51st AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition
07 - 10 January 2013, Grapevine (Dallas/Ft. Worth Region), Texas
AIAA 2013-1100
John R. Hooker * and Andrew Wick
†
Lockheed Martin Aeronautics Company, Marietta, GA, 30063
Cale Zeune
‡
Air Force Research Laboratory, Wright-Patterson AFB, OH 45433
Greg Jones § and William Milholen **
NASA Langley Research Center, Hampton, VA
Lockheed Martin (LM) has teamed with the Air Force Research Laboratory (AFRL) and NASA Langley
Research Center to design and validate the efficient transonic cruise performance of a “speed agile” enabled
STOL (short take-off and landing) military transport. Speed agility refers to the capability of efficient flight at both low speeds (~70 knots) and at transonic cruise speeds (Mach 0.80). One of the primary challenges associated with this type of aircraft is the efficient, system level integration of the enabling technologies which permit efficient flight at both low and high speeds. This challenge was addressed using computational fluid dynamics (CFD) to design an efficient transonic cruise transport which incorporates the LM and AFRL developed hybrid powered lift system (HPLS) for efficient STOL performance. This paper focuses on the aircraft transonic aerodynamic design using advanced CFD methods, performance validation with NASA
National Transonic Facility (NTF) wind tunnel testing of a semi-span powered model at flight Reynolds numbers, and validation of CFD based design tool predictions. Unfortunately test results from this effort were inconclusive as deficiencies in the initial NTF air routing system, which permitted powered testing, caused significant scatter in the test data. These deficiencies have been addressed by NASA with system enhancements incorporated post-test. Ongoing testing and evaluation of these enhancements may lead to a future re-test of the SACD powered model.
Energy efficiency, access, and flexibility have risen to be top concerns of the United States Air
Force’s mobility fleet. These concerns are being addressed with research into a new “speed agile” enabled military transport capable of efficient transonic cruise at speeds faster than the current mobility fleet (Mach 0.80) coupled with a STOL capability for operations from short, unimproved runways. The Speed Agile Concept Demonstrator (SACD) effort is an AFRL sponsored program aimed at raising the TRL of a “speed agile” enabled military transport to a
Level 5 or better through high value wind tunnel testing of the integrated system. The LM /
AFRL SACD aircraft (depicted in Figure 1) incorporates a unique hybrid powered lift system
(HPLS) that enables low speed high lift performance and has also been designed / optimized for efficient transonic cruise. This HPLS (described in References 1 - 3) utilizes an inboard
*
Aeronautical Engineer, Senior Staff, Lockheed Martin Aeronautics Company, Marietta, GA
†
Aeronautical Engineer, Senior, Lockheed Martin Aeronautics Company, Marietta, GA
‡
Aerospace Engineer, Air Vehicles Directorate, AFRL/RBAA, 2130 8 th
Street
§
Research Engineer, NASA Langley Research Center, Hampton, VA
**
Research Engineer, NASA Langley Research Center, Hampton, VA
Copyright © 2012 Lockheed Martin Corporation. Published by the American Institute of Aeronautics and
Astronautics, Inc. with permission.
Copyright © 2013 by Lockheed Martin Corporation. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission.

Reversible Ejector Nozzle (REN) and a Circulation Control (CC) system on the outer wing to enable both efficient STOL and transonic cruise performance. Demonstration of TRL 5 validation of the efficient transonic cruise performance of the integrated performance of this system was planned through 5.25% scale powered, semi-span testing in NASA’s NTF.
Cryogenic testing at -50 deg F permitted simulation of flight Reynolds numbers. Powered testing enabled the simulation of the embedded engine exhaust nozzle and the assessment of the transonic cruise performance characteristics of an outer wing incorporating a CC system.
Figure 1: LM / AFRL SACD Configuration
The approach used to validate the transonic cruise performance of the speed agile enabled multimission mobility transport relied extensively on CFD to design the aircraft and wind tunnel testing in NASA LaRC’s National Transonic Facility (NTF) to verify its performance.
CFD Flow Solver Description
The flow solver utilized for the aircraft design portion of the effort was USM3D. USM3D is an unstructured grid flow solver developed by NASA Langley Research Center (LaRC) (References
4 - 9). It utilizes tetrahedral-based, viscous, unstructured grids generated with VGRID to rapidly
assess even the most complex of configurations. VGRID generates viscous grids using advancing layers to resolve the viscous boundary layer and the advancing front method to resolve the remaining portion of the volume mesh. Anisotropic stretching in both the viscous layers and the volume mesh is a unique feature of the VGRID grid generator, which permits a high level of stretching of the mesh in directions not of critical importance. For example the mesh in the vicinity of the wing leading edge is highly stretched in the span-wise direction which permits adequate chord-wise resolution without incurring the penalty of a small spacing in the span-wise direction. USM3D solves the Navier-Stokes equations using a cell-centered finitevolume formulation with upwind flux differencing. It incorporates numerous turbulence models.

