nervous systems and neurocomputing has successfully been 

applied to a variety of complex optimization problems. This project investigates the potential of 

applying neurocomputing to the control allocation optimization problem of Hybrid Wing Body 

(HWB) aircraft concepts to minimize control power, hinge moments, and actuator forces, while 

keeping system weights within acceptable limits. 

The main objective of this project is to develop a proof-of-concept process suitable to 

demonstrate the potential of using neurocomputing for optimizing actuation power for aircraft 

featuring multiple independently actuated control surfaces. A Nastran® aeroservoelastic finite 

element model is used to generate a learning database of hinge moment and actuation power 

characteristics for an array of flight conditions and control surface deflections. An artificial neural 

network incorporating a genetic algorithm then uses this training data to perform control 

allocation optimization for the investigated aircraft configuration. The phase I project showed 

that optimization results for the sum of required hinge moments are improved by more than 12% 

over the best Nastran® solution by using the neural network optimization process. 

Nomenclature 

ANN = Artificial Neural Network 

DEP = Distributed Electric Propulsion 

FEM = Finite Element Model 

HWB = Hybrid Wing Body 

OREIO = Open Rotor Integration on a BWB Concept by Boeing 

S&C = Stability and Control 
4

Purpose 

This project investigates the potential of applying artificial intelligence methods like 

neurocomputing to the control allocation optimization problem of Hybrid Wing Body (HWB) 

aircraft concepts. Researchers from NASA Langley, Virginia Tech, and Boeing Research and 

Technology are exploring the use of artificial neural networks to develop innovative control 

algorithms minimizing control power, hinge moments, and actuator forces to keep system 

weights within acceptable limits. 

HWB platforms feature multiple control surfaces, with large control surface geometries leading 

to large hinge moments and high control power demands. Due to the large number of control 

surfaces on an HWB, there is no unique relationship between control inputs and resulting 

aircraft response, i.e. different combinations of control surface deflections may potentially result 

in the same maneuver, but with large differences in control power.1 While traditional methods of 

control allocation optimization may have limitations in exploiting the full potential of controlling 

large arrays of control devices, artificial neutral networks (ANN) are inspired by biological 

nervous systems and have successfully been applied to a variety of complex optimization 

problems (see e.g. Refs. 2-4). 

This project employs a finite element based aeroelastic HWB model, as well as wind tunnel and 

flight test data from the Boeing X-48 flight demonstrator5 to build a database that can be used to 

train an artificial neural network (ANN) to perform control allocation optimization for tailless 

aircraft at the conceptual design level (Fig. 1). 

Background 

HWB aircraft concepts offer the potential to achieve significant fuel burn savings of over 25%, 

with resulting emissions reductions, as well as community noise benefits, therefore countering 

the impact of dramatic increases in future air traffic volume.6,7 Recent wind tunnel testing 

indicates that control authority issues still exist (e.g. stall recovery and three-dimensional 

coupling effects8). Stability augmentation and control power optimization for these aircraft 

concepts can be enablers for their success. 

The use of artificial intelligence to overcome the shortcomings of conventional methods for 

control allocation has not been explored to a significant extent in the open literature. A literature 

search yielded very few results, most of them not applicable to this critical need for HWB 

configurations. Artificial intelligence offers new ways to optimize control power while minimizing 

hinge moments and structural loads. 

The proposed concept applies to the design and development stages of future air vehicles. If 

the research proves successful, artificial intelligence will be used to develop optimized control 

laws in a much more efficient manner than by using traditional methods; and these optimized 

Figure 1: Boeing’s X-48 Blended Wing Body demonstrator (NASA Photo).
5

control laws are an enabler for an entire fleet of revolutionary aircraft. Although the control laws 

will be developed using artificial intelligence methods, there is no need to implement these tools 

on the aircraft itself; therefore, no certification issues are anticipated for such a solution. 

Approach 

The project applies artificial intelligence to the HWB control allocation problem to optimize 

control surface schedules and minimize control power. An aeroelastic finite element model 

(FEM) provides the baseline for stability and control (S&C) analyses. Boeing Research and 

Technology provided control surface data, actuator characteristics, and aeroservoelastic 

modeling support to generate an accurate representation of the HWB baseline. The FEM model 

has been used to generate a database of hinge moment and actuation power characteristics for 

an array of control surface deflections. 

