April 2014 

NASA/TM–2014-218257 

Coupling Damage-Sensing Particles to the 

Digitial Twin Concept 

Jacob D. Hochhalter, William P. Leser, John A. Newman, 

and Edward H. Glaessgen 

Langley Research Center, Hampton, Virginia 

Vipul K. Gupta and Vesselin Yamakov 

National Institute of Aerospace, Hampton, Virginia 

Stephen R. Cornell 

Texas A&M University, College Station, Texas 

Scott A. Willard 

Science and Technology Corporation, Hampton, Virginia 

Gerd Heber 

The HDF Group, Champaign, Illinois 
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April 2014 

NASA/TM–2014-218257 

Coupling Damage-Sensing Particles to the 

Digitial Twin Concept 

.

Jacob D. Hochhalter, William P. Leser, John A. Newman, 

and Edward H. Glaessgen 

Langley Research Center, Hampton, Virginia 

Vipul K. Gupta and Vesselin Yamakov 

National Institute of Aerospace, Hampton, Virginia 

Stephen R. Cornell 

Texas A&M University, College Station, Texas 

Scott A. Willard 

Science and Technology Corporation, Hampton, Virginia 

Gerd Heber 

The HDF Group, Champaign, Illinois 
Available from: 

NASA Center for AeroSpace Information 

7115 Standard Drive 

Hanover, MD 21076-1320 

443-757-5802 

Acknowledgments 

The work discussed in this report was enabled by the Aeronautics Research Mission 

Directorate (ARMD) Seedling Fund, which invests in early stage concepts. 

Computational resources supporting this work were provided by the NASA High-End 

Computing (HEC) Program through the NASA Advanced Supercomputing (NAS) 

Division at Ames Research Center. Professors Darren Hartl and Ibrahim Karaman of 

Texas A&M University provided significant support throughout this work: Prof. Hartl 

provided the material models used in the presented simulations and Prof. Karaman 

provided consultation on sensory alloy fabrication. One author (Stephen Cornell) 

contributed to this effort while funded by the Langley Aerospace Research Summer 

Scholars Program (LARSS). 
1 

Abstract 

The research presented herein is a first step toward integrating two emerging structural 

health management paradigms: digital twin and sensory materials. Digital twin is a life 

management and certification paradigm whereby models and simulations consist of as-built 

vehicle state, as-experienced loads and environments, and other vehicle-specific history to 

enable high-fidelity modeling of individual aerospace vehicles throughout their service lives. 

The digital twin concept spans many disciplines, and an extensive study on the full domain is 

out of the scope of this study. Therefore, as it pertains to the digital twin, this research focused 

on one major concept: modeling specifically the as-manufactured geometry of a component 

and its microstructure (to the degree possible). 

The second aspect of this research was to develop the concept of sensory materials such 

that they can be employed within the digital twin framework. Sensory materials are shapememory alloys that undergo an audible phase transformation while experiencing sufficient 

strain. Upon embedding sensory materials with a structural alloy, this audible transformation 

helps improve the reliability of crack detection especially at the early stages of crack growth. By 

combining these two early-stage technologies, an automated approach to evidence-based 

inspection and maintenance of aerospace vehicles is sought. 

 Introduction 

It is expected that concept vehicles that will enable future missions will: be designed for 

service conditions that are not repeatable in a lab; experience loads and environments that are 

not foreseen during the design phase; and require extensive empirical data for the 

multifunctional next-generation materials. Each of these constitutes significant sources of 

uncertainty in safety and reliability of vehicles and missions. Furthermore, novel materials and 

structures add to the uncertainty in our design and management methods due to the lack of 

experience that comes with charting new territory. 

Current structural life-management approaches are typically based on empirically-derived 

estimates of a worst-case scenario. Relying on worst-case scenarios derived from testing 

implies that in-service loading conditions are well understood; tails of material behavior 

distributions are accurately modeled; and coupled damage modes that lead to reduced life are 

identified. Unfortunately, actual in-service loading conditions and material behavior are not 

recorded and no validation of these assumptions can currently be made. Thus, uncertainty 

estimates are applied in an attempt to gauge the range of potential outcomes as dependent on 

errors in the assumptions. 