The USM3D CFD flow solver has been extensively validated using both wind tunnel and flight test data. In addition to recent extensive validations on the C-5 and F-22, validation has also been
conducted on the propeller driven C-130 and P-3 configurations (References 8 and 9).
CFD-based Design/Optimization Tools
Aerodynamic shape optimization was a critical enabler for this effort as it was utilized to design both the aircraft and the wind tunnel model. Critical requirements for the optimizer were for rapid analysis times and appropriate fidelity. LM has developed the KNOPTER (KNowledge based OPTimizER) optimization process which builds upon two mature design methods developed by NASA. These methods are the Constrained Direct Iterative Surface Curvature
(CDISC) inverse design methodology and the SUSIE stationless design method (Reference 10).
The CDISC method is a knowledge-base design approach that has been coupled with a variety of
2-D and 3-D flow solvers ranging from potential flow to Navier-Stokes and, for the latter category, has been used with structured, overset, and unstructured grids. The code uses specified flow/geometry relationships developed from analytical or empirical studies to compute geometry changes based on the difference between current and target flow quantities. This eliminates the need to compute sensitivity derivatives and allows the design to converge in parallel with the flow solution, thus greatly reducing the time required for a design. The design time is further reduced by the use of flow constraints to automatically develop the target distribution for flow quantities such as pressure coefficient from the current values. These constraints address common engineering design variables such as span load, section lift and pitching moment coefficients, and shock strength. In addition to the flow constraints, geometry constraints such as thickness, curvature, volume and leading edge radius are available to address requirements from other disciplines such as structures and manufacturing. All of these features combine to provide a design capability that is comparable in computer time and memory requirements to a single CFD flow analysis, thus allowing the use of large, detailed grids and high-level flow physics in design.
While CDISC is a very effective method for designing lifting surfaces such as wings and tails, it can be more difficult to use in regions of complex geometry. This difficulty arises both in accurately placing CDISC design stations in these areas as well as consistently interpolating geometry changes to the surface grid between them. The SUSIE station-less design method was developed in an attempt to mitigate these difficulties. This method uses a knowledge-based flow/geometry algorithm similar to CDISC, but operates directly on each active surface grid point. It is extremely easy to set up a design case with SUSIE and it has proven to be effective in cases with subsonic and transonic flow where simple smoothing or limiting of a flow variable is desired.
The LM developed KNOPTER optimization process (depicted in Figure 2) adds an outer loop of
optimization to CDISC and SUSIE that enables rapid, drag based optimization with the traditional CDISC and SUSIE knowledge based design methods. With this process, KNOPTER drives the inputs to CDISC and SUSIE using the Sandia-developed DAKOTA optimizer. This process permits complete drag based optimization to be performed typically within two calendar

days – an approximate 3 to 5 times reduction in the time required for the standard designer-inthe-loop CDISC / SUSIE optimization process.
• KNowledge-based
OPTimizER
• KNowledge-based
•
Varies CDISC & SUSIE
OPTimizER
•
• Varies CDISC & SUSIE
Objective Functions
• Global Objective
Function Evaluation
• Enables Drag-
• Uses 2-D Design Stations
• Specify Flow Features
• Airfoil Pressures
• Shock Location
• Span Loading
• Specify Geometric Features
• Spanwise Thickness
• Spanwise Twist
Alters Shock Location,
Rooftop Pressure
Cp x/c
Direct modification of surface grid eliminates need for stream-wise design stations in regions of complex curvature
Figure 2: KNOPTER Optimization Process Overview
Wind Tunnel Testing
The overall objective of the test effort was to demonstrate TRL 5+ validation of the efficient transonic cruise performance of the SACD developed configuration, which requires component validation in a relevant environment. The critical components for validation were the integrated jet effects and the overall planform characteristics. The relevant environment was transonic speeds and flight Reynolds numbers. This led to the selection of powered, semi-span testing in
NASA’s NTF as it is the only facility capable of meeting all requirements. Powered testing was
performed using the newly developed NTF Air Station described in Reference 11. The air
station routed high pressure air across the tunnel sidewall to the nozzle for propulsion simulation and to the circulation control (CC) system incorporated on the outer wing. This test advanced the state of the art as it represented the first-ever production, flight Reynolds number, powered, high speed test ever performed in the NTF. The risk associated with this first ever milestone was