A sufficient amount of training data is a prerequisite for successfully training an ANN for control 

allocation and to determine the proper size and architecture of the ANN, both of which are 

crucial for the success of applying neurocomputing to the problem of control power optimization. 

Phase I of the effort focused on the development of a proof-of-concept process demonstrating 

the feasibility of using neurocomputing for actuation power optimization (Fig. 2). 

Figure 2: Using an artificial neural network for control allocation optimization.
6

In the interest of maximizing the results and impact of the Phase I effort, some modeling 

simplifications were applied. While these limitations did not affect the validity of the Phase I 

results, they will be removed in the future to improve the value and practical usefulness of the 

approach. As will be described in the “Results” section, the total hinge moment sum from all 

active control surfaces was used as a proxy figure-of-merit to minimize actuation power. Future 

efforts will include the complete actuator dynamics developed by Boeing during the Phase I and 

therefore use actual actuation power as a figure of merit for the optimization. The Boeing 

actuator dynamics model was validated through X-48 wind tunnel and flight testing. The 

aeroelastic finite element analysis used a symmetric half model for an initial 2.5g pitch 

maneuver analysis. 

Accomplishments 

The main objective of the Phase I effort was to develop a proof-of-concept process suitable to 

demonstrate the potential of using neurocomputing to optimize actuation power for aircraft 

featuring multiple independently actuated control surfaces (see Fig. 3). All the proposed work 

tasks and milestones were completed on time and within budget. All deliverables from the 

project partners were received by the Principal Investigator and directly contributed to the 

success of the project. The project team successfully laid all the necessary groundwork to 

continue the initial research in a follow-on project. 

The key accomplishments from the Phase I project are outlined below: 

 Established a complete proof-of-concept aeroservoelastic neurocomputing process to 

optimize actuation power for a representative 2.5g symmetric pitch maneuver. 

 Developed a full aeroelastic model suitable for analyzing a representative HWB platform 

developed by Boeing. The non-proprietary Boeing OREIO (Open Rotor Engine Integration 

Figure 3: Proof-of-concept process to optimize actuation power through aeroservoelastic 

neurocomputing.
7

and Optimization on an HWB9) concept was chosen for this task (see Figs. 4 and 5). Boeing 

provided control surface geometries and developed actuator dynamics models based on a 

Simulink analysis model that was validated through X-48 wind tunnel and flight testing. 

Details regarding the structural and aeroelastic modeling of the OREIO configuration can be 

found in Refs. 10 and 11. 

 Generated an aeroelastic trim database containing up to 2,500 Nastran® aeroelastic TRIM12

solutions. For this purpose, the baseline Nastran® model is used to perform a large number 

of maneuver-trim analyses with randomly generated linkage coefficients between the 

different control surfaces. Latin Hypercube Sampling is used to generate random Nastran®

AELINK12 card coefficients for each individual Nastran SOL144 static aeroelastic TRIM 

analysis.13 A Matlab® script then reads trim variables, control surface deflections, and stores 

all this information for post-processing and optimization using artificial neural networks. 

Figure 4: The Boeing OREIO features up to 25 control surfaces and high lift devices. 

(OREIO = Open Rotor Engine Integration and Optimization on an HWB9)

Figure 5: Finite element model of the Boeing OREIO used for the study.
8

 Investigated neural network topologies (number of layers/neurons) and learning algorithms to 

train the neural network using the aeroelastic trim database. A neural network with one 

hidden layer of up to 300 neurons was chosen as the network to perform the actuation power 

optimization. The hidden layer neurons were trained using a hyperbolic tangent sigmoid 

transfer function, while a single output neuron featuring a linear transfer function represents 

the required actuation power. 

 Optimized the neural network using a genetic algorithm to develop sets of control surface 

linkage coefficients minimizing the sum of all control surface hinge moments, which was the 

figure-of-merit used as a representation of total required actuation power. 

 Quantified the optimization results by demonstrating a more than 12% improvement over the 

best Nastran® solution by using the neural network optimization process. 

Results From the Seedling Phase I Effort: 

Figure 6 shows the probability density function of the training data generated by the FEM 

analysis for different numbers of test cases. During the network training process, the least 

squares error between the pre-computed results and ANN predictions is minimized through 

back-propagation by adjusting the weights and biases of the individual neurons. With each 

generation, the fitness value is reduced until it is flattening out after about 60 generations, 

indicating that the neural network is fully trained at this point. A genetic algorithm is used to 

minimize the absolute sum of the hinge moments. 

