Reengineering aircraft structural life prediction using a digital twin
Open accessAbstract
Reengineering of the aircraft structural life prediction process to fully exploit advances in very high performance digital computing is proposed. The proposed process utilizes an ultrahigh fidelity model of individual aircraft by tail number, a Digital Twin, to integrate computation of structural deflections and temperatures in response to flight conditions, with resulting local damage and material state evolution. A conceptual model of how the Digital Twin can be used for predicting the life of aircraft structure and assuring its structural integrity is presented. The technical challenges to developing and deploying a Digital Twin are discussed in detail.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The US Air Force Research Laboratory adopted DT technology for aircraft design, maintenance, and prediction.
“Subsequently, following NASA's footsteps, the US Air Force Research Laboratory (AFRL) adopted DT technology for aircraft design, maintenance, and prediction [20].”
From Digital Twins in the IIoT: Current Practices and Future Directions Toward Industry 5.0 (2025) · cites this paper as [20]
Section 5, “Developing the Digital Twin” (page unavailable), says AFRL was investigating a component for structural life prediction and planned an initial, low-fidelity Digital Twin. Section 2 presents aircraft design, maintenance, and life forecasting as hypothetical capabilities. The paper does not establish that AFRL had adopted Digital Twin technology for all three uses or that it did so following NASA. (LLM, conf=0.88)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
The stochastic finite element method: Past, present and future
Stefanou, G. (2008) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2008.11.007 · Cited by 1,006 · see citing works
Partitioned analysis of coupled mechanical systems
Felippa, C. A., Park, K. C., & Farhat, C. (2001) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/s0045-7825(00)00391-1 · Cited by 822 · see citing works
Generalized finite element methods for three-dimensional structural mechanics problems
Duarte, C. A., Babuska, I. M., & Oden, J. T. (2000) · Computers & Structures · doi:10.1016/s0045-7949(99)00211-4 · Cited by 652 · see citing works
Studies on Fluid-Thermal-Structural Coupling for Aerothermoelasticity in Hypersonic Flow
Culler, A. J., & McNamara, J. J. (2010) · AIAA Journal · doi:10.2514/1.j050193 · Cited by 250 · see citing works
Duarte, C. A., & Kim, D. J. (2007) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2007.08.017 · Cited by 215 · see citing works
Nonlinear modal models for sonic fatigue response prediction: a comparison of methods
Hollkamp, J. J., Gordon, R. W., & Spottswood, S. M. (2004) · Journal of Sound and Vibration · doi:10.1016/j.jsv.2004.08.036 · Cited by 190 · see citing works
A general theory of finite elements. II. Applications
Oden, J. T. (1969) · International Journal for Numerical Methods in Engineering · doi:10.1002/nme.1620010304 · Cited by 189 · see citing works
SUGGAR: A General Capability for Moving Body Overset Grid Assembly
Noack, R. W. (2005) · AIAA Computational Fluid Dynamics Conference · doi:10.2514/6.2005-5117 · Cited by 167 · see citing works
Some challenges of realistic flow simulations by unstructured grid CFD
Nakahashi, K., Ito, Y., & Togashi, F. (2003) · International Journal for Numerical Methods in Fluids · doi:10.1002/fld.559 · Cited by 154 · see citing works
Research directions in computational mechanics
Oden, J. T., Belytschko, T., Babuska, I. M., & Hughes, T. J. R. (2003) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/s0045-7825(02)00616-3 · Cited by 153 · see citing works
Solving thermal and phase change problems with the eXtended finite element method
