Paper for the 53rd Structures, Structural Dynamics, and Materials Conference:Special Session on the Digital Twin

The Digital Twin Paradigm for
Future NASA and U.S. Air Force Vehicles
E. H. Glaessgen*
Durability and Damage Tolerance BranchNASA Langley Research Center, Hampton, Virginia, 23681
D.S. Stargel†
Aerospace, Chemical, and Material Sciences
Air Force Office of Scientific Research, Arlington VA, 22203

Future generations of NASA and U.S. Air Force vehicles will require lighter mass while being
subjected to higher loads and more extreme service conditions over longer time periods than thepresent generation. Current approaches for certification, fleet management and sustainment arelargely based on statistical distributions of material properties, heuristic design philosophies,
physical testing and assumed similitude between testing and operational conditions and will likely beunable to address these extreme requirements. To address the shortcomings of conventional
approaches, a fundamental paradigm shift is needed. This paradigm shift, the Digital Twin,
integrates ultra-high fidelity simulation with the vehicle’s on-board integrated vehicle healthmanagement system, maintenance history and all available historical and fleet data to mirror the lifeof its flying twin and enable unprecedented levels of safety and reliability.

Introduction

Existing methodologies for vehicle certification, fleet management and sustainment are largely
based on similitude and a heuristic understanding of the effects of operational and anomalous
conditions on the structural health, safety and performance of a vehicle. A common
manifestation of similitude and heuristics is in the form of the “factors-of-safety” used during
design and certification. So-called factors-of-safety are rooted in a heuristic legacy wherein afactor of, say, 1.5 or 2.0 has “always” been sufficient to account for a particular class of
unknown unknowns (e.g., loads, material properties). Often, the history and pedigree of suchfactors is uncertain.
Additionally, compounding of factors-of-safety is pervasive and may lead to unnecessarily heavy
structures and reduced performance without necessarily improving the actual safety of thevehicle or the probability of mission success. Even current probabilistic or reliability
methodologies are inadequate because they are based on assumed similitude between thecircumstances in which the underlying statistics were obtained and the environment in which thevehicle operates. When similitude is violated, probabilistic methods break down as readily as
those based on factors-of-safety. Although statistical assessments are important, they must bepart of an overall best-physics approach that is relevant to each individual vehicle.
*Assistant	
  Branch	
  Head.	
  	
  Associate	
  Fellow	
  AIAA	  †Program	  Manager.	
  	
  Senior	
  Member	
  AIAA	
  

	
  

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American Institute of Aeronautics and Astronautics	  Paper for the 53rd Structures, Structural Dynamics, and Materials Conference:Special Session on the Digital Twin

Similitude is so commonplace in the current engineering process that it is often invisible to theengineers who invoke it. A ubiquitous example can be found in engineering design and analysis
where computer codes (including commercial finite element codes) are used to “predict” failures.
Such codes are of limited “predictive” capability because, in a general sense, they only produce
responses that have previously been observed experimentally and then programmed for futureassessment. For example, to account for a phenomenon such as the interaction between
environment and structural damage, the issue must have been foreseen, specific experiments
must have been performed and their results must have been incorporated within the models.
Further, since different environments, or even different degrees of environment, have differingsynergies with damage, the specific environments that will be encountered by the vehicle musthave been considered during the design process. Otherwise, “worst case” scenarios must be used
to ensure the health and safety of the vehicle; albeit at the expense of weight and performance.
Another common manifestation of similitude and heuristics is in the form of inspection intervals
and schedules used for fleet management and sustainment. Inspection schedules are typicallydetermined from experience with similar vehicles that have performed similar missions. Often,
inspection is performed based on experience with the fleet leader, the vehicle in the fleet thatexperienced the most flights or the worst degradation. If, for example, unacceptable degradationof a component was found during an inspection of the fleet leader, it may be decided that all
vehicles of that class be inspected immediately regardless of the specific history of each vehicle.
These issues are amplified when new designs or operating conditions are considered. Unlike themethods used to insure safety and reliability of existing vehicles that have a clear and wellunderstood legacy, many future vehicles will have little direct precedent to follow, and in somecases (e.g., long duration space missions) the vehicle may be impossible to inspect and maintainin the conventional sense. Thus, the ability to fully understand degradation and anomalous
events and foresee previously unknown unknowns may represent the difference between mission
success and mission failure.
The Digital Twin* paradigm is a long-term vision aimed at addressing these and othershortcomings of current practices for certification, fleet management and sustainment. This
paper focuses on a discussion of the requirements for, development of and application of theDigital Twin. The paper is composed of the following sections: conventional approaches forcertification and sustainment; the concept of a Digital Twin and its applications, including
certification and sustainment, vehicle health and mission management, and in-situ forensics;
identification of near-term opportunities for implementing elements of the Digital Twin
paradigm; a brief discussion of the advantages of the Digital Twin; its potential influence on
National goals; and a brief summary and references.
Conventional Approaches
Much of the philosophy and many of the guidelines used for certification, fleet management andsustainment of NASA and U.S. Air Force vehicles can be found in government and professionalsociety standards and handbooks. A small sampling of requirements for structures, materials andnon-destructive evaluation (NDE) is shown in Table 1. These documents include expansive*Originally conceived by the	
   Defense Sciences Office at DARPA