The AELINK control surface linkage coefficients in Nastran® define relationships between 

individual control surface deflections and an “independent” control surface, which in the present 

case is the center “elevator” surface in Fig. 7. For this study, the range of allowable control 

surface deflections has been limited to the [-1,1] interval, and the overall analysis data base for 

the 2.5g maneuver case included 2,500 samples. Future studies will further increase this 

interval to avoid extrapolation on the critical AELINK coefficients. 

Figure 6: Training data probability density function.
9

The neurocomputing process was applied to a symmetric half model of the Boeing OREIO, 

featuring eight actuated control surfaces, i.e. seven trailing edge flaps and the rudder (Fig. 7). 

To improve engine noise shielding, the OREIO vertical tails are highly canted,14 resulting in a 

significant amount of pitch authority for the rudder. To test the potential of the neurocomputing 

process, optimization of the neural network was performed using both, the AELINK control 

surface deflection coefficients and the direct values of the individual control surface deflection 

angles. The obtained control surface deflections minimizing the total hinge moment sum for both 

cases are highlighted in Fig. 7 and although quite similar, the deflections for both approaches 

are not identical, thus further highlighting the fact that different control surface deflection 

schedules may result in similar actuation power requirements. 

Table 1 shows that for both cases, the total sum of the hinge moments is less than the best 

Nastran solution, with more than 12% improvement when using the control surface deflection 

angles as the input parameter. As a validity check, both control surface deflection data sets 

Figure 7: Optimized control surface deflections (in degrees) for a Boeing OREIO 2.5g 

symmetric pitch maneuver.

Table 1: 2.5g Maneuver optimization results for the total sum of hinge moments (in lb-in) 

as a proxy for actuation power.
10

were used as fixed values for a Nastran® SOL 144 aeroelastic trim solution to check whether 

Nastran would reproduce the results when using the predictions from the optimizer. Table 1 

shows that the Nastran FEM analysis for the actuation power proxy matched the neural network 

prediction within numerical accuracy. 

Potential Impact on NASA or National Aeronautics Challenges 

Reducing actuation power is an enabler for ultra-efficient commercial transport aircraft and 

therefore directly impacts the National Aeronautics Challenges of simultaneously reducing fuel 

consumption, emissions, and noise of future air traffic. HWB aircraft and other innovative 

concepts suitable for this research offer the potential to achieve significant fuel burn savings of 

over 25%, with resulting emissions reductions, as well as community noise benefits, therefore 

countering the impact of dramatic increases in future air traffic volume. As a result, the proposed 

research directly applies to three of the six ARMD Strategic Thrust areas: 

 Innovation in Commercial Supersonic Aircraft 

 Ultra-Efficient Commercial Transports 

 Transition to Low-Carbon Propulsion 

The process can easily be adapted to other innovative and unconventional configurations. 

Potential candidates currently under development by other NASA projects are shown below: 

Low Boom Supersonic Vehicles: These vehicles have proven to be extremely difficult to trim for 

cruise conditions, making this an even greater challenge for maneuvers (Fig. 8). In addition, 

their very thin airfoils require detailed structural and aeroservoelastic models for realistic 

analyses, which is generally beyond the scope of traditional flight controls models. 

Distributed Electric Propulsion (DEP) Vehicles: The control laws required to ensure a robust 

transition control in pitch, roll, and yaw, while achieving high cruise aerodynamic efficiency are 

depending on a new approach for control allocation. Actuation power optimization is crucial on 

these vehicles to satisfy their stringent power management requirements and weight savings 

goals. In addition, multiple distributed concentrated masses and control surfaces, combined with 

high structural flexibility and significant configuration changes make these concepts ideal 

candidates for the proposed neurocomputing process. An example of a DEP vehicle is shown in 

Fig. 9. 

Figure 8: NASA Low Boom Supersonic Transport Concept.
11 

Changes to Current Concept of Operation for the Proposed Concept 

to Achieve Practical Application 

The proposed concept applies to the design and development stages of future air vehicles. If 

the research proves successful, artificial intelligence will be used to develop optimized control 

laws in a much more efficient manner than by using traditional methods; and these optimized 

control laws are an enabler for an entire fleet of revolutionary aircraft. The proposed approach 

reduces power requirements, hinge moments, structural loads, and therefore overall vehicle 

weight, therefore allowing us to exploit the full potential of innovative aircraft with multiple 

distributed control surfaces. 