reduced with a NASA sponsored jet effects check-out test on the FAST-MAC model (described
in Reference 12).
Aircraft Design
The SACD configuration was designed around the unique HPLS which enables STOL performance in an efficient transonic cruise compatible configuration. This system culminated from LM internal research and development efforts initiated in the 1980’s with component validation progressing through the early 2000’s under AFRL sponsorship. This research builds upon and extends previous efforts which culminated in TRL 3 validation of the efficient transonic cruise performance of the integrated system.
The process utilized to design the aircraft incorporating the HPLS is depicted in Figure 3. This
process progressed in three distinct design stages incorporating conceptual, preliminary, and detailed design efforts. Each phase of this design process is described in the following sections.
Even though a primary emphasis of the discussion is on aerodynamic design, the overall configuration structural layout, weight estimates, propulsion system, and stability and control characteristics were also refined.
Design Synthesis Methodology
PROPULSION
MODELING
Navier-Stokes CFD Based
Aerodynamic Design
Advanced
Tips
Optimized
Empennage
MISSION
MODELING
AERODYNAMIC
MODELING
Optimized
Wing
INPUTS
Requirements
Constraints
Etc.
Synthesis
Engine
OUTPUTS
Trade Studies
Optimizations
Sensitivities
Optimized Aft
Fuselage
WEIGHTS
MODELING
CONCEPTUAL DESIGNER
Rapid Conceptual Design (RCD)
Employs An Integration Framework
To Provide The Link Between Design
Tools & Analysis Tools
CAD
MODELING
Re-Designed
REN Flap
Improved
PAI
Figure 3: Overview of the Process Used for the Aircraft Design
Conceptual Design Phase
The objective of the conceptual design phase is to develop the “best” overall configuration arrangement by evaluating numerous alternative design concepts against the design requirements. This phase of the aircraft design process emphasizes overall design characteristics rather than fine details of the configuration. LM utilizes a Rapid Conceptual Design (RCD) framework that integrates parametric aerodynamic, mass properties, propulsion, configuration,
and mission performance tools to perform aircraft conceptual design. As depicted in Figure 4,
RCD is a multidisciplinary design and optimization (MDAO) process that uses Phoenix
Integration’s Model Center to integrate a suite of specific parametric analysis tools. This framework enables the execution of complex trade studies through design of experiments (DOE).
This RCD process was utilized to develop a CAD loft for use in the preliminary design process.

PROPULSION
MODELING
MISSION
MODELING
AERODYNAMIC
MODELING
INPUTS
Requirements
Constraints
Etc.
Synthesis
Engine
OUTPUTS
Trade Studies
Optimizations
Sensitivities
WEIGHTS
MODELING
CONCEPTUAL DESIGNER
Rapid Conceptual Design (RCD)
Employs An Integration Framework
To Provide The Link Between Design
Tools & Analysis Tools
CAD
MODELING
Figure 4: RCD Design Environment
Preliminary Design Phase
The objective of the preliminary design phase is to rapidly evaluate significant configuration design variations utilizing higher fidelity analysis processes in order to develop a refined configuration suitable for use in the detailed design phase. The preliminary design process is
depicted in Figure 5. From this figure it can be seen that the loft developed in the conceptual
design phase was used as a starting point for overall configuration design trades. During this design phase, Navier-Stokes (N-S) CFD simulations were utilized to evaluate the overall configuration performance and to develop recommended gross configuration geometry changes for improved performance. These recommended changes were then lofted in CAD and the overall process repeated until the desired improvements were achieved. High fidelity N-S CFD simulations were selected for use in this design phase as they provide the appropriate fidelity
needed to perform the desired design trades. For example, as depicted in Figure 5, design trades
on overall propulsion airframe integration (PAI), forebody shape, and raked wing tip shape were performed. These trades were performed primarily at transonic cruise conditions and the resulting complex integrated propulsion and shock interactions could only be accurately simulated with these higher fidelity methods.
Additional design trades were also performed on empennage type, size, and placement. These trades were performed with lower fidelity Vorlax based aerodynamic analysis methods as they afforded appropriate fidelity to accurately predict the overall impact of the empennage on aircraft stability characteristics. As part of this empennage study, F-22, V, and longhorn type tails were evaluated. The longhorn type tail arrangement was selected based on performance and risk benefits.