Heavy reliance on empirical data and worst-case scenarios to quantify and handle 

uncertainty comes at the cost of over design, extensive test programs, overly-conservative 

inspection, and unnecessarily frequent part replacement [1,2]. These inefficiencies not only 

delay the implementation of novel materials and structural designs into NASA missions, but also 

maintain unnecessarily high maintenance costs for the vehicles currently in use. Therefore, 

improvements to current design and management approaches should focus on methods that 

help mitigate uncertainty and heavy reliance on empirical data. 

Digital twin is an emerging concept which employs modeling and simulation of the as-built 

vehicle state, as-experienced loads and environments, and other vehicle-specific history to 

enable high-fidelity modeling of individual vehicles throughout their service lives [3,4]. Using this 
2 

method, each as-manufactured aircraft (or critical components) will be digitally replicated, then 

managed based on the data gathered from on-board sensors and damage progression 

simulations. The digital twin method, therefore, will preclude the issues related to assuming a 

worst-case scenario. Furthermore, a close coupling between diagnosis from nondestructive 

evaluation (NDE) and continual prognosis from updated simulations will provide better estimates 

for when inspections should occur and improve reliability for each vehicle individually. 

Essentially, digital twin aims to reduce uncertainty by incorporating into available models as 

much initial and in-service information as possible. 

To provide a real-time monitoring capability, sensory particles can be embedded within 

structural components, see Figure 1. Sensory particles are micron-sized segments of a shape 

memory alloy that, when embedded in a material, can undergo a magnetic and audible phase 

transformation upon reaching a critical transformation strain. During transformation, sensory 

particles produce a characteristic acoustic signal when in the presence of a crack, which are 

then detected and used to triangulate crack location during flight. During transformation the 

material deforms by rapid twinning [5] generating an acoustic emission (AE) signal that is much 

stronger than the emission from crack growth in common structural alloys.

Figure 1: Schematic of embedded sensory particles near a crack. 

Existing nondestructive evaluation (NDE) methods are typically limited by the physical 

response of the inspected material. As a result, existing methods do not reliably detect 

microstructurally-small cracks or localized plastic deformation [6,7]. Early detection of damage 

is critical since it reduces uncertainty for components where test data and validated predictive 

tools are lacking and will extend the manageable life of each vehicle. Since sensory particles 

can be placed throughout the material, at small length scales, damage detection can be made 

much earlier than with existing NDE technologies alone. 

Combining the digital twin concept with the sensory particles technology addresses two 

main shortcomings in the way aerospace vehicles are currently designed and managed. First, 

digital twin will provide an individualized approach to inspection, repair, and replacement; 

ensuring functional parts will not be retired earlier than necessary. Second, automated 

monitoring will enable detection of unforeseen damage initiation in real-time and provide the 

digital twin with updates of actual usage and damage states. Such preventative and 

individualized condition-based inspection intervals provided by the proposed methodology will 

result in significant cost reduction by minimizing unnecessary inspections and early retirement 
3 

of parts. Furthermore, in most NASA missions, the ability to inspect vehicle components after 

going into service is an extremely difficult task and impossible in some long-duration flight 

cases. These two new techniques could direct an autonomous healing technology in the cases 

where repair is necessary. This process would not only improve the efficiency of current 

materials and practices, but permit faster implementation of novel, more efficient materials and 

structural designs into future NASA missions. 

The purpose of this initial research was to: define and develop digital twin through a simple 

use case; verify acoustic emission detection from a shape memory material during 

transformation; embed sensory particles in a commonly-used aluminum alloy in aerospace 

applications and assess near-crack-tip phase transformation; and generate a digital twin model 

of a fatigue test specimen with sensory particles and simulate the observed phase 

transformation. Each of these topics is discussed in sequence below. 