The process is easily applicable to other innovative and unconventional configurations. Without 

the necessity to implement artificial intelligence on the aircraft itself, no certification issues are 

associated with such a solution. 

Conclusions 

During the course of this project the researchers developed a proof-of-concept process to

demonstrate the potential of using neurocomputing for optimizing actuation power for aircraft 

featuring multiple independently actuated control surfaces. A Nastran® aeroservoelastic finite 

element model was used to generate a learning database of hinge moments and actuation 

power characteristics for a symmetric 2.5G pull-up maneuver using various sets of control 

surface deflections. An artificial neural network incorporating a genetic algorithm then used this 

training data to perform control allocation optimization for the investigated aircraft configuration. 

The phase I project showed that optimization results for the sum of required hinge moments are 

improved by more than 12% over the best Nastran® solution by using the neural network 

optimization process. 

Figure 9: Greased Lightning (GL-10) Distributed Propulsion (DEP) Vehicle flight 

demonstrator.
12

References 

1. Cameron, D. and Princen, N., “Control Allocation Challenges and Requirements for the 

Blended Wing Body,” AIAA Paper 2000-4539, AIAA Guidance, Navigation, and Control 

Conference and Exhibit, August 14-17, 2000, Denver, CO. 

2. Haykin, S., Neural Networks - A Comprehensive Foundation, 2nd Edition, Prentice Hall, July 

1998.

3. Ansari, N., Hou, E., Computational Intelligence for Optimization, Kluwer Academic 

Publishing Group, Norwell, MA, 1997. 

4. Tagliarini, G.A., Christ, J.F., and Page, E.W., “Optimization Using Neural Networks,” IEEE 

Transactions on Computers, Vol. 40, No. 12, Dec. 1991, pp. 1347-1358.

5. Risch, T., Cosentino, G., Regan, C.D., Kisska, M., and Princen, N., “X-48B Flight-Test 

Progress Overview,” AIAA-2009-0934, 47th AIAA Aerospace Sciences Meeting and Exhibit, 

January 5-8, 2009, Orlando, Florida. 

6. Collier, F. S., Thomas, R, Nickol, C. A., Lee, Chi-Ming, Tong, M. “Environmentally 

Responsible Aviation - Real Solutions for Environmental Challenges Facing Aviation, 27th 

International Congress of the Aeronautical Sciences, Paper No. 802, Nice, France, 19-24 

September, 2010. 

7. Liebeck, R.H., “Design of the BWB Subsonic Transport”, AIAA-2002-0002, 40th AIAA 

Aerospace Sciences Meeting and Exhibit, January 14-17, 2002, Reno, NV. 

8. Gatlin, G.M., Vicroy, D.D., and Carter, M.B., “Experimental Investigation of the Low-Speed 

Aerodynamic Characteristics of a 5.8-Percent Scale Hybrid Wing Body Configuration,” AIAA 

Paper 2012-2669, 30th AIAA Applied Aerodynamics Conference, June 25-28, 2012, New 

Orleans, LA. 

9. Pitera, D.M., DeHaan, M., Brown, D., Kawai, R.T., Hollowell, S., Camacho, P., Bruns, D., 

and Rawden, B.K., Blended Wing Body Concept Development with Open Rotor Engine 

Integration, NASA/CR–2011-217303, The Boeing Company, Huntington Beach, California, 

November 2011. 

10. Gern, F.H., “Finite Element Based HWB Centerbody Structural Optimization and Weight 

Prediction,” AIAA-2012-1606, 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural 

Dynamics and Materials Conference, April 23-26, 2012, Honolulu, Hawaii. 

11. Gern, F.H., “Conceptual Design and Structural Analysis of an Open Rotor Hybrid Wing Body 

Aircraft,” AIAA Paper 2013-1688, 54th AIAA/ASME/ASCE/AHS/ASC Structures, Structural 

Dynamics and Materials Conference, April 8-11, 2013, Boston, Massachusetts. 

12. MSC Nastran 2008, Quick Reference Guide, MSC Software Corporation, 2 MacArthur 

Place, Santa Ana, CA 92707, USA, 2008. 