Navier-Stokes CFD Based
Aerodynamic Design
Conceptual
Design Output
Detailed
Design Input
• Propulsion Airframe
Integration Trades
• Forebody Design
Trades
• Raked Tip Trades
• Empennage
Trades
Figure 5: Overview of CFD Based Preliminary Design Configuration Trade Study
A representative preliminary design trade is depicted in Figure 6 for propulsion airframe
integration (PAI). This figure depicts drag contours on the conceptual design configuration and the final PAI improved configuration generated at transonic cruise conditions. This figure also illustrates the complex flowfield and shock interactions resulting from the closely integrated engine and justifies the need for high fidelity Navier-Stokes CFD simulations in the preliminary design phase. These PAI improvements accounted for an approximate 100 count drag reduction at transonic cruise conditions by streamlining the external nacelle shape and altering the overall integration strategy. This result highlights the importance of the preliminary design phase in general and the use of appropriate high fidelity simulation methods in particular. This performance improvement was enabled with the rapid time-to-analysis of the high fidelity CFD simulations. Typical geometry variations could be meshed within 1 to 2 hours of receipt of the
CAD loft and resulting cruise solutions generated within 3 to 4 hours – permitting the effective assessment of multiple variations in parallel in a single day. This rapid turn-around enabled the use of these high fidelity methods early in the design process.
M=0.81, Cruise C
L M=0.81, Cruise C
L
Lower Surface
Lower Surface
Drag
Contours
Figure 6: Overview of Propulsion Airframe Integration (PAI) Preliminary Design Trade
Conducted with Navier-Stokes Based CFD

Detailed Design Phase
The objective of the detailed design phase is to finalize the fine details in order to achieve all possible performance from the configuration. During this phase all aspects of the overall aircraft
performance were evaluated and the final aerodynamic shape optimized. As depicted in Figure
7, all major aircraft components were optimized during this design phase. The wing, aft
fuselage, empennage, and raked wing tip shapes were all optimized using the KNOPTER high fidelity CFD based design process. In addition, improvements to the REN flap shape and the overall propulsion airframe integration were incorporated. Additional details of select component optimizations follow.
Optimized Aft
Fuselage
Re-Designed
REN Flap
Figure 7: Overview of Aircraft Components Optimized During the Detailed Design Phase
The wing shape was designed using the LM developed KNOPTER optimization process.
KNOPTER evaluated variations of wing twist, airfoil shape, and camber against constraints such as wing sweep, thickness, and spanload to minimize the configuration drag. Specifically,
KNOPTER drove the inputs to the CDISC design process which altered the wing shape to decrease the shock strength, vary the shock location, and improve the overall pressure distribution. Results from the KNOPTER optimization process on the wing at transonic cruise
conditions are depicted in Figure 8. This figure depicts CFD predicted surface pressure contours
generated on both the baseline and final optimized wings at transonic cruise speeds and fixed aircraft lift coefficients. Also depicted are the corresponding 2-D wing pressure distributions and airfoil shapes at three select span stations. This figure highlights the strong shock present on the baseline wing that was significantly reduced through the wing shape optimization while maintaining the overall aircraft lift coefficient. It also illustrates how the airfoil shapes were changed while maintaining thickness. The final wing shape was able to reduce aircraft drag by
40 counts at transonic cruise conditions. A total of 10 KNOPTER optimization cycles were required to develop the final wing shape -- which is the wall-clock equivalent of 10 standard
CFD flow simulations. Off-design conditions were also evaluated with full drag polars and drag rise curves generated over a range of flight conditions.

η = 95%
η = 95%
η = 65%
η = 35%
η = 95%
65%
35%
η = 65%
η = 35%
Baseline Optimized
Figure 8: Wing Shape Optimization Results at Cruise Conditions (M=0.81) with Surface
Pressure Contours and Airfoil Sections
The aft fuselage shape was optimized at cruise conditions using the KNOPTER optimization
process using the station-less SUSIE method as the shape design driver. As depicted in Figure 9,
there were only relatively small geometry changes between the baseline and optimized aft fuselage shapes. However, these changes resulted in a significant performance improvement as the aircraft drag was reduced by 12 counts. A total of 14 KNOPTER optimization cycles were required to develop the final aft fuselage shape, requiring approximately 36 hours of wall-clock time on 64 CPU cores of a linux based PC cluster.