Merle, R., & Dolbow, J. E. (2002) · Computational Mechanics · doi:10.1007/s00466-002-0298-y · Cited by 143 · see citing works
Kestrel -- A Fixed Wing Virtual Aircraft Product of the CREATE Program
Morton, S., Tillman, B., McDaniel, D. R., Sears, D. R., & Tuckey, T. R. (2009) · DoD High Performance Computing Modernization Program Users Group Conference (HPCMP-UGC) · doi:10.1109/hpcmp-ugc.2009.26 · Cited by 125 · see citing works
Stochastic Reduced Basis Methods
Nair, P. B., & Keane, A. J. (2002) · AIAA Journal · doi:10.2514/2.1837 · Cited by 119 · see citing works
O’Hara, P. J., Duarte, C. A., & Eason, T. G. (2009) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2008.12.024 · Cited by 103 · see citing works
Nouy, A., Clément, A., Schoefs, F., & Moës, N. (2008) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2008.06.010 · Cited by 99 · see citing works
A multiscale stochastic finite element method on elliptic problems involving uncertainties
Xu, F. F. (2007) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2007.02.002 · Cited by 99 · see citing works
Dynamic Snap-Through of Thin-Walled Structures by a Reduced-Order Method
Przekop, A., & Rizzi, S. A. (2007) · AIAA Journal · doi:10.2514/1.26351 · Cited by 93 · see citing works
Bozek, J. E., Hochhalter, J. D., Veilleux, M. G., Liu, M., Heber, G., Sintay, S. D., Rollett, A. D., Littlewood, D. J., Maniatty, A. M., Weiland, H., et al. (2008) · Modelling and Simulation in Materials Science and Engineering · doi:10.1088/0965-0393/16/6/065007 · Cited by 91 · see citing works
Investigation of incremental fatigue crack growth mechanisms using in situ SEM testing
Zhang, W., & Liu, Y. (2011) · International Journal of Fatigue · doi:10.1016/j.ijfatigue.2011.03.004 · Cited by 91 · see citing works
Acharjee, S., & Zabaras, N. J. (2006) · Computers & Structures · doi:10.1016/j.compstruc.2006.10.004 · Cited by 87 · see citing works
Combined interface boundary condition method for coupled thermal simulations
Roe, B., Jaiman, R. K., Haselbacher, A., & Geubelle, P. (2007) · International Journal for Numerical Methods in Fluids · doi:10.1002/fld.1637 · Cited by 86 · see citing works
Small time scale fatigue crack growth analysis
Lu, Z., & Liu, Y. (2010) · International Journal of Fatigue · doi:10.1016/j.ijfatigue.2010.01.010 · Cited by 84 · see citing works
Kestrel: A Fixed Wing Virtual Aircraft Product of the CREATE Program
Morton, S., McDaniel, D., Sears, D., Tillman, B., & Tuckey, T. R. (2009) · AIAA Aerospace Sciences Meeting · doi:10.2514/6.2009-338 · Cited by 82 · see citing works
Nonlinear Reduced Order Random Response Analysis of Structures with Shallow Curvature
Przekop, A., & Rizzi, S. A. (2006) · AIAA Journal · doi:10.2514/1.18868 · Cited by 80 · see citing works
Assessment of conservative load transfer for fluid-solid interface with non-matching meshes
Jaiman, R. K., Jiao, X., Geubelle, P. H., & Loth, E. (2005) · International Journal for Numerical Methods in Engineering · doi:10.1002/nme.1434 · Cited by 78 · see citing works
Miller, B. A., McNamara, J. J., Spottswood, S. M., & Culler, A. J. (2011) · Journal of Sound and Vibration · doi:10.1016/j.jsv.2011.06.028 · Cited by 77 · see citing works
A Parallelized Meshfree Method with Boundary Enrichment for Large-Scale CFD
Zhang, L. T., Wagner, G. J., & Liu, W. K. (2002) · Journal of Computational Physics · doi:10.1006/jcph.2002.6999 · Cited by 65 · see citing works
Transient analysis of sharp thermal gradients using coarse finite element meshes
O’Hara, P. J., Duarte, C. A., & Eason, T. G. (2010) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2010.10.005 · Cited by 62 · see citing works
Rabier, P. J. (1989) · International Journal of Engineering Science · doi:10.1016/0020-7225(89)90166-3 · Cited by 54 · see citing works
Unified Aeroelastic and Flight Dynamic Formulation via Rational Function Approximations