A Simple Digital Twin Example 

The digital twin concept spans many disciplines, and an extensive study on the full domain 

is out of the scope of this study. Therefore, as it pertains to the digital twin, this research 

focused on one major concept: modeling specifically the as-manufactured geometry of a 

component. A simple, non-standard material test specimen which failed along one of two 

different likely crack paths was considered, as posed in a National fracture challenge problem 

by Sandia National Laboratories [8]. Small deviations in geometry resulted in crack path 

ambiguity, motivating the consideration of as-built geometry in the prediction of component 

behavior. The material of interest was an off-the-shelf precipitation hardened stainless steel. 

Thirteen challenge specimens were machined with the nominal specimen geometry shown in 

Figure 2. The posed question of interest to this seedling research was: Through which holes 

(B, C, and D) does the crack navigate on its way to the back edge, E? 

Figure 2: Challenge specimen configuration, thickness = 3.18 mm (to scale) [8].

First, using a calibrated damage plasticity model, blind crack path predictions were provided, 

without knowledge of any as-built geometry. After a sensitivity study of specimen geometry, it 

was determined from simulation (using Abaqus [9]) that the crack path would consistently be AC-E. However, two crack paths were observed in the physical testing of the challenge 
4 

specimens: A-C-E and A-D-C-E. Of the thirteen specimens, ten cracked along the path defined 

by A-D-C-E, while three cracked along A-C-E. Further investigation showed only one of the 

specimens was machined to-specifications and its crack path was A-C-E, consistent with the 

blind prediction; however, the others exhibited deviations up to twice the specified tolerance 

(0.05 mm). The precise location of C in relation to D was observed to be critical. 

This discrepancy between predicted and observed behavior is a result of the epistemic 

uncertainty in the blind prediction. In practice, the as-built geometry of structural components is 

not considered as predictions are based on an assumed (specified) nominal geometry. As a 

test of employing the digital twin concept, the simulations were run a second time, but with the 

as-built geometry of each of the challenge specimens. It was found that by considering the asbuilt geometry, instead of a single nominal geometry, the crack path was correctly predicted for 

each of the 13 specimens, rather than just 3 of the 13 specimens. A detailed account of these 

results is given in [10]. 

One of the major payoffs of the digital twin method has been identified: the reduction of 

epistemic and aleatoric uncertainty. Epistemic uncertainty, systematic variation of measurable 

variables, was shown to be mitigated through this challenge problem. Aleatoric uncertainty, 

statistical variation from unknown variables that cannot be suppressed by more accurate 

measurement, was reduced by updating the material model to account for damage modes not 

observed in the calibration specimens, but affected the challenge specimens that were not 

machined within the specified tolerance. Although this example addressed the efficacy of the 

digital twin as a beneficial concept, further developments are required to incorporate Bayesian 

updating aspects, a critical aspect of digital twin. 

Sensory Alloy Testing 

Acoustic Emission Sensing 

A schematic of the AE system used during experiment is shown in Figure 3. AE events were 

captured using Digital Wave B-1025 piezo sensors. The captured signals were pre-amplified by 

20 db before being routed to a Digital Wave FM-1 digitizer/signal conditioner where further 

amplification and/or filtering could be applied as needed before the signals were digitized and 

recorded using the commercial software Wave Explorer [11]. All signals were recorded at a 

sampling rate of 10 MHz. 

Figure 3: Schematic of the data acquisition system used to capture and record AE events. 
5 

Bulk Sensory Alloy Experiment 

A proof-of-concept of the acoustic emission process was conducted using a Nickel-Titanium 