Optimized Fuselage
Baseline Fuselage
Figure 9: Aft Fuselage Shape Optimization Results
The aerodynamic performance of the final optimized configuration from the detailed design
effort is depicted in Figure 10 along with the performance from the conceptual and preliminary
design phases. This figure depicts the aerodynamic efficiency, measured with Mach x (Lift /
Drag), plotted as a function of Mach number where each curve was generated at the design cruise lift coefficient. It illustrates that the aircraft aerodynamic efficiency was improved 28% during the preliminary design phase and an additional 15% during the detailed design phase along with an increase in cruise Mach number from ~M=0.77 to 0.81 (as defined with ML/D maximum). This figure highlights the importance of the preliminary design phase to the aircraft final performance as the largest performance improvements resulted from this design phase. The majority of these benefits resulted from the propulsion airframe integration and forebody design trades which required higher fidelity Navier-Stokes based CFD simulations. The result reaffirms the emphasis placed on improving the time-to-analysis of higher fidelity Navier-Stokes CFD simulation methods to permit their use earlier in the design process.

Conceptual Design
Preliminary Design
Detailed Design
28%
Improvement
15%
Improvement
0.68
0.7
0.72
0.74
0.76
0.78
0.8
0.82
0.84
Mach
Figure 10: Aerodynamic Performance History from the Conceptual, Preliminary, and
Detailed Design Phases
Wind Tunnel Model Design
A first step in the design of the transonic wind tunnel model was selection of an appropriate scale. Since the semi-span model incorporated air routed across the tunnel sidewall to simulate nozzle and CC flow, facility air availability had to be included in the model scale selection in addition to model blockage and balance capability concerns. Results from a model blockage, air availability, and balance capability assessment indicated that a 5.25% scale model is the maximum sized model that would meet all requirements. In addition, since a flowing inlet was not required for the model, a faired inlet design was developed. Results from the fairing design effort indicated that the final faired inlet design maintained the cruise Mach number but did result in increased drag at transonic cruise conditions of 10 drag counts. Consequently, CFD predictions were utilized to develop a correction for the NTF wind tunnel data for full aircraft performance assessments.
With the wind tunnel model scale established, the next step was to design the internal flow path needed to route the tunnel sidewall provided air to the nozzle and CC system. The initial nozzle
and CC system flow path design is depicted in Figure 11. From this figure it can be seen that a
single plenum routes air through two separate pipes to both the nozzle and CC system. A valve and choke plate is used to regulate the air supply to both components. Air was routed to the
model using the bellows system depicted in Figure 12. From this figure, it can be seen that
facility air is routed across the tunnel side wall, through the NTF semi-span balance, and to the model using a bellows system. As will be described in the Results section, this system had a

significant impact on the accuracy and repeatability of the balance measurements.
Nozzle choke plate 189 holes at 0.170” diam.
1200 psia max
(1000 psi

P)
CC choke plate 11 holes at
0.170” diam.
125 psia max
(50 psi

P)
350 psia max
(275 psi

P)
Tunnel Sidewall
Figure 11: Model Internal Flowpath Description
Pipe to Air
Supply
NTF Semi-Span
Balance
Bellows
Common Model
Plenum
Model Nozzle
Model CC Wing
Figure 12: Schematic of NTF Facility Bellows System Used to Route Air to the Model

The performance of the internal flowpath was verified using detailed Navier-Stokes based
USM3D CFD simulations. The resulting CFD mesh of the full aircraft including the internal
flowpath is depicted in Figure 13 along with a representative flow solution. From this figure, it
can be seen that the full flowpath was simulated, including the 189 holes in the nozzle and 7 holes in the CC choke plates. The CFD solutions were generated with boundary conditions applied at the pipe entrance to the main plenum. The results demonstrated that the maximum desired flow rates through both the nozzle and CC system could be achieved and that the flow aft of both the nozzle and CC choke plates is uniform with little cross sectional variation. These results verified the performance of the internal ducting for wind tunnel testing.
CFD Mesh of Model and Internal Ducting CFD Predicted Internal Flowfield
Figure 13: Internal Flowpath Performance Verified with Detailed CFD Simulations
Since wind tunnel model static aeroelastic effects at cryogenic test conditions in the NTF can be significant, they were accounted for by defining the model jig wing such that it would deform into the desired 1-g cruise shape under cruise loading conditions. The process used to define the model jig wing utilized a loosely coupled CFD / finite element method (FEM) approach (which incorporated all significant structural cut-outs) to calculate the external air loads, predict the resulting deformations, and converge on the final jig wing shape. The final predicted model
deformation characteristics at cruise, cryogenic conditions are depicted in Figure 14 with wing
twist and wing deflection plotted as a function of wing span. From this figure it can be seen that significant static aeroelastic deformations are predicted, resulting in an ~1 degree reduction in wing twist near the tip.