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  American Institute of Aeronautics and Astronautics	  Paper for the 53rd Structures, Structural Dynamics, and Materials Conference:Special Session on the Digital Twin

discussions of structural design and test, including factors-of-safety; fracture control; materials
and processing; NDE; effects of specific environments; detailed analysis of failure modes; andstandards for numerical models and analysis. Many of the documents used by NASA and the
U.S. Air Force are developed in-house, while others are derived from technical societies such as
the American Institute of Aeronautics and Astronautics (AIAA) and the American Society of
Mechanical Engineers (ASME). These documents are rooted in decades-old experience withlaboratory tests, protoflight tests and flight histories. In this section, some of the conventional
approaches to certification, fleet management and sustainment will be illustrated through thediscussion of examples taken from several of the documents, including: NASA-STD-5001A,
NASA-STD-5019 and NASA-HDBK-5010.
The criteria for determination of factors-of-safety for both deterministic and probabilistic
analyses are discussed in several of the standards listed in Table 1, including NASA-STD5001A1 that specifically addresses spaceflight hardware.	
   	
   As discussed in the standard, theaccepted practice for “verification” of launch vehicles is “the prototype approach in which aseparate, dedicated test structure, identical to the flight structure, is tested to demonstrate that thedesign meets the factor-of-safety requirements.” An alternative to the prototype approach is “theprotoflight approach wherein the flight structure is tested to levels somewhat above limit stress(or load) but below yield strength.” The latter approach requires a factor-of-safety for yield thatis higher than the factor-of-safety used for prototype testing. “No test” options are also allowed
for metallic structures; however, according to the standard “projects which propose to use the‘no-test’ approach generally must use larger factors of safety.” In all of these cases,“verification” depends on the ability to perform laboratory tests on a “dedicated test structure[that is] identical to the flight structure.” Additionally, the loading, boundary conditions,environment must also be representative of flight conditions.
Table 1: Sample of Publicly Available Standards and Handbooks

	
  

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Paper for the 53rd Structures, Structural Dynamics, and Materials Conference:Special Session on the Digital Twin

The minimum design and test factors-of-safety for metallic and composite structures outlined inNASA-STD-5001A1 are given in Tables 2 and 3. These factors vary widely depending on
circumstance and can be as large as 2.0 depending on the material and local structuralconfiguration. Because of the apparent scatter in material response under cyclic and creep
loading, the corresponding factors are even larger. According to NASA-STD-5001A1, a service
life factor of 4.0 is applied to “spaceflight structures made of well-characterized materials andwith sufficient load cycle data that accounts for all in-service environments,” while for“structures made of materials that are not well characterized or those that may have complex
failure modes, such as composite structures, an additional factor and testing may be required by
the assigned Technical Authority at the responsible NASA Center.”Table 2: Minimum Design and Test Factors for Metallic Structures1	
  Table 3: Minimum Design and Test Factors for Composite/Bonded Structures
	
  

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Fracture critical hardware requires the additional imposition of damage tolerance requirements(see, for example, NASA-STD-50192).
Here, specific requirements, exceptions andmethodologies for assessing metallic and composite pressure vessels, rotating machinery,structural components and other fracture critical hardware are discussed. The application ofcontinuum fracture mechanics analyses, including NASGRO fatigue crack growth analysis (seeJSC 22267B6), is the foundation of much of the codified fracture control methodology.
An extreme case of codified conservatism is discussed in NASA-HDBK-501010, where separate
conservatisms for crack size, applied loads, material properties, and analysis are discussed for
	
  