(Ni-Ti) tensile specimen. Ni-Ti was chosen as the proof-of-concept material since it is readily 

available, relatively cheap, and much is already known about its behavior. Tensile tests on Ni-Ti 

were conducted using an Instron servo-hydraulic machine as shown in Figure 4. Two AE 

sensors were attached at either end of the gage section in order to allow for one-dimensional 

location to be calculated for acquired AE events. Strain in the sample was monitored with a one 

inch extensometer as well as with digital image correlation (DIC). DIC was performed using a 

speckle pattern created by airbrushing the surface of the specimen with a coat of black paint, 

followed by a dusting of white paint to create a pattern as shown in Figure 4. This pattern was 

then monitored via a stereo set of cameras, calibrated and focused before testing. As the test 

progresses, the cameras continuously take images. The images are then correlated using the 

commercially available software VIC3D [12]. Deformations in the painted pattern are tracked 

from image to image creating a full field strain map for the painted area of the specimen. Figure 

5 illustrates the measured strain field with the location of the detected AE events superimposed. 

The location of recorded events tracks the movement of the transformation front. 

Figure 4: Illustration of the Ni-Ti tensile specimen with measurement devices annotated. 

The amplitude of the acoustic emission was determined to be approximately 10 times 

greater than that of cracking in common aluminum alloys, significantly increasing the likelihood 

of crack detection. Although these bursts were detected, and occurred at the expected load 

levels, the AE energy should be increased to enhance signal detection. This can be achieved by 

using a different material composition for the sensory particles. Compositions that are expected 

to release more acoustic energy and provide other benefits are being identified for subsequent 

testing. 
6 

Figure 5: Measured principal strain fields at 4 load levels (increasing from left-to-right) and 

triangulated acoustic event locations (white boxes). 

Embedded Sensory Particles Experiment 

Since testing of the bulk specimens proved that transformation could be audibly detected, 

the next step in development was to test if transformation of embedded particles could also be 

detected. To this end, a specimen with embedded sensory particles was fabricated at NASA 

Langley Research Center (LaRC) utilizing vacuum hot press (VHP). Gas atomized shape 

memory alloy (SMA) particles were hot-pressed between two plates of aluminum alloy (AA) 

7050 at 490°C. When cut orthogonal to the plane of the particles, the resulting specimens 

contained Ni-Ti surface particles along their centerline. These single edge notch (SEN) 

specimens were then cyclically loaded until a fatigue crack initiated and approached the 

particles. The specimen was then removed and prepared for strain mapping using in-situ DIC 

within a scanning electron microscope (SEM), Figure 6. Gupta et al. [13] describes in detail the 

speckle pattern application process and resolution. 

Assessing Transformation of Embedded Particles 

Once a crack had propagated up to a sensory particle, DIC was used to measure the strain 

during fatigue loading. These measurements quantified the strain within each embedded 

particle. By measuring the strain in and around each sensory particle, verification was made 

that the particles near the crack tip were transforming. By relating the measured particle strain 

to the applied load, a determination of the transformation strain could be made. This 

determination was readily made since there was a distinct change the load-strain slope, 

indicating a fundamental change in sensory material stiffness. The critical transformation strain 

was determined to be ~1%. Figure 7 illustrates the results of the DIC measurement (for the 

same sensory particles as in Figure 6), and shows that the sensory particles near the fatigue 

crack tip were nearly fully transformed under the applied load. In Figure 7, each point on the 

surface of the particle where strain was greater than 1% (i.e. purple contour fill) transformed. 
7 

 

Figure 6: Illustration of embedded sensory particles near a growing crack. The black and white 

dots are the DIC speckle pattern. 

 

Figure 7: Measured strain in the near-crack-tip sensory particles. Regions colored with 

purple indicate transformed material. 

Similar to the bulk tensile specimens discussed above, these embedded particle specimens 

also had the acoustic sensing system attached during loading. Acoustic signals were detected 

at the applied load values where the measured strain values indicated transformation was 

occurring. This provides a second verification that the particles transformed near the crack tip, 

and that they emit sufficient acoustic energy to be detected. For this configuration of crack size 

and its location with respect to neighboring sensory particles, transformation was detected to 

occur at a load level indicative of a severe overload cycle. Future work should include a 

reduction in the required transformation strain of the sensory particles, such that they could also 

detect cracks growing under high-cycle fatigue, where applied load amplitudes are reduced. 
8 

