AIAA 2015-0042
AIAA SciTech
5-9 January 2015, Kissimmee, Florida
53rd AIAA Aerospace Sciences Meeting
HPCMP CREATETM-AV and the
Air Force Digital Thread
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Dr. Edward M. Kraft §
United States Air Force
The United States Air Force is developing and applying the Digital Thread analytical framework to provide
engineering analysis capabilities and support to decision making over the entire lifecycle of air vehicles. The
Digital Thread merges physics-based modeling and data to generate an authoritative digital representation of
the system at each phase of the acquisition and sustainment process of a weapon system. The High
Performance Computing Modernization Program (HPCMP) Computational Research Engineering
Acquisition Tools Environment for Air Vehicles (CREATETM-AV ¥) is a key enabler for providing multidiscipline, multi-physics, multi-fidelity simulation of an air vehicle essential to the authoritative digital
representation of the system in the Digital Thread. In this paper, the Digital Thread is defined and the key
role CREATE-AV plays in implementation of the Digital Thread concept is presented. An overview of
several pilot studies designed to validate the value of the Digital Thread in support of acquisition is also
presented.
I. Introduction
Timely and cost-effective acquisition of military systems has deteriorated significantly over the last three
decades. Despite numerous attempts at acquisition reform, the number of acquisition programs behind schedule and
over costs continues to escalate. Behind the deterioration is a convergence of factors including increased complexity,
unproven technologies, low-balled cost projections, politicization of the technical process, and a shortage of skilled
engineers.
In response to these trends, the Office of the Under Secretary of Defense Acquisition, Technology and Logistics
(ATL) instituted a series of “Better Buying Power (BBP)” initiatives aimed at continuous improvement as the best
approach to improving the performance of the defense acquisition enterprise1. The latest edition, BBP 3.0, is a
continuation of the approach building on BBP 1.0 and 2.0 with a shift in emphasis through innovation and technical
excellence. In addition to always important affordability, some of the emphasis areas in BBP 3.0 focused on
technical achievement include the use of prototyping and experimentation, use of modular open system architectures
to stimulate innovations, reduce cycle time while maintaining sound investments, and strengthen organic
engineering capabilities.
Similarly, the Assistant Secretary of the Air Force for Acquisition has set as a priority to “Own the Technical
Baseline (OTB)2.” The tenets of this priority include system design, interface controls, an end-to-end system model
and ability to exercise it, development and operational performance data, data rights and open architectures, cost
actuals, and risk management.
In 2013, the US Air Force initiated two parallel activities that resonate with BBP 3.0 and OTB. The first, the AF
Chief Scientist lead examination of future technology trends resulted in the report “Global Horizons3.” In parallel,
the AF Chief Engineer was tasked to revitalize engineering across the AF resulting in the final report “The Air Force
Engineering Enterprise Strategic Plan4.” Common to both reports was a recommendation to develop an analytical
framework to support engineering, referred to as the “AF Digital Thread.” The intent of the analytical framework is
to:
§
Technical Advisor for Aerospace Ground Testing, AEDC/CZ, 100 Kindel Dr, Arnold AFB, TN 37389-1327,
AIAA Fellow.
¥
Material presented in this paper is a product of the CREATE-AV Element of the Computational Research and
Engineering for Acquisition Tools and Environments (CREATE) Program sponsored by the U.S. Department of
Defense HPC Modernization Program Office.
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This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States.
• Reverse unsustainable trends in Acquisition and Sustainment of warfighting capabilities through better system
insight and knowledge at key decision points for risk management
• Develop a model-based systems engineering framework to leverage existing tools and expertise in industry and
the government
• Enable virtual prototyping and experimentation earlier in the lifecycle
• Optimize reuse of knowledge over the entire life cycle and from program to program
• Rebuild a lost generation of technical expertise to better support acquisition and sustainment
• Enable the government to regain insight and ownership of technical knowledge
Downloaded by Steven Dunn on January 5, 2015 | http://arc.aiaa.org | DOI: 10.2514/6.2015-0042
Also recognized in these two reports was the need for applications of physics-based modeling to systems
engineering to support the Digital Thread analytical framework. The Digital Thread requires a multi-discipline,
multi-physics, multi-fidelity capability with the ability to rapidly and efficiently generate reduced order models for
surrogate representations. The HPCMP CREATETM-AV air vehicle modeling capability exhibits these
requirements and is identified as a key enabler for the AF Digital Thread as an instantiation of the need for an endto-end system model in support of the Own the Technical Baseline emphasis. In this paper, the important features
of CREATE-AV capability that enable the Digital Thread are presented.