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American Institute of Aeronautics and Astronautics	  Paper for the 53rd Structures, Structural Dynamics, and Materials Conference:Special Session on the Digital Twin“known cracks” in fracture critical hardware. Known cracks in fracture critical hardware require
special treatment wherein upper bounds for crack size estimate accounting for “any possible nondetected adjacent cracks and/or crack tip extensions” are required, as shown in Figure 1. Here,the crack size assumed for analysis (2cA) is a combination of the NDE-detected crack dimensions	
  Figure 1: Imposed Conservatism for Detected Cracks (Adapted from Reference 10).	
  (2cD) and an adjustment based on the NDE detection capability (2cN).
Additionally, worst-case limit loads for the mission must be used to compute the far field stressstate at the location of the detected crack. Typically, these loads are used in conjunction with anassumed fatigue spectrum. As an example, one of the many loading spectra developed by NASA
Goddard Space Flight Center for the analysis of payloads during launch and landing of the Space
Shuttle (STS) is shown in Table 4. The table shows the number of cycles during launch andlanding and the percentage of the total limit stress to be applied during each of these cycles.
Here, total limit stress is defined as the sum of the stresses due to low frequency, random andacoustic loading.
Table	  4:	  Launch	
  and	  Landing	  Spectrum	
  for	  STS	
  Payloads10	
  	
  

	
  

	
  

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American Institute of Aeronautics and Astronautics	  Paper for the 53rd Structures, Structural Dynamics, and Materials Conference:Special Session on the Digital Twin

The handbook requires analysts to use material properties corresponding to the lower bound of
fracture toughness and the upper bound of fatigue crack growth rate (da/dN vs. ΔK) as shown in
Figure 2. Upper bounds of fatigue crack growth rate curves are either visually estimated or
statistically estimated as being three standard deviations above the mean values. Finally,
analyses of known flaws must show that the component has a service life factor of four and asafety factor of 1.5 against fracture.

	
  Figure 2: Crack Growth Rate for Detected Cracks10
The codified practices that are advocated and mandated by these many standards and handbooks
have been carefully developed over several decades and have certainly stood the test of time.
They are invaluable tools for use by the present-day engineer for assessment of vehicle structure.
However, they tend to be reactive rather than proactive and are often based on heuristic
experience, worst-case scenarios and fleet leaders rather than on the specific material, structuralconfiguration and usage of an individual vehicle. Moreover, they may not be sufficient toaddress the future extreme requirements of some future NASA and U.S. Air Force vehicles.
The Digital Twin
Concept
Future generations of aerospace vehicles will require lighter mass while being subjected tohigher loads and more extreme service conditions over longer time periods than the present
generation of vehicles. As a result, demands on structural materials will be greatly increased andstructural margins will necessarily be decreased. Since extreme thermal, mechanical, andacoustical loadings may be impossible to reproduce in a laboratory at anything more than thecomponent scale, the identification and quantification of limit states via computational
simulation is needed.
	
  

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American Institute of Aeronautics and Astronautics	  Paper for the 53rd Structures, Structural Dynamics, and Materials Conference:Special Session on the Digital Twin