Modeling and Simulation of Embedded Particles 

Once each specimen was fabricated, with particles embedded, X-ray computed tomography 

(CT) was used to determine the 3D geometry of each embedded particle, see Figure 8. These 

data were then used to generate a finite element model: a digital twin with the as-built geometry 

was instantiated. The digital twin models generated from x-ray CT were too large to simulate on 

a desktop or with commercially-available software, thus high-performance computing and a 

custom code was required. An explicit finite element software was written to employ Pleiades (a 

NASA Ames supercomputer) to carry out the simulations. The software was written in 

collaboration with The HDF Group for this seedling research. Several verification problems 

were tested successfully. 

Figure 8: X-ray CT results showing embedded particles (gray) and the crack surface (blue). 

The loads applied during testing were then used during simulation of the digital twin FEM 

and the evolution of strain fields and transformation within the sensory particles were computed. 

Figure 9 illustrates the simulated strain fields, which are in good agreement with the test data of 

Figure 7. It is seen in both measurement and simulation that sensory particles initially transform 

along their boundaries with remaining regions transforming at higher load. A computational 

study which provides the optimal sensory particle composition should be undertaken next. 

Figure 9: Digital twin finite element model with computed strain field in both sensory particles 

shown in Figures 3 and 4. 
9 

Concluding Remarks 

The digital twin example presented herein is intended to be a simple first step to motivate 

adoption of the technique. The example presented simply shows that by incorporating the asfabricated geometry of a specimen, along with over-the-counter software, predictions of 

behavior while in-service can be significantly improved. It is expected that incorporating realtime sensing of damage and Bayesian updating, this methodology will become increasingly 

accurate and cost effective. One major question that should be answered through future 

research is: How can the loads be measured in service, which are necessary to inform the 

digital twin? 

The sensory alloy research illustrated several encouraging results. First, it was observed in 

the bulk tensile specimens of Ni-Ti that the location of transformation within the specimen could 

be triangulated using acoustic emission sensors. Second, Ni-Ti sensory particles were 

successfully embedded in a commonly-used aluminum alloy using standard fabrication 

techniques. Third, the transformation of the embedded sensory particles near a growing crack 

was detected using both strain measurements and acoustic emission sensing, which verified the 

overall concept. Finally, X-ray CT was used to replicate the as-build geometry of the specimen 

(including each embedded sensory particle), and subsequent finite element simulations 

successfully reproduced the observed strain fields at transformation. Future work for sensory 

particle development should include investigation of alternative sensory alloys and fabrication 

techniques. The next step for digital twin development should involve automating feedback of 

the acoustic emission events from the sensory particles to update its computational model. 

Much research is still required for combining the digital twin concept with the sensory 

particles technology to address any shortcomings in the way aerospace vehicles are currently 

designed and managed. However, the direction of combining a methodology such as digital 

twin, with real-time damage detection is important and recognized in NASA aeronautics. One of 

NASA aeronautics three main research goals is to “maintain or improve safety of aircraft in an 

increasingly complex system.” The importance of this goal is exemplified in the Aviation Safety 

Program (AvSP). The AvSP lists the top ten technical challenges, of which 3 are directly 

addressed by the digital twin and sensory particle research proposed above: discovery of 

precursors to safety incidents; prognostic algorithm design for safety assurance; and 

maintaining vehicle safety between major inspections. The digital twin method will provide a 

capability to predict and prevent safety issues by constantly monitoring usage and simulating 

possible future damage states. In addition to coping with weather, traffic congestion, and other 

terrestrial or airborne security concerns already addressed by AvSP research, the digital twin 

and sensory particles concepts will provide pilots and controllers the ability to modify flight 

parameters due to unforeseen issues with structural integrity before and during flight. 

References 

[1] Christodoulou L., Larsen J.M., “Using materials prognosis to maximize the utilization 

potential of complex mechanical systems,” JOM, 2004, vol. 56, pp. 15-19. 