II. The AF Digital Thread / Digital Twin
The term “digital thread” originated in the aerospace industry to describe an integrated systems engineering
process for digitally managing the entire process from the 3-D CAD design of system components through the
manufacturing, assembly, and delivery of the system. The digital thread, in this context, included model based
engineering (MBE), the digitized drawings, the directly generated bill of materials, the manufacturing processes,
assembly logistics, configuration management, and delivery of the system.
For the last several years the AF Research Laboratory Materials and Manufacturing Directorate has been
pursuing the development of technologies focused on applying physics-based modeling to the manufacturing,
operations, and sustainment of aircraft structural elements. This technology thrust is referred to as the “Digital
Twin” with a long term goal of using physics-based modeling and probabilistic analysis to forecast the life cycle by
part number and by aircraft tail number of structural elements with the intent to reduce the cost of Operations and
Sustainment.
The “Digital Thread” as envisioned in the Global Horizons report and AF Engineering Enterprise Strategic Plan
is intended to expand the principles of MBE, industry’s digital thread, and the Digital Twin to encompass the entire
life cycle from early research through development planning, design and manufacture, testing, operations,
sustainment, and training as illustrated in Figure 1. The combined Digital Thread / Digital Twin encompass the Air
Force’s model-based engineering analytical framework over the entire life cycle.
As depicted in Figure 1, each component of the overall life cycle process represents an engineering community
of practice with unique expertise and model-based tools. Many of the very high fidelity physics-based models used
in the various domains are not conducive to either supporting rapid parametric analyses to support probability-based
decision making or to share information across the interfaces between the domains. The intent of the Digital
Thread is to translate output from these specialized high-fidelity tools into digital surrogate models using response
surface methodologies and modern high performance computing resources. These reduced order models are
inserted into the Digital Thread through an Applications Interface Protocol (AIP) at the appropriate decision points.
These reduced order models enable rapid manipulation of the output from the physics based model by translating the
complex code’s output into equation based regressions with negligible loss in accuracy of the original tools. The
reduced order models can be executed in fractions of a second instead of hours or days and provide on-the-fly
tradeoffs which yield resilient, robust solutions to support critical decisions.
An authoritative digital surrogate representation is formed by combining the reduced order models, empirical
data as the system moves into physical prototyping and testing, and statistical engineering to characterize the system
and quantify margins and uncertainties at key decision points. An important characteristic of the Digital Thread is
that a single authoritative digital surrogate representation exists in the system at any instance in time and is
accessible by both the government and industry. In this way, each domain is working with the same definitive
representation of the performance of the system. For example, the reduced order model representation of the
aerodynamic characteristics of the vehicle – the fly out model – can be developed in the earliest phases of
developmental planning, enhanced through the detailed design process, and reach its final maturation in testing.
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Other interested communities such as flight simulators, system integration labs, hardware in the loop facilities,
trainers, etc., can access this single authoritative digital surrogate representation of the flight characteristics of the
vehicle. This assures configuration management and reduces costs associated with duplication and parallel
activities by different engineering domains.
Use of reduced order models does not necessarily translate into reduced accuracy models. Through the use of
statistical engineering methods, uncertainties in the original physics-based models as well as uncertainties in the
response surface surrogates can be quantified at each step of the development process. As experimental data is
generated it can be merged with the computational data to update the surrogate response surface and refine the
uncertainty measurements. The use of experimental data as it becomes available assures the authoritative digital
surrogate is the best available information within the accuracies of the experimental data itself.
The box labeled “Industry Proprietary” in Figure 1 is not intended to connote that industry plays a narrow role in
the overall life cycle of a system. On the contrary, industry is involved in essentially the entire lifecycle. The
intent of the Industry Proprietary box is to indicate that by including industry’s input into the Digital Thread as a
reduced order model authoritative digital surrogate, it is not necessary for the government to have access to internal,
proprietary design tools and practices. This does, however, enable the government to have in-depth technical
comprehension of the performance of the system to make the government a “smart buyer” and to realize the Air
Force’s intent to Own the Technical Baseline by acquiring, maintaining, and exercising a system model over the
entire life cycle.
Simultaneous with the Air Force Digital Thread / Digital Twin initiative, the Office of the Secretary of Defense
Systems Engineering Office has developed the principles for a Digital System Model which is described in Chapter
4 of the Defense Acquisition Guide5. The Digital System Model is focused on the policy guidance and structure for
implementing a digital representation of a weapon system while the Digital Thread / Digital Twin are focused on the
model-based technical approach to developing the digital representation. OSD and the AF have a collaborative
working group aligning the Digital System Model and the Digital Thread / Digital Twin in support of the life cycle
systems engineering processes.