Baldelli, D. H., Chen, P. C., & Panza, J. (2006) · Journal of Aircraft · doi:10.2514/1.16620 · Cited by 54 · see citing works
Nonlinear Reduced Order Modeling of Curved Beams: A Comparison of Methods
Wang, X. Q., Mignolet, M. P., Eason, T. G., & Spottswood, S. M. (2009) · AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference (SDM) · doi:10.2514/6.2009-2433 · Cited by 49 · see citing works
Time derivative equations for mode I fatigue crack growth in metals
Pommier, S., & Risbet, M. (2005) · International Journal of Fatigue · doi:10.1016/j.ijfatigue.2005.06.034 · Cited by 43 · see citing works
Thompson, L. L., & Pinsky, P. M. (1996) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/0045-7825(95)00955-8 · Cited by 43 · see citing works
A Geometric Approach to Modeling Microstructurally Small Fatigue Crack Formation
Ingraffea, A. R., Christ, R. J., Littlewood, D. J., Maniatty, A. M., Bozek, J. E., Veilleux, M. G., & Hochhalter, J. D. (2010) · NASA STI Repository (National Aeronautics and Space Administration) · Cited by 29 · see citing works
Dynamic CFD Simulation of Aircraft Recovery to an Aircraft Carrier
Shipman, J. D., Arunajatesan, S., Cavallo, P. A., Sinha, N., & Polsky, S. A. (2008) · AIAA Applied Aerodynamics Conference · doi:10.2514/6.2008-6227 · Cited by 28 · see citing works
Studies on Fluid-Structural Coupling for Aerothermoelasticity in Hypersonic Flow
Culler, A. J., Crowell, A. R., & McNamara, J. J. (2009) · AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference (SDM) · doi:10.2514/6.2009-2364 · Cited by 27 · see citing works
Emery, J. M., Hochhalter, J. D., Wawrzynek, P. A., Heber, G., & Ingraffea, A. R. (2009) · Engineering Fracture Mechanics · doi:10.1016/j.engfracmech.2009.02.018 · Cited by 22 · see citing works
Remarks on uncertainty assessment and management in modeling and computation
Banks, H. T. (2001) · Mathematical and Computer Modelling · doi:10.1016/s0895-7177(00)00227-2 · Cited by 15 · see citing works
A Reduced Order Modeling Framework for Integrated Thermo-Elastic Analysis of Hypersonic Vehicles
Falkiewicz, N. J., & Cesnik, C. E. S. (2009) · AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference (SDM) · doi:10.2514/6.2009-2308 · Cited by 14 · see citing works
Chen, P., Baldelli, D. H., & Zeng, J. (2008) · AIAA Atmospheric Flight Mechanics Conference · doi:10.2514/6.2008-6376 · Cited by 11 · see citing works
Subcritical crack growth in ductile fracture with continuum damage mechanics
June, W., & Chow, C. L. (1989) · Engineering Fracture Mechanics · doi:10.1016/0013-7944(89)90032-5 · Cited by 10 · see citing works
Modeling of damage accumulation for Duffing-type systems under severe random excitations
Yang, B., Mignolet, M. P., & Spottswood, S. M. (2004) · Probabilistic Engineering Mechanics · doi:10.1016/j.probengmech.2003.11.015 · Cited by 9 · see citing works
Generalized finite element methods for three-dimensional crack growth simulations
Pereira, J. P. (2010) · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign) · Cited by 7 · see citing works
Garretson, D., Mair, H. U., Martin, C. B., Sullivan, K., & Teichman, J. A. (2005) · doi:10.21236/ada537587 · Cited by 5 · see citing works
Modeling and Characterization of Damage Processes in Metallic Materials
Glaessgen, E. H., Saether, E., Smith, S., Hochhalter, J. D., Yamakov, V. I., & Gupta, V. K. (2011) · AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference (SDM) · doi:10.2514/6.2011-2177 · Cited by 3 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- Integrated Computational Materials Engineering-A Transfor- mational Discipline for Improved Competitiveness and National Security, Committee on Integrated Computational Materi- als Engineering, National Research Council, The National Academies Press, Washington, DC, USA, 2008.