Wing Twist
Tw ist
Wing Deflection
LE & TE Delta Z
1.2
1.0
0.8
0.6
0.4
0.2
0.4
0.6
Wing Station
0.8
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
0.0
0 0.2
1 1.2
0.2
0.4
0.6
Wing Station
Delta Z LE Delta Z TE
0.8
1 1.2
Figure 14: Predicted Wind Tunnel Model Static Aeroelastic Deformations at Cruise
Conditions
Wind Tunnel Test
The High Speed Powered Model (HSPM) was a 5.25% scale semi-span model and was delivered
to NASA’s NTF in May of 2011. The model (depicted in Figure 15) was then installed in the
NTF test section, followed by several weeks of instrumentation hookup and checkout. The majority of air-on testing occurred during July – August 2011. During this period, there were approximately 482 runs performed, with 290 runs in air (120 deg-F) and 192 runs in cryogenic mode (-50 deg-F).
During testing, significant scatter was noted from the balance data. The scatter was attributed to the sensitivity of the NASA 5-component sidewall balance to bellows pressure and temperature.
It was decided to continue testing, but correct the final balance data post-test with a bellows pressure and temperature calibration effort.

Figure 15: Powered Semi-Span Model in the NTF Test Section
Unfortunately post-test corrections for balance sensitivity to bellows pressure and temperature were unable to reduce the scatter in the wind tunnel sufficiently. A typical unpowered drag polar
is depicted in Figure 16 at transonic cruise test conditions (M=0.81). This figure depicts
numerous repeat runs generated with both air and cryo test mediums. This figure highlights that even with post-test bellows corrections applied, there are approximately 15 drag counts of scatter in the air data and 50 drag counts of scatter in the cryo data. Numerous data reduction schemes were also evaluated, but none were able to substantially reduce the scatter. With this level of uncertainty in the unpowered data, configuration performance was unable to be ascertained.
However, drag rise results, depicted in Figure 17, were used to confirm the overall configuration
cruise Mach number of 0.81 (as defined with the maximum M x L/D) at the design lift coefficient.
Since this wind tunnel test was performed, NASA has developed substantial improvements to the air supply and routing arrangement. They are currently evaluating the balance accuracy of this upgraded system with a powered, semi-span wind tunnel test of a generic configuration.
Successful performance of this test may lead to a re-test of the SACD model.

Cryo
Air
20 Drag
Counts
Figure 16: There was Large Scatter in Unpowered Air and Cryo NTF Wind Tunnel Test
Data at Transonic Cruise Condition (M=0.81)
M cruise
= 0.81
Figure 17: Cruise Mach Number Verified with Unpowered Air Data

An overview was presented of the aerodynamic design, wind tunnel testing, and validation of a revolutionary STOL, survivable, and efficient transonic cruise compatible military transport. As described in the paper, the initial NTF air routing system, which permitted powered testing, had deficiencies that resulted in an inconclusive wind tunnel test effort as there was significant scatter in the test data. These deficiencies were due to the unique set-up for powered testing in the NTF. Enhancements to this system have been incorporated post-test and are currently being evaluated with the testing of a generic powered model. If successful, the SACD powered model may be re-tested.
The authors would like to acknowledge the generous help and support of fellow LM teammates
Chris Hardin and KC Martin for performance of the conceptual design effort, Neil Hall for performance of the empennage type, sizing, and placement trade study, and Fred Barberie for propulsion support. The authors would also like to acknowledge the development efforts of
Charlie Novak, who was the primary researcher of the HPLS technologies. Finally, the authors would like to acknowledge the support of former LM teammate Anthony Agelastos in the development of the KNOPTER optimization process.
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