The current definitions the OSD and AF working group, in concert with industry, have developed for the Digital
System Model, Digital Thread, and Digital Twin are:
Digital System Model - A digital representation of a weapon system, generated by all stakeholders, that
integrates the authoritative data, information, algorithms, and systems engineering processes which define all
aspects of the system for the specific activities throughout the system lifecycle.
Digital Thread - An extensible, configurable and Agency enterprise-level analytical framework that seamlessly
expedites the controlled interplay of authoritative data, information, and knowledge in the enterprise datainformation-knowledge systems, based on the Digital System Model template, to inform decision makers throughout
a system's life cycle by providing the capability to access, integrate and transform disparate data into actionable
information.
Digital Twin - An integrated multi-physics, multi-scale, probabilistic simulation of an as-built system, enabled
by Digital Thread, that uses the best available models, sensor information, and input data to mirror and
predict activities/performance over the life of its corresponding physical twin.
III. HPCMP CREATETM-AV
TM
CREATE -AV is one of three primary elements of the CREATETM Program, established in 2008 by the DoD
High Performance Computing Modernization Program to enable major improvements in defense acquisition
engineering workflows associated with the design and analysis of Air Vehicles, Ships, and Radio Frequency
Antennas. The CREATE-AV Project is designed to develop, deploy, and support a set of multi-disciplinary,
physics-based simulation software products for the engineering workforces supporting air vehicle acquisition
programs6. The products are designed to exploit the capacity of next generation computer resources and enable
increased capacity of the engineering workforce, reduce workloads through streamlined and more efficient
engineering workflows, and minimize the need for rework through early detection of aircraft design faults and
performance anomalies.
CREATE-AV is comprised of three products that encompass the full range of design and analysis requirements
for air vehicles. The products are described in Ref. 6 and include:
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DaVinci –an early phase acquisition tool designed around a unified life-cycle engineering model encompassing
multi-fidelity analysis for a wide range of applications. At its core, DaVinci provides next generation modeling
capability for functional analysis, alternative design evaluation, trade-space exploration, and acquisition planning.
Kestrel –high-fidelity physics-based simulation designed from the ground up to address fixed-wing aircraft in
flight regimes ranging from subsonic through supersonic flight, including maneuvers, multi-aircraft configurations,
and operational conditions. Key functional attributes of Kestrel include capability to simulate complex maneuvers,
propulsion effects, moving control surfaces, aero-elastic effects, multiple-body relative motion, and incorporation of
realistic inner loop and outer loop control laws.
Helios – high-fidelity modeling capability for rotary wing simulations comparable to Kestrel. Specific
capabilities include off-body adaptive mesh refinement and the ability to handle multiple interacting rotorcraft
components such as the fuselage, rotors, flaps and stores. The off-body adaptive mesh capability is critical to
capturing and persisting vortex flow structures ubiquitous to rotor craft vehicles.
In addition, a modular propulsion system modeling capability called Firebolt is integrated into all three products.
Firebolt can model propulsion systems from simple zeroth order “engine deck” semi-empirical models through full
three-dimensional, high-fidelity, physics-based models of rotating fans and compressors.
The key features the CREATE-AV tools, particularly Kestrel, bring to the Digital Thread are:
• Multi-discipline, multi-physics, multi-fidelity capability
• Ability to rapidly and efficiently generate reduced order models for surrogate representations
• Ability to address system integration issues during detailed design
• Scalability to take advantage of high performance computing assets.
The ability to rapidly generate reduced order models for surrogate representation in the Digital Thread sets the
CREATE-AV tools apart from other air vehicle modeling tools. Kestrel, in particular, is an integrating product that
allows cross-over between simulation of aerodynamics, dynamic stability and control, and structures. At a fixed
Mach number and altitude, a prescribed computational maneuver can be performed exercising roll, pitch, and yaw at
various angles and rates and then creating a system identification model of the output loads information7. Typically
a time-accurate, multi-axis, computational training maneuver incorporating pitch, roll, and yaw is performed using
Kestrel. The maneuver incorporates a chirp signal (sinusoid with varying frequency and varying amplitude) and
excites a range of aerodynamics during the maneuver. The resulting input angles, rates and output loads history are
then modeled using system identification software (SIDPAC) which constructs a mathematical model from input
and output data for a system under testing and characterizes the system uncertainties and measurement noises.
These models allow aerodynamic coefficient data as well as aerodynamic loads data to be extracted for comparison
against known values. The training maneuver incorporates multiple rotations and/or translations to ensure proper
"regressor space" coverage. The "regressor space" is the range of angles and rates that an aircraft would typically see
during normal flight. The output from the computed chirping maneuver is a reduced order response surface that can
immediately be used as a fly out model for engagement models and/or flight simulators as depicted in Figure 2.