[2] Lincoln J.W., “Damage-Tolerance – USAF Experience,” Proceedings of the 13th

Symposium of the Int. Comm. on Aeronautical Fatigue, Pisa Italy, 1985, pp. 1-31. 

[3] Glaessgen E.H., Stargel D.S., “The digital twin paradigm for future NASA and U.S. Air 

Force Vehicles,” 53rd Struct. Dyn. Mater. Conf. Special Session: Digital Twin, Honolulu, 

HI, U.S., 2012, pp. 1-14. 
10 

[4] Tuegel E.J., Ingraffea A.R., Eason T.G., Spottswood S.M., “Reengineering aircraft 

structural life prediction using a digital twin,” Int. J. Aerosp. Engr., 2011, pp. 1-14. 

[5] Pun G.P., Mishin Y., “Molecular dynamics simulation of the martensitic phase 

transformation in NiAl alloys,” 2010, J. of Phys.: Condensed Matter, vol. 22., pp. 1-7. 

[6] Lindgren E. A., Knopp J. S., Aldrin J. C., Steffes G. J., Buynak C. F., “Aging aircraft NDE: 

capabilities, challenges, and opportunities,” AFRL-ML-WP-TP-2006-497, 2006. 

[7] Duke J. C. Jr., Green R. E. Jr., “Simultaneous monitoring of acoustic emission and 

ultrasonic attenuation during fatigue of 7075 aluminum,” Int. J. Fat., 1979, vol. 1, pp. 125-

132 

[8] Boyce B.L., Kramer S.L.B., Fang H.E., Cordova T.E., Neilsen M.K., Dion K., et al. “The 

Sandia fracture challenge: blind round robin predicitons of ductile tearing,” Int. J. Fract., 

2013, pp. 1-64. 

[9] Simulia, “Abaqus 6.11 user manual,” Providence, RI, 2011. 

[10] Cerrone A., Hochhalter J.D., Heber G., Ingraffea A.R., “A usage case for digital twin,” 

2014, IJAE, submitted for review. 

[11] Digital Wave, http://www.digitalwavecorp.com, 2014. 

[12] Correlated Solutions, http://www.correlatedsolutions.com, 2014. 

[13] Gupta V.K., Willard S.A., Hochhalter J.D., Smith S.W., “Image correlation and EBSDbased study of crack tip and grain deformation in aluminum alloys,” 2014, ASTM Special 

Edition, submitted for review. 
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Coupling Damage-Sensing Particles to the Digital Twin Concept 

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Hochhalter, Jacob D.; Leser, William P.; Newman, John A.; Glaessgen, 

Edward H.; Gupta, Vipul K.; Yamakov, Vesselin I.; Cornell, Stephen R.; 

Willard, Scott A.; Heber, Gerd

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14. ABSTRACT

The research presented herein is a first step toward integrating two emerging structural health management paradigms: digital twin and sensory materials. 

Digital twin is an emerging life management and certification paradigm whereby models and simulations consist of as-built vehicle state, as-experienced loads 

and environments, and other vehicle-specific history to enable high-fidelity modeling of individual aerospace vehicles throughout their service lives. The 

digital twin concept spans many disciplines, and an extensive study on the full domain is out of the scope of this study. Therefore, as it pertains to the digital 

twin, this research focused on one major concept: modeling specifically the as-manufactured geometry of a component and its microstructure (to the degree 

possible). The second aspect of this research was to develop the concept of sensory materials such that they can be employed within the digital twin 

framework. Sensory materials are shape-memory alloys that undergo an audible phase transformation while experiencing sufficient strain. Upon embedding 

sensory materials with a structural alloy, this audible transformation helps improve the reliability of crack detection especially at the early stages of crack 

growth. By combining these two early-stage technologies, an automated approach to evidence-based inspection and maintenance of aerospace vehicles is 

sought. 

15. SUBJECT TERMS

Aerospace vehicles; Cracks; Digital twin; Fatigue; Sensory materials; Shape memory alloys

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