13. Rodden, W.P. and Johnson, E.H. (Editors), MSC.Nastran Version 68, Aeroelastic Analysis 

User’s Guide, MSC Software.

14. Thomas, R.H., Burley, C.L., Lopes, L.V., Bahr, C.J., Gern, F.H., and Van Zante, D.E., 

“System Noise Assessment and the Potential for a Low Noise Hybrid Wing Body Aircraft 

with Open Rotor Propulsion,” AIAA Paper 2014-0258, 52nd Aerospace Sciences Meeting, 

SciTech 2014, January 13-17, 2014, National Harbor, Maryland. 
REPORT DOCUMENTATION PAGE Form Approved

OMB No. 0704-0188

2. REPORT TYPE 

Technical Memorandum

 4. TITLE AND SUBTITLE

Artificial Intelligence Based Control Power Optimization on Tailless 

Aircraft ARMD Seedling Fund Phase I 

5a. CONTRACT NUMBER

 6. AUTHOR(S)

.

Gern, Frank H.; Vicroy, Dan D.; Mulani, Sameer B.; Chhabra, Rupanshi; 

Kapania, Rakesh K.; Schetz, Joseph A.; Brown, Derrell; Princen, Norman 

H.

 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

NASA Langley Research Center

Hampton, VA 23681-2199

 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)

National Aeronautics and Space Administration

Washington, DC 20546-0001

 8. PERFORMING ORGANIZATION

 REPORT NUMBER

L-20510

10. SPONSOR/MONITOR'S ACRONYM(S)

NASA

13. SUPPLEMENTARY NOTES

Final Report

12. DISTRIBUTION/AVAILABILITY STATEMENT

Unclassified - Unlimited

Subject Category 08

Availability: NASA STI Program (757) 864-9658

19a. NAME OF RESPONSIBLE PERSON

STI Help Desk (email: help@sti.nasa.gov)

14. ABSTRACT

Traditional methods of control allocation optimization have shown difficulties in exploiting the full potential of controlling large arrays of control devices on 

innovative air vehicles. Artificial neutral networks are inspired by biological nervous systems and neurocomputing has successfully been applied to a variety of 

complex optimization problems. This project investigates the potential of applying neurocomputing to the control allocation optimization problem of Hybrid 

Wing Body (HWB) aircraft concepts to minimize control power, hinge moments, and actuator forces, while keeping system weights within acceptable limits. 

The main objective of this project is to develop a proof-of-concept process suitable to demonstrate the potential of using neurocomputing for optimizing 

actuation power for aircraft featuring multiple independently actuated control surfaces. A Nastran® aeroservoelastic finite element model is used to generate a 

learning database of hinge moment and actuation power characteristics for an array of flight conditions and control surface deflections. An artificial neural 

network incorporating a genetic algorithm then uses this training data to perform control allocation optimization for the investigated aircraft configuration. The 

phase I project showed that optimization results for the sum of required hinge moments are improved by more than 12% over the best Nastran® solution by 

using the neural network optimization process. 

15. SUBJECT TERMS

Actuation power; Aeroelasticity; Artificial intelligence; Fight controls; Finite element; Neurocomputing; Structures

18. NUMBER

 OF 

 PAGES

15

19b. TELEPHONE NUMBER (Include area code)

(443) 757-5802

a. REPORT

U

c. THIS PAGE

U

b. ABSTRACT

U

17. LIMITATION OF 

 ABSTRACT

UU

Prescribed by ANSI Std. Z39.18

Standard Form 298 (Rev. 8-98)

3. DATES COVERED (From - To)

5b. GRANT NUMBER

5c. PROGRAM ELEMENT NUMBER

5d. PROJECT NUMBER

5e. TASK NUMBER

5f. WORK UNIT NUMBER

 694478.02.93.02.13.56.23 

11. SPONSOR/MONITOR'S REPORT

 NUMBER(S)

NASA-TM-2014-218671

16. SECURITY CLASSIFICATION OF:

The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, 

gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this 

collection of information, including suggestions for reducing this burden, to Department of Defense, Washington Headquarters Services, Directorate for Information Operations and 

Reports (0704-0188), 1215 Jefferson Davis Highway, Suite 1204, Arlington, VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person 

shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number.

PLEASE DO NOT RETURN YOUR FORM TO THE ABOVE ADDRESS.

1. REPORT DATE (DD-MM-YYYY)

01- 12 - 2014