This is in contrast to the normal application of computational fluid dynamics which is used to compute the flow field
around the aircraft at each angle of attack and sideslip to determine the loads at that computed condition in a direct
mimic of how data is generated in a wind tunnel. The use of Kestrel with SIDPAC and high performance
computers to provide a good prediction of aerodynamics and structural loads has been successfully validated for
various aircraft.
V. Digital Thread Pilot Studies Using CREATE-AV
The earlier in the lifecycle the Digital Thread is initiated for an acquisition program the more positive impact it
will have on the overall total ownership cost and cycle time. Resurgence across the Air Force in Developmental
Planning (DP) is increasing emphasis on the use of physics-based modeling as early as the Materiel Development
Decision (MDD) and Analysis of Alternative (AoA) phases used to define requirements for the system of interest.
Traditionally, the modeling tools available during the MDD and AoA phases are the well-known hierarchy of
Campaign, Operation, Mission, and Engagement models as illustrated on the left hand side of Figure 3. The higher
up the hierarchy, the less the actual physical behavior of a materiel solution of interest is involved. Physics-based
modeling of a flight vehicle, as depicted on the right side of Figure 3, has generally been challenging to implement
during the MDD and AoA phase because of the time and cost required to provide solutions for concepts of interest.
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Also as noted in Figure 3, there is a large disparity between computational times for operational engagement models
and physics-based models. Consequently only a few conceptual designs are analyzed sufficiently to support the DP
trade studies. Engineers calculating the performance trade space could only deliver point designs to the cost
estimators and mission utility analysts to evaluate the cost-effectiveness of the concept. Usually the designers could
not iterate more than once or twice with the mission utility analysts due to funding and timeline restrictions. This
would typically result in a concept at the dominant frontier of the design space which is typically the highest risk
and highest cost design. This process left the designers wishing they had more robust and resilient assessments
much sooner in the design process to better inform their design selections.
The Digital Thread enables the use of a physics-based digital surrogate to develop a “Modeling Commons” as
also depicted in Figure 3. The Modeling Commons provides the surrogate representation of the output from the
physics-based models in a convenient algebraic format or response surface that can be ported directly into
Engagement level models. Using high performance computing assets, the physics based modeling can encompass
the entire design space for concept systems in a relatively short time frame. The integration of these two tools
enables a resilient, robust look at the trade space for system feasibility, mission utility, and affordability.
Not only will the Modeling Commons enable interfacing with Engagement models, but the same digital
surrogate content can be imported into flight simulators like the SIMAF at Wright Patterson Air Force Base. The
SIMAF opens a window into live-virtual-constructive simulations of systems of systems. More importantly,
introducing the physical performance of a platform of interest into the SIMAF opens up the opportunity to evaluate
interoperability through use of the emerging Air Force Intelligence Surveillance and Reconnaissance MultiResolution Analysis (ISR-MRA) capability that will be will described in more detail below.
A. Addressing Feasibility, Affordability, and Mission Utility During
Developmental Planning
With reference to Figure 3, the first step in developing the modeling commons is to connect physics-based
models with operational engagement models. The DoD Engineering Resilient Systems (ERS) Community of
Interest in FY 2013 funded an ERS pilot study which was an important formative precursor to the AF Digital
Thread.
The ERS pilot project successfully demonstrated a methodology to support resilient system design. The trade
space exploration environment effectively fused performance, effectiveness, and cost data into a single design trade
space to give the analyst and design engineer the insights needed to make informed design decisions. The resulting
analysis established well-founded and traceable point designs to bring to decision makers or to feed into Concept
Characterization and Technical Description Documents (CCTD's) for an Analysis of Alternatives (AoA).
The ERS trades pace analysis for a potential next generation cargo transport, designated the C-X, is illustrated in
Figure 4. Although the ERS C-X initially planned to use DaVinci to address the parametric design space, the early
version of DaVinci was not complete enough to support the analysis. Instead, an AF in-house design tool called the
Integrated Computer Aided Design (ICAD) program was used to evaluate the mission performance characteristics.
The techniques and lessons learned from the ERS C-X study, however, are being integrated into the DaVinci toolset.
The process outlined in Figure 4 starts with evaluating potential air vehicle design parameters such as wing
aspect ratio, wing sweep, takeoff gross weight, wing loading, payload weight, payload bay dimensions, etc. Three
separate candidate cargo transport configurations were used in the trade study – a traditional tube and wing, a
blended wing body, and an integrated wing body. Five thousand cases were run for each configuration in ICAD to
develop the digital surrogate response surface for the output parameters. The reduced order response surface was
typically within 1-2% of the actual predicted values and did not degrade the integrity of the trade space analysis.