Because the vehicles are likely to encounter conditions that cannot be foreseen, revolutionary
approaches to verification and validation of the models, simulations and systems must also be
developed. Additionally, the ability to modify and evaluate the consequences of modification to
mission parameters in near-real time will be required. Moreover, the consequences of failure
during a long-duration space mission, where the vehicle is far from home, will almost certainly
be catastrophic.
Future generations of vehicles will rely on increasingly complex, heterogeneous andmultifunctional material forms with increasingly complex failure modes. Thus, the extensive
legacy of historical flight information incorporated in the various standards and handbooks thatwere based on decades of aircraft and spacecraft design experience, will likely be insufficient toeither certify future extreme vehicles or to guarantee mission success. Additionally, theextensive physical testing that provided the confidence needed to assure the success of previous
missions has become increasingly expensive to perform. Thus, a complete and fundamental
understanding of physical processes related to degradation at the material, structural and systemlevel and throughout the vehicle’s life-cycle is needed to move beyond the past decades of
empirical and heuristic design rules that result in inefficiencies and unquantifiable reliability.
Complex missions, particularly those where external support is difficult or impossible, will
necessitate complete real-time management of complex materials, structures and systems thatwill ultimately lead to “self-aware” vehicles. The numerous resulting engineering challenges
will necessitate a shift from current empirical-based standard engineering practice to an
additional emphasis on cradle-to-grave sustainment and reliability that will include (1) new
multidisciplinary physics-based methods to ensure robust certification, and (2) new
multidisciplinary methodolgies to ensure life-cycle sustainability. If various best-physics (i.e.,the most accurate, physically realistic and robust) models can be integrated with one another andwith on-board sensor suites, they will form a basis for certification of vehicles by simulation andfor real-time, continuous, health management of those vehicles during their missions. They will
form the foundation of a Digital Twin15-18.
A Digital Twin is an integrated multiphysics, multiscale, probabilistic simulation of an as-built
vehicle or system that uses the best available physical models, sensor updates, fleet history, etc.,
to mirror the life of its corresponding flying twin. The Digital Twin is ultra-realistic and may
consider one or more important and interdependent vehicle systems, including airframe,
propulsion and energy storage, life support, avionics, thermal protection, etc. The extreme
requirements of the Digital Twin motivate the integration of design of materials andrevolutionary approaches for material processing. Manufacturing anomalies that may affect thevehicle are also explicitly considered, evaluated and monitored. In addition to the backbone ofhigh-fidelity physical models of the as-built structure, the Digital Twin integrates sensor datafrom the vehicle’s on-board integrated vehicle health management (IVHM) system, maintenancehistory and all available historical and fleet data obtained using data mining and text mining.
By combining all of this information, the Digital Twin continuously forecasts the health of thevehicle or system, the remaining useful life and the probability of mission success. The Digital
Twin can also predict system response to safety critical events and uncover previously unknown
	
  

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American Institute of Aeronautics and Astronautics	  Paper for the 53rd Structures, Structural Dynamics, and Materials Conference:Special Session on the Digital Twin

issues before they become critical by comparing predicted and actual responses. Finally, thesystems on board the Digital Twin are capable of mitigating damage or degradation by activating
self-healing mechanisms or by recommending changes in mission profile to decrease loadings
thereby increasing both the life span and the probability of mission success.
Attributes
A graphical representation of some of the attributes of a Digital Twin is shown in Figure 3. The
narrative of the figure proceeds by column from the left-top pane to the right-bottom pane, as
follows:	
  Figure 3: Graphical Representation of the Digital Twin Paradigm.
	  Left Top PaneThe Digital Twin incorporates precise models of the as-built configuration of a vehicle or component,
including material microstructure, defects, fabrication anomalies, etc. Determination of these parameters
requires characterization at multiple length scales in the range from less than a micron to meters. Once this
precise “baseline” is determined, the ultra-high fidelity physical models can be used to predict future statesof the vehicle. The montage illustrates the wide range of material types, physical length scales andstructural configurations that may be considered by the Digital Twin. From upper left to lower right are:failed fibers in a fiber-reinforced composite material, a finite element model of a space vehicle, a specimen
undergoing arc-jet testing, the BSTRA (ball strut tie rod assembly) joint in the Space Shuttle Orbiter main

	
  