Cited by
Sorted by citations
High-performance medicine: the convergence of human and artificial intelligence
Topol, E. J. (2019) · Nature Medicine · doi:10.1038/s41591-018-0300-7 · Cited by 10,450 · see citing works
Digital Twin in Industry: State-of-the-Art
Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2873186 · Cited by 4,391 · see citing works
Digital Twin: Mitigating Unpredictable, Undesirable Emergent Behavior in Complex Systems
Grieves, M., & Vickers, J. (2017) · Transdisciplinary Perspectives on Complex Systems · doi:10.1007/978-3-319-38756-7_4 · Cited by 3,727 · see citing works
Digital twin-driven product design, manufacturing and service with big data
Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., & Sui, F. (2018) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-017-0233-1 · Cited by 2,920 · see citing works
The Digital Twin Paradigm for Future NASA and U.S. Air Force Vehicles
Glaessgen, E., & Stargel, D. (2012) · 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference<BR>20th AIAA/ASME/AHS Adaptive Structures Conference<BR>14th AIAA · doi:10.2514/6.2012-1818 · Cited by 2,404 · see citing works
Review of digital twin about concepts, technologies, and industrial applications
Liu, M., Fang, S., Dong, H., & Xu, C. (2020) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2020.06.017 · Cited by 1,965 · see citing works
Digital twin and big data towards smart manufacturing and Industry 4.0: 360 degree comparison
Qi, Q., & Tao, F. (2018) · IEEE Access · doi:10.1109/access.2018.2793265 · Cited by 1,716 · see citing works
Lu, Y., Liu, C., Wang, K. I., Huang, H., & Xu, X. W. (2019) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2019.101837 · Cited by 1,753 · see citing works
Enabling technologies and tools for digital twin
Qi, Q., Tao, F., Hu, T., Anwer, N., Liu, A., Wei, Y., Wang, L., & Nee, A. Y. C. (2021) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2019.10.001 · Cited by 1,579 · see citing works
A survey on digital twin: definitions, characteristics, applications, and design implications
Barricelli, B. R., Casiraghi, E., & Fogli, D. (2019) · IEEE Access · doi:10.1109/access.2019.2953499 · Cited by 1,480 · see citing works
Digital twin shop-floor: a new shop-floor paradigm towards smart manufacturing
Tao, F., & Zhang, M. (2017) · IEEE Access · doi:10.1109/access.2017.2756069 · Cited by 1,397 · see citing works
Towards a semantic Construction Digital Twin: Directions for future research
Boje, C., Guerriero, A., Kubicki, S., & Rezgui, Y. (2020) · Automation in Construction · doi:10.1016/j.autcon.2020.103179 · Cited by 1,477 · see citing works
Digital twin-driven product design framework
Tao, F., Sui, F.-Y., Liu, A., Qi, Q., Zhang, M., Song, B.-Y., Guo, Z.-R., Lu, S. C. Y., & Nee, A. (2019) · International Journal of Production Research · doi:10.1080/00207543.2018.1443229 · Cited by 1,085 · see citing works
Digital twin: origin to future
Singh, M., Fuenmayor, E., Hinchy, E., Qiao, Y., Murray, N., & Devine, D. (2021) · Applied System Innovation · doi:10.3390/asi4020036 · Cited by 1,038 · see citing works
Digital twin paradigm: A systematic literature review
Semeraro, C., Lezoche, M., Panetto, H., & Dassisti, M. (2021) · Computers in industry (Print) · doi:10.1016/j.compind.2021.103469 · Cited by 852 · see citing works
Digital Twin Networks: A Survey
Wu, Y., Zhang, K., & Zhang, Y. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3079510 · Cited by 847 · see citing works
Simulation in the design and operation of manufacturing systems: state of the art and new trends
Mourtzis, D. (2019) · International Journal of Production Research · doi:10.1080/00207543.2019.1636321 · Cited by 620 · see citing works
Zhuang, C., Liu, J., & Xiong, H. (2018) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-018-1617-6 · Cited by 700 · see citing works
Digital Twin as a Service (DTaaS) in Industry 4.0: An Architecture Reference Model
Aheleroff, S., Xu, X. W., Zhong, R. Y., & Lu, Y. (2020) · Advanced Engineering Informatics · doi:10.1016/j.aei.2020.101225 · Cited by 680 · see citing works
Toward a Digital Twin for real-time geometry assurance in individualized production
Söderberg, R., Wärmefjord, K., Carlson, J. S., & Lindkvist, L. (2017) · CIRP Annals · doi:10.1016/j.cirp.2017.04.038 · Cited by 543 · see citing works
Rathore, M. M., Shah, S. A., Shukla, D., Bentafat, E., & Bakiras, S. (2021) · IEEE Access · doi:10.1109/access.2021.3060863 · Cited by 616 · see citing works
An application framework of digital twin and its case study
Zheng, Y., Yang, S., & Cheng, H. (2018) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-018-0911-3 · Cited by 534 · see citing works
Digital Twins: State of the art theory and practice, challenges, and open research questions
Sharma, A., Kosasih, E., Zhang, J., Brintrup, A., & Calinescu, A. (2022) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2022.100383 · Cited by 587 · see citing works
Haag, S., & Anderl, R. (2018) · Manufacturing Letters · doi:10.1016/j.mfglet.2018.02.006 · Cited by 559 · see citing works
Digital Twin for rotating machinery fault diagnosis in smart manufacturing
Wang, J., Ye, L., Gao, R. X., Li, C., & Zhang, L. (2019) · International Journal of Production Research · doi:10.1080/00207543.2018.1552032 · Cited by 552 · see citing works
A hybrid predictive maintenance approach for CNC machine tool driven by Digital Twin
Luo, W., Hu, T., Ye, Y., Zhang, C., & Wei, Y. (2020) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2020.101974 · Cited by 525 · see citing works
A Digital-Twin-Assisted Fault Diagnosis Using Deep Transfer Learning
Xu, Y., Sun, Y., Liu, X., & Zheng, Y. (2019) · IEEE Access · doi:10.1109/access.2018.2890566 · Cited by 517 · see citing works
Digital twin and its implementations in the civil engineering sector
Jiang, F., Ma, L., Broyd, T., & Chen, K. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103838 · Cited by 531 · see citing works
Digital twin-driven manufacturing cyber-physical system for parallel controlling of smart workshop
Leng, J., Zhang, H., Yan, D., Liu, Q., Chen, X., & Zhang, D. (2019) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-018-0881-5 · Cited by 475 · see citing works
A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies
Thelen, A., Zhang, X., Fink, O., Lu, Y., Ghosh, S., Youn, B. D., Todd, M. D., Mahadevan, S., Hu, C. K., & Hu, Z. Y. (2022) · Structural and Multidisciplinary Optimization · doi:10.1007/s00158-022-03425-4 · Cited by 471 · see citing works
Machine Learning based Digital Twin Framework for Production Optimization in Petrochemical Industry
Min, Q., Lu, Y., Liu, Z., Su, C., & Wang, B. (2019) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.05.020 · Cited by 436 · see citing works
Zhang, H., Liu, Q., Chen, X., Zhang, D., & Leng, J. (2017) · IEEE Access · doi:10.1109/access.2017.2766453 · Cited by 471 · see citing works
Manesh, M. F., Pellegrini, M. M., Marzi, G., & Dabić, M. (2020) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2019.2963489 · Cited by 451 · see citing works
Lee, D., Lee, S. H., Masoud, N., Krishnan, M. S., & Li, V. C. (2021) · Automation in Construction · doi:10.1016/j.autcon.2021.103688 · Cited by 454 · see citing works
Mandolla, C., Petruzzelli, A. M., Percoco, G., & Urbinati, A. (2019) · Computers in Industry · doi:10.1016/j.compind.2019.04.011 · Cited by 422 · see citing works
Yu, W., Patros, P., Young, B., Klinac, E., & Walmsley, T. (2022) · Renewable & Sustainable Energy Reviews · doi:10.1016/j.rser.2022.112407 · Cited by 361 · see citing works
Fabris, M., Abbriano, R. M., Pernice, M., Sutherland, D. L., Commault, A. S., Hall, C. C., Labeeuw, L., McCauley, J. I., Kuzhiuparambil, U., Ray, P., et al. (2020) · Frontiers in Plant Science · doi:10.3389/fpls.2020.00279 · Cited by 382 · see citing works
Kamble, S., Gunasekaran, A., Parekh, H., Mani, V., Belhadi, A., & Sharma, R. (2021) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2021.121448 · Cited by 414 · see citing works
Digital twin-driven rapid individualised designing of automated flow-shop manufacturing system
Liu, Q., Zhang, H., Leng, J., & Chen, X. (2019) · International Journal of Production Research · doi:10.1080/00207543.2018.1471243 · Cited by 370 · see citing works
The role of data fusion in predictive maintenance using digital twin
Liu, Z., Meyendorf, N., & Mrad, N. (2018) · AIP conference proceedings · doi:10.1063/1.5031520 · Cited by 364 · see citing works
A probabilistic graphical model foundation for enabling predictive digital twins at scale
Kapteyn, M. G., Pretorius, J. V. R., & Willcox, K. E. (2021) · Nature Computational Science · doi:10.1038/s43588-021-00069-0 · Cited by 363 · see citing works
Applications of Digital Twin across Industries: A Review
Singh, M., Srivastava, R., Fuenmayor, E. A., Kuts, V., Qiao, Y., Murray, N., & Devine, D. M. (2022) · Applied Sciences · doi:10.3390/app12115727 · Cited by 376 · see citing works
A review of digital twin in product design and development
Lo, C.-H., Chen, C., & Zhong, R. Y. (2021) · Advanced Engineering Informatics · doi:10.1016/j.aei.2021.101297 · Cited by 369 · see citing works
The modelling and operations for the digital twin in the context of manufacturing
Bao, J., Guo, D., Li, J., & Zhang, J. (2019) · Enterprise Information Systems · doi:10.1080/17517575.2018.1526324 · Cited by 347 · see citing works
Wagg, D. J., Worden, K., Barthorpe, R., & Gardner, P. (2020) · ASCE-ASME J Risk and Uncert in Engrg Sys Part B Mech Engrg · doi:10.1115/1.4046739 · Cited by 321 · see citing works
Digital twin for CNC machine tool: modeling and using strategy
Luo, W., Hu, T., Zhang, C., & Wei, Y. (2019) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-018-0946-5 · Cited by 350 · see citing works
Perno, M., Hvam, L., & Haug, A. (2021) · Computers in Industry · doi:10.1016/j.compind.2021.103558 · Cited by 328 · see citing works
Recent advances on industrial data-driven energy savings: Digital twins and infrastructures
Teng, S. Y., Touš, M., Leong, W. D., How, B. S., Lam, H. L., & Máša, V. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.110208 · Cited by 315 · see citing works
Zhu, L., Chen, X., Ouyang, B., Yan, W., Lei, H., Chen, Z., & Luo, Z. (2022) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.2c01036 · Cited by 303 · see citing works
Digital Twin in Aerospace Industry: A Gentle Introduction
Li, L., Aslam, S., Wileman, A., & Perinpanayagam, S. (2021) · IEEE Access · doi:10.1109/access.2021.3136458 · Cited by 316 · see citing works
A Survey on Digital Twin for Industrial Internet of Things: Applications, Technologies and Tools
Xu, H., Wu, J., Pan, Q., Guan, X., & Guizani, M. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3297395 · Cited by 304 · see citing works
Digital twin-based WEEE recycling, recovery and remanufacturing in the background of Industry 4.0
Wang, X. V., & Wang, L. (2019) · International Journal of Production Research · doi:10.1080/00207543.2018.1497819 · Cited by 330 · see citing works
From Corrective to Predictive Maintenance—A Review of Maintenance Approaches for the Power Industry
Moleda, M., Małysiak-Mrozek, B., Ding, W.-P., Sunderam, V., & Mrozek, D. (2023) · Italian National Conference on Sensors · doi:10.3390/s23135970 · Cited by 317 · see citing works
Digital twins for collaborative robots: A case study in human-robot interaction
Malik, A. A., & Brem, A. (2020) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2020.102092 · Cited by 299 · see citing works
Wang, H., Li, B., Gong, J., & Xuan, F. (2023) · Engineering Fracture Mechanics · doi:10.1016/j.engfracmech.2023.109242 · Cited by 289 · see citing works
Digital Twins in Pharmaceutical and Biopharmaceutical Manufacturing: A Literature Review
Chen, Y., Yang, O., Sampat, C., Bhalode