The outputs from ICAD which were designed to match up with required inputs for the operational engagement
model. Representative outputs from ICAD included max payload range, airspeed, average fuel burn rate, range,
payload with max fuel, takeoff distance, landing distance, empty weight, max thrust, life cycle costs, and cost per
flight hour.
The performance outputs from ICAD were a good match for the operational engagement model called the
Analysis of Mobility Platform (AMP). AMP is a tool that models the air, sea, and land deployment of military
cargo. Representative outputs from AMP include fuel effectiveness, total fleet fuel used, average sorties, average
fuel usage, average days late, aggregate day late, peak aircraft used, etc.
Three mission scenarios were employed in AMP to evaluate mission utility – a transatlantic deployment to
Tunisia, a transpacific deployment to Australia, and a domestic humanitarian relief operation to Haiti. Each
scenario ran with 2,500 different C-X design definitions to build a data set for regression and surrogate model
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creation. The reduced order models permitted a design optimization search leading to 9 candidate designs (3
configurations times 3 scenarios).
Data regression experts at the Georgia Institute of Technology Aerospace Systems Design Laboratory were
invaluable in developing polynomial response surface and neural network equations to fit the data. They also
supported the development of a visualization environment which fused performance, effectiveness, and cost data to
enable analysts to change aircraft design parameters and immediately see the impact on mission effectiveness and
costs. The right side of Figure 4 suggests a screen shot of this visualization environment.
The ERS C-X pilot study successfully demonstrated the ability to connect physics based models with operational
engagement models in support of early AoA analyses as the initial first step to establishing the Digital Thread digital
surrogate representation of candidate concepts. It is important to recognize that the surrogate response surfaces
developed in the early AoA analyses can be reused for alternative mission scenarios and other trade studies as well
as design parameter sensitivity coefficients to aid in detailed design later in the acquisition process.
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B. Addressing Interoperability During Developmental Planning
One of the findings of a recent AF Studies Board on Intelligence, Surveillance, and Reconnaissance (ISR)8 is a
lack of integrated modeling and simulation and analysis tools that provides traceability from requirements to
capability and that conducts operationally-relevant ISR trade-space analyses across the doctrine, organization,
training, materiel, leadership and education, personnel, facilities, and policy (DOTMLPF-P) framework and within
and across air, space, and cyberspace domains. Based on these findings, the AF initiated the development of an
Intelligence, Surveillance, and Reconnaissance Multi-Resolution Analysis Capability (ISR-MRA) providing the
DOT_LPF-P environment to address systems-of-systems, networks, modular open architectures, non-materiel
alternatives, etc, that impact the interdependence and interoperability of platforms of interest. Although the ISRMRA environment can make use of physics-based models, it depends on other modeling tools to generate the
physical characteristics of a platform of interest.
The Air Force initiated development of the ISR-MRA environment in FY13 using the overall concept depicted in
Figure 5. The entry point for CREATE-AV inputs into the ISR MRA environment comes through the Physics
Based Capability and Architecting Analysis (PCA) which incorporates platform performance characterization,
systems and sensor configurations, and interactions. The PCA is centered in the SIMAF flight simulator
environment.
The SIMAF centered PCA capability interfaces with the Analysis of Netted Information and Integration (ANII)
where the architectures and network topologies necessary for Blue systems to communicate properly to support
planned missions are modeled. The PCA and ANII form an iterative loop which permits the analyst to trade
architecture and system characteristics to define available BLUE information architecture (integrated systems and
network topologies) to support various mission threads.
The Mission Utility Analysis (MUA) domain is a Campaign and Mission level assessment tool that allow the
user to assess mission effectiveness, and/or campaign effectiveness for the integrated information architecture under
assessment. The iterative loop between MUA and ANII allows the analyst to understand the network topology
trade space (sensitivity of network topology parameters and options with mission effectiveness for various
missions/mission threads) and balance mission utility versus cost drivers.
The interaction of the Digital Thread Modeling Commons with the ISR MRA is also illustrated in Figure 5. The
physics-based platform trade space analysis and connection to an operational engagement model as outlined in the
ERS C-X demo provides two thirds of the modeling commons. The other key element of the modeling commons is
the detailed fly out model of selected optimum vehicle configurations which is provided as input to the PCA. This
is achieved using the Kestrel code plus system identification as depicted in Figure 2.
The Air Force is currently performing a Digital Thread interoperability pilot study using a generic next
generation tanker configuration to exercise the various models and iterative loops indicated in Figure 5. A
parametric design space analysis will be performed using the same process as the ERS C-X study but with different
mission scenarios and different operational engagement models. This analysis will provide the first assessment of
feasibility, mission utility, and affordability. The optimum configurations resulting from the trade space analysis
will then be modeled in Kestrel to provide the fly out model for insertion into the SIMAF PCA environment. Note
also that the SIMAF facility is an active node in distributed live-virtual-constructive exercises which provides an
additional assessment of a concept vehicle in a system-of-systems environment.
Successful completion of this Digital Thread pilot study will demonstrate for the first the time the ability to
address interoperability and interdependence at the earliest requirements setting phase. The CREATE-AV toolset
provides an important complement to the ISR MRA environment to enable this assessment of interoperability.
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C. Reducing Cycle Time During Development Test and Evaluation (DT&E)
The system development cycle time from the Milestone B (MS-B) decision to design and build the system until
the Initial Operational Capability (IOC) has dramatically increased over the last several decades (Figure 6) until the
time from MS-B to IOC for the Joint Strike Fighter is well in excess of 200 months. Although there are multiple
causes for this unsustainable trend including system complexity, lack of open, modular architectures, and reduced
capacity of the aeronautical industry, there are fundamental engineering practices that have not significantly changed
in decades.
One of the engineering practices that have not changed significantly in the past 30 years is the overall
Developmental Test & Evaluation (DT&E) campaign required to develop an air vehicle. The DT&E processes
overlap 85% of the total development cycle time between MS-B and IOC. In spite of increases in testing
efficiencies, a nominal wind tunnel campaign requires 22,000 Occupancy Hours9 in an array of wind tunnels and 68,000 sorties for the flight test campaign. The typical wind tunnel campaign requires nominally 4 years while the
typical flight test program requires 6-8 years – a total of 10-12 years of developmental testing cycle time.
The total workload involved in aeronautical system development is primarily process driven. For example, the
wind tunnel campaign for a major fixed-wing aircraft has persistently required about 22,000 hours of wind tunnel
testing for a specific configuration, which given today’s national capacity of about 6,000 hrs/year, requires 3 to 4
years to conduct. Surprisingly, wind tunnel campaigns are traditionally designed around test hours, not test points.
That is why a fourfold increase in test productivity generated by the wind tunnel community in the 1990s had
essentially no impact on reducing the number of wind tunnel hours for the F-35 program10,11. As compared to the F22 wind tunnel campaign ( conducted a decade earlier before the major efficiency gains) the F-35 still used the
same nominal 22,000 hours for each configuration even though the F-35 has a smaller flight envelope.
The application of Kestrel with system identification software as described above enables an innovative new way
to determine the static and dynamic stability and control (S&C) characteristics of high-performance aircraft. In
contrast, the traditional “brute force” approach to filling an entire S&C database for an aircraft requires about 2.5
million data points in the wind tunnel. Correspondingly the traditional approach to using computational fluid
dynamics (CFD) to develop the performance and S&C of the aircraft tried to emulate the wind tunnel by computing
the aerodynamics point by point. Thus the straightforward application of CFD to define the S&C of the flight
envelope of a platform never became a viable approach.
An alternate approach using the capabilities of Kestrel is outlined in Figure 7. The reduced order response
surface invites the opportunity to apply design of experiments (DOE) to reduce the overall cycle time for the wind
tunnel tests. Attempts to apply DOE to streamline a traditional individual wind tunnel test have been only
marginally successful because current wind tunnels are not conducive to rapidly changing parameters to optimize
randomness of the data set. However, if one shifts to thinking about DOE at the “campaign” level there may be a
more productive approach to using DOE. Instead of the one-factor-at-a-time (OFAT) approach to building the
colossal database characteristic of today’s aeronautical development processes, an approach applying DOE to the
computed Digital Thread response surface could be more effective.
Given the outer mold line (OML) of the vehicle the response surface is generated as described in Figure 2. The
uncertainty of the response surface needs to be quantified using statistical engineering methods. Those areas on the
response surface which still exhibit a high degree of uncertainty then become the primary focus for the wind tunnel
test campaign, i.e., the focus is put on key areas for risk reduction versus defining the entire parameter space. The
mathematics of the DOE methodology helps assure the optimum data set is taken. The alpha and beta (or power
coefficients) of the DOE process can be used to address how much further variance can be reduced on the response
surface by an additional calculation, wind tunnel test, or flight test. There is a point at which doing another CFD
solution will not reduce uncertainty further; hence, one needs to move on to wind tunnel testing. Likewise, there is a
point of diminishing return for doing another wind tunnel test and the program needs to move on to flight testing.
Thus, unnecessary modeling and/or testing can be minimized. The beta coefficient also provides some insight into
the probability that a defect is being passed downstream to the next development step.
This approach is being validated in the Digital Thread Pilot Study using existing data bases for the F-22 program.
As illustrated in Figure 7, the OML of the F-22 is input into Kestrel and the chirping maneuver at a number of Mach
number and altitudes are computed to define the overall surrogate response surface. DOE will be applied to
determine the minimum number of experimental data points required to define the performance and S&C of the
vehicle. The comprehensive wind tunnel data base will be used to develop an equivalent experimental response
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surface defined to be the ground truth for the performance and S&C for the vehicle. A minimal set of data points
prescribed by the DOE analysis will be selected from the original wind tunnel data and analyzed to see if they
provided an equivalent definition of performance and S&C. Uncertainties in the reduced-set experimental response
surface will be evaluated using statistical engineering methods. Statistical techniques will also be used to merge the
computed response surface with the minimal experimental data base to establish the authoritative digital surrogate
model of the performance and S&C. This output will also be compared with the Lockheed standard performance
model for the F-22. Based on the success of this initial pilot study, a follow on study will evaluate extending the
same approach to reduce the amount of flight testing required.
It should be recognized that the use of Kestrel and system identification to produce the performance and S&C for
the streamlined test campaign is exactly the same modeling process used to provide a fly out model for insertion into
the SIMAF facility to evaluate interoperability. This suggests that these calculations could be performed during the
requirements setting phase and be used to address testability of systems of interest as well as develop an initial
strategy for designing a minimal approach to the test campaign. Information from this early evaluation of an
optimum test campaign can be used to structure the Test and Evaluation Master Plan (TEMP) required at Milestone
A as well as provides guidance in the pre-Milestone B Request for Proposal. This will ensure that a minimal cycle
time for testing will be implemented into program planning at the earliest possible phase.
The response surface method also provides an invaluable approach to supporting integrated developmental
testing (DT) and operational testing (OT) as well as addressing networking and interoperability issues. The
characteristics of the vehicle captured in the response surface can be translated directly into the performance math
engine for a manned flight simulator. Even at the earliest phases of development, this manned flight simulator can
start to address some of the operational integration issues thereby allowing integrated DT/OT earlier in the program.
If early brass-board or digital models of the avionics and communications packages are brought into the manned
flight simulator, the evolving performance of the system can be evaluated as a node in a distributed mission
simulation. Feedback from this integrated approach can be used in the very early stages to improve the design for
maximum performance as an interoperable system. Today, most of the OT interface issues as well as interoperability
are not addressed until very late in the development process. The overall impact on reducing development cycle time
using such an innovative approach could be immense.
Finally, the same computations using Kestrel to establish the performance and S&C of the platform can also
provide a nodal analysis of the aerodynamic loads on the vehicle through the use of Proper Orthogonal
Decomposition of the spatial and temporal distribution of the aerodynamic forces12. The next version of Kestrel
will include a full finite element structural analysis that will enable analysis of structural elements. Coupling this
early model based assessment of the structural loads with advanced test techniques in the wind tunnel using pressure
sensitive paint13 can provide insight into structural loads for detailed structural design. Also as the OML potentially
changes to improve aerodynamic performance, the structural loads can be efficiently updated. This should enable a
more rapid closure of the design and weight management. Lack of closure of the design at the Critical Design
Review, as measured by either critical drawings released or actual vs design weight, can drive a significant cost and
schedule overrun. Likewise, if structural design and weight issues continue past first flight, the risk of increased
cost and cycle time increases significantly. Discovery of a major structural after first flight can require 1-2 years
and of the order of $1B to overcome.
V. Concluding Remarks
An additional Digital Thread pilot study is also underway applying physics-based modeling of the material and
manufacturing processes to address disposition of nonconformities discovered in the Material Review Board process
during manufacturing and production. Applying probabilistic analysis to the material properties, this pilot is
expected to reduce operational and sustainment costs downstream. The same tools can also be used to address
future service life extension programs (SLEP) as a first step in the AF Digital Twin.
The CREATE-AV physics-based modeling tools for air vehicles are an integral enabler for Digital Thread pilot
studies for both early Capability Planning and Analysis and streamlined developmental testing during the
Engineering Manufacturing Development phase of acquisition. Full implementation for the Air Force Digital
Thread/Digital Twin approach has the potential to reduce the overall acquisition cycle time and costs.
The CREATE-AV enabled Digital Thread supports the Air Force priority to “Own the Technical Baseline” by
enhancing the system design, interface controls, end-to-end system model and ability to exercise it, development and
operational performance data, data rights and open architectures, cost actuals, and risk management.
8
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References
Downloaded by Steven Dunn on January 5, 2015 | http://arc.aiaa.org | DOI: 10.2514/6.2015-0042
1
Honorable Frank Kendall “Better Buying Power 3.0 White Paper “Office of the Under Secretary of Defense
Acquisition, Technology and Logistics, 19 September 2014 URL
http://bbp.dau.mil/docs/2_Better_Buying_Power_3_0(19_September_2014).pdf
2
Dr. William LaPlante “Air Force Acquisition” Comments Made at the Air Force Association - Air & Space
Conference and Technology Exposition, 16 September 2014, URL
http://www.af.mil/Portals/1/documents/af%20events/Speeches/16SEP2014-DrWilliamLaPlante-AFA-AFAcquisition.pdf
3
Maybury, Mark T. “Global Horizons Final Report, United States Air force Global Science and Technology
Vision,” AF/ST TR 13-01, 21 June, 2013. URL
http//www.defenseinnovationmarketplace.mil/resources/GlobalHorizonsFINALREPORT6-26-13.pdf.
4
Walker, David “AF Engineering Enterprise Strategic Plan,” May 2014. URL http://www.my.af.mil/gcssaf/USAF/AFP40/d/s2D8EB9D63EF1AADB013FA4B5882917FE/Files/editorial/AF%20EE%20STRATEGIC%20P
LAN_SIGNED.pdf
5
Defense Acquisition Guidebook, 28 June 2013 URL http//acc.dau.mil/CommunityBrowser.aspx?id=654219.
6
Meakin, Robert L., Atwood, Christopher A., and Hariharan, Nathan “Development, Deployment, and Support
of a Set of Multi-Disciplinary, Physics-Based Simulation Software Products,” AIAA Paper 2011-1104 Presented at
the 49th AIAA Aerospace Sciences Meeting, Orlando, Florida, January 4 - 7, 2011.
7
Dean, John P., Clifton, James D., Bodkin, David J., and Ratcliff, C. Justin, “High Resolution CFD Simulations
of Maneuvering Aircraft Using the CREATE-AV/Kestrel Solver,” AIAA Paper 2011-1109 presented at the 49th
AIAA Aerospace Sciences Meeting including the New Horizons Forum and Aerospace Exposition 4 - 7 January
2011, Orlando, Florida.
8
Arnold, Brian A. and Delaney, Lawrence J., “Capability Planning and Analysis to Optimize Air Force
Intelligence, Surveillance, and Reconnaissance Investments” Pre Publication Copy Air Force Studies Board
Committee on Examination of the Air Force Intelligence, Surveillance, and Reconnaissance (ISR) Capability
Planning and Analysis (CP&A) Process Report, National Research Council, National Academies Press, 2012.
9
Melanson, Mark R.. “An assessment of the increase in wind tunnel testing requirements for air vehicles over the
last fifty years,” AIAA paper 2008-830. Presented at 46th AIAA Aerospace Sciences Meeting and Exhibit, , Reno,
Nevada. January 7–10, 2008.
10
Kraft, Edward M. and Huber, Arthur F II “A Vision for the Future of Aeronautical Ground Testing,” The ITEA
Journal of Test and Evaluation, Vol 30, No 2, June 2009.
11
Kraft, Edward M. “After 40 Years Why Hasn’t the Computer Replaced the Wind Tunnel,” The ITEA Journal
of Test and Evaluation, Vol 31, pp. 329-346, September 2010.
12
Morton, Scott A., McDaniel, David R., Sears, David R., Tillman, Brett, and Tuckey, Todd R. “Rigid,
Maneuvering, and Aeroelastic Results for Kestrel - A CREATE Simulation Tool” AIAA Paper 2010-1233, 48th
AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition, Orlando,
Florida, 4 - 7 January 2010
13
Sellers, Marvin E.. Advances in AEDC’s lifetime pressure-sensitive paint program, AIAA paper 2005-7638.
Presented at U.S. Air Force T&E Days, December 6–8, 2005. Arnold Air Force Base, Tennessee
.
9
American Institute of Aeronautics and Astronautics
Distribution Statement A. Approved for public release; distribution is unlimited.
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Figure 1. The Air Force Digital Thread Architecture
Figure 2. Use of Kestrel and System Identification to Generate Reduced Order Response Surface.
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Figure 3. A Modeling Commons for Capability Planning and Analysis
Figure 4. Engineering Resilient Design Trade Space Analysis
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Figure 5. Merging the Digital Thread Modeling Commons with the ISR-MRA
Figure 6. History of Development Cost and Cycle Time for Tactical Aircraft.
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Figure 7. Digital Thread Approach to Reducing Developmental T&E Time and Costs.
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