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American Institute of Aeronautics and Astronautics	  Paper for the 53rd Structures, Structural Dynamics, and Materials Conference:Special Session on the Digital Twin
engine feedline flowliner, the microstructure of an advanced alloy containing a fatigue crack, a carbon
nanotube and a friction stir-welded aluminum skin-stiffened panel.
Left Middle PaneThe Digital Twin relies heavily on its on-board IVHM system to continuously monitor aerodynamic,
thermal, inertial and other loading in addition to measures of degradation in vehicle health andperformance. The montage illustrates representative IVHM systems and system outputs. From left to right
are: a measured strain field near a fatigue crack, a cyclic loading spectrum, an IVHM fiber optic strain
sensing system and a computer tomography image of delaminations (blue squares) in a laminatedcomposite material.
Left Bottom Pane
The backbone of the Digital Twin is a suite of ultra-high fidelity physical models of the vehicle and its
systems and structures of interest. These may include models of one or more important and interdependent
vehicle systems, including airframe, propulsion and energy storage, life support, avionics, thermal
protection, etc. The montage illustrates the wide range of physical phenomena, models and modelingoutputs that may be included in the Digital Twin. From upper left to lower right are: an illustration of an
atom modeled in a quantum mechanics or molecular dynamics simulation, a three-dimensional finite
element simulation of deformation and fracture in an aluminum alloy, a hypothetical computational fluid
dynamics simulation of a vehicle during atmospheric re-entry, prediction of the number of cycles needed tonucleate a fatigue crack (Nnuc), a finite element simulation of mode shapes in the Space Shuttle Orbiter
main engine feedline flowliner, a multi-scale model of crack growth, and a simple model of radiationtransport through boron-nitride nanotubes as a neutron shielding material.
Center Top PaneThe Digital Twin uses its on-board IVHM system to update the physics-based models with sensor data andproduce continuously refined predictions of vehicle health and probability of mission success. It can also
perform option and impact investigation to answer questions regarding the impact of changes to mission
profile on all future conditions of the vehicle. The figure shows Bayesian updates of the probability of
failure as a function of time wherein sensor data is incorporated to increase accuracy and decrease
uncertainty in the predictions.
Center Pane
Each Digital Twin is uniquely developed for a particular vehicle and may be employed to “fly” future
missions during the design and certification process, continuously assess health during flight, perform insitu forensics of the effect of potential or real hazards and virtually evaluate the effect of potential changes
to the mission. A hypothetical vehicle is shown in the figure.
Center Bottom PaneThe Digital Twin integrates fleet data, maintenance reports and other historical information via data mining
and text mining to further inform simulations of the vehicle. The figure shows a notional representation of
the computational and data processing capabilities of the Digital Twin as represented by the PLEIADES
supercomputer at NASA Ames Research Center, a model of the human brain and streams of text and data
supporting the Digital Twin.
Right Top PaneThe Digital Twin is enabling to virtual digital certification and sustainment by replacing past decades of
empirically based design rules that result in structural inefficiencies and unquantifiable reliability withultra-high fidelity simulations, sensor systems and data that are immediately relevant to each uniquevehicle. The figure shows several of the many existing standards and handbooks that may be replaced by
the Digital Twin.
Right Middle PaneThe Digital Twin mirrors the actual flight of the flying twin by using sensor updates to continuously assess
vehicle health including determining remaining life of structural components and systems, estimating
probability of mission success and evaluating in-situ repair (self-healing), in-flight mitigation or load

	
  

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American Institute of Aeronautics and Astronautics	  Paper for the 53rd Structures, Structural Dynamics, and Materials Conference:Special Session on the Digital Twin
alleviation strategies and other mission modifications. The effects of modifications of mission parameters
are evaluated and previously unforeseen consequences are evaluated. The figure shows estimates ofremaining useful life (RUL) as a function of the number of load cycles.
Right Bottom PaneThe Digital Twin also uses its assessment of historical norms, fabrication/manufacturing reports andmaintenance data (where available) combined with Bayesian updates to high-fidelity models to perform insitu forensics, including anomaly detection/diagnosis and fault tree analysis. The figure shows a schematicof a fault tree associated with the hypothetical failure of a sensor.

Identification of Near-Term Opportunities
Many of the technologies critical for the Digital Twin are currently being developed underrelated visions for multi-scale and multi-physics modeling, structural health management, highperformance computing and others. Unlike those individual visions, the vision for the Digital
Twin integrates the broad range of technologies with a singular focus. However, such an
ambitious concept is unlikely to attain maturity for many decades, so a series of intermediate
steps is required. Among these steps is the Air Force Research Laboratory (AFRL) Digital
Twin: Spiral 117, a plan to integrate existing state-of-the-art technologies, benchmark current
capabilities and identify gaps using components from an existing U.S. Air Force vehicle,
specifically, the F-15, as a testbed. Another approach, being considered by NASA, is to focus ona small highly critical non-redundant component such as a microelectromechanical system(MEMS) that has characteristic length scales accessible with only the highest fidelity simulation
methods and health management tools available. By exercising such ultra-high fidelity
approaches on flight hardware, the shortcomings of the existing state-of-the-art in multiscalemodeling, in-situ experimentation and material state awareness will be highlighted.
Advantages Over Conventional Approaches
As a virtual instantiation of a flying vehicle, the Digital Twin is expected to be able to experience
every event that its flying twin experiences. Because of its ability to mirror the life of a specificvehicle in an as-built state, the Digital Twin will necessarily revolutionize certification, fleet
management and sustainment. It will also decrease system weight by reducing reliance on
statistical distributions of material properties, heuristic design philosophies, physical testing andassumed similitude between testing and operational conditions. Once the vehicle is launched,
the Digital Twin will increase the reliability of the flying vehicle because of its ability tocontinuously monitor and mitigate degradation and anomalous events. Additionally, it will
enable mission managers to make knowledgeable decisions regarding the consequences ofpossible in-flight changes to a vehicle’s mission.
Influence on National Goals
Although the Digital Twin represents a revolution in the paradigms used for certification, fleet
management and sustainment in support of extreme flight missions, it has the ability to influenceother National priorities and goals. In addition to its prominence in the recently-published
	
  

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American Institute of Aeronautics and Astronautics	  Paper for the 53rd Structures, Structural Dynamics, and Materials Conference:Special Session on the Digital Twin

roadmaps of NASA’s Office of Chief Technologist (OCT)15-16 and plans of the Air Force
Research Laboratory17-18, the Digital Twin paradigm can provide focus to the recent advocacy of
the National Science Foundation (NSF) and the National Research Council (NRC) in the areas ofSimulation Based Engineering Science (SBES)19, Integrated Computational Materials
Engineering (ICME)20 and Materials State Awareness21.
Many of the concepts discussed in the NSF’s recommendations for SBES are pervasive
throughout the themes of the Digital Twin, including, multiscale modeling of materials, dynamic
simulation of sensors, and verification and validation. Because the vehicles needed for future
extreme missions are unlikely to be developed using existing material forms, the design anddevelopment of new enabling (e.g., multifunctional, nanostructured, ultra-durable) materials is
required and is well aligned with the NRC’s ICME roadmap. Additionally, an understanding of
the exact state of the structure of the vehicle or its systems, including the existence of defects at
the microstructural and, possibly, the atomistic scale, is required as a baseline for ultra-highfidelity simulations. The ability to assess the evolution of defects is a critical element within the
Digital Twin’s on-board IVHM system and is well aligned with the NRC’s advocacy fordevelopments in Materials State Awareness.
Figure 4 is taken from the executive summary of the NASA OCT roadmap for technology area
12 (TA 12)16 and proposes a broad spectrum of future NASA technologies that can be focused by
the Digital Twin. The headings in the ellipses within Figure 4 represent the numbers and titles ofvarious sections in the OCT TA 12 roadmap where the technologies are discussed.
Potential spin-offs from the development of the Digital Twin span the breadth of the Nation’s
industrial and manufacturing sectors, infrastructure and development of nanotechnology. By
replacing conventional engineering practices with an understanding of the degradation of anindividual vehicle or vehicle system, the paradigm of the Digital Twin will impact the way thatphysical items ranging from micro-devices to civil infrastructure are developed, certified andmaintained.

	
  

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American Institute of Aeronautics and Astronautics	  Paper for the 53rd Structures, Structural Dynamics, and Materials Conference:Special Session on the Digital Twin	
  Figure 4: Integration of Technologies within a Digital Twin

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Summary
Future generations of aerospace vehicles will require lighter mass while being subjected tohigher loads and more extreme service conditions over longer time periods than the present
generation of vehicles. As a result, demands on structural materials will be greatly increased andstructural margins will necessarily be decreased. Additionally, the requirements placed on othersystems and subsystems will be greater than those previously experienced even though demands
for long-term reliability will increase. Because extreme thermal, mechanical, and acousticalloadings may be impossible to reproduce in a laboratory at anything more than the component
scale, the identification and quantification of limit states via computational simulation is needed.
Additionally, the vehicles are likely to encounter conditions that cannot be foreseen. Thus, theability to modify and evaluate the modification of mission parameters in near-real time will be
required.
Current approaches to certification, fleet management and sustainment of NASA and U.S. Air
Force vehicles are largely based on statistical distributions of material properties, heuristic
design philosophies, physical testing and assumed similitude between testing and operational
conditions and will likely be unable to address future extreme requirements. Some of the current
approaches to certification, fleet management and sustainment were illustrated through the
	
  

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American Institute of Aeronautics and Astronautics	  Paper for the 53rd Structures, Structural Dynamics, and Materials Conference:Special Session on the Digital Twin

discussion of examples taken from several documents, including: NASA-STD-5001A, NASASTD-5019 and NASA-HDBK-5010.
To address the shortcomings of conventional approaches, the paradigm of a Digital Twin waspresented. The Digital Twin is an integrated multiphysics, multiscale, probabilistic simulation ofan as-built vehicle or system that uses the best available physical models, sensor updates, fleet
history, etc., to mirror the life of its corresponding flying twin. The Digital Twin is ultra-realistic
and may consider one or more important and interdependent vehicle systems. The Digital Twin
integrates sensor data from the vehicle’s on-board IVHM system, maintenance history and allavailable historical and fleet data. By combining this information, the Digital Twin continuously
forecasts the health of the vehicle or system, the remaining useful life and the probability of
mission success. The systems on board the Digital Twin are also capable of mitigating damage
or degradation by activating self-healing mechanisms or by recommending changes in mission
profile to decrease loadings thereby increasing both the life span and the probability of mission
success.
Near-term opportunities for early development of the Digital Twin paradigm at AFRL andNASA were discussed and include the integration of various existing state-of-the-art
technologies, benchmarking of current capabilities and identification of gaps using critical flightcomponents as testbeds. Advantages of the Digital Twin, its potential influence on far reaching
NASA, U.S. Air Force goals and broader National goals were also discussed.
References
1Anon., “Structural Design and Test Factors of Safety for Spaceflight Hardware,” NASA-STD5001A, August 2008.
2

Anon., “Fracture Control Requirements for Spaceflight Hardware,” NASA-STD-5019, January
2008.
3

Anon., “Standard Materials and Process Requirements for Spacecraft,” NASA-STD-6016, July
2008.
4

Anon., “Non-Destructive Evaluation Requirements for Fracture Control Programs,” NASASTD-5009, April 2008.
5

Anon., “Guidelines for the Selection of Metallic Materials for Stress Corrosion Cracking
Resistance in Sodium Chloride Environments,” MSFC-STD-3029, May 2000.
6

Anon., “Fatigue Crack Growth Computer Program NASGRO,” Version 3.0, JSC 22267B,
November 2001.
7

Anon., “Strength and Life Assessment Requirements for Liquid Fueled Space Propulsion
System Engines,” NASA-STD-5012, June 2006.
8

Anon., “Standard for Models and Simulations,” NASA-STD-7009, July 2008.

9

Anon., “Fracture Control Requirements for Payloads Using the Space Shuttle,” NASA-STD5003, October 1996.

	
  

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American Institute of Aeronautics and Astronautics	  Paper for the 53rd Structures, Structural Dynamics, and Materials Conference:Special Session on the Digital Twin
10

Anon., “Fracture Control Implementation Handbook for Payloads, Experiments, and Similar
Hardware,” NASA-STD-5010, May 2005.
11

Anon., “Fracture Control Requirements, Nondestructive for Aircraft and Missile Materials andParts,” Mil-HDBK-6870A, August 2001.
12

Anon., “Metallic Materials Properties Development and Standardization,” DOT/FAA/ARMMPDS-03, 2007.
13

Anon., “Space Systems – Composite Overwrapped Pressure Vessels,” ANSI/AIAA S-081,
December 2000.
14

Anon., “Rules for Construction of Pressure Vessels,” ASME Boiler and Pressure Vessel Code,
Section VIII, September 2004.
15

Shafto, M., Conroy, M., Doyle, R., Glaessgen, E., Kemp, C., LeMoigne, J. and Wang, L.,“Technology Area 11: Modeling, Simulation, Information Technology and Processing
Roadmap,” NASA Office of Chief Technologist, November, 2010.
16

Piascik, R., Vickers, J., Lowry, D., Scotti, S., Stewart, J. and Calomino, A., “Technology Area
12: Materials, Structures, Mechanical Systems, and Manufacturing Roadmap,” NASA Office ofChief Technologist, November, 2010.
17

Kobryn, P.A. and Tuegel, E.J., “Condition-based Maintenance Plus Structural Integrity(CBM+SI) & the Airframe Digital Twin,” USAF Air Force Research Laboratory, 88ABW201101428, March 2011.
18

Tuegel, E.J., Ingraffea, A.R., Eason, T.J. and Spottswood, S.M., “Reengineering Aircraft
Structural Life Prediction Using a Digital Twin,” International Journal of Aerospace
Engineering, doi:10.1155/2011/154798, 2011.
19

Anon., “Simulation-Based Engineering Science: Revolutionizing Engineering Science throughSimulation,” National Science Foundation, May 2006.
20

Anon., “Integrated Computational Materials Engineering: A Transformational Discipline for
Improved Competitiveness and National Security,” National Research Council, ISBN: 0-30912000-4, 2008.
21

Anon., “Proceedings of a Workshop on Materials State Awareness,” National ResearchCouncil, ISBN: 0-309-12166-3, 2008.

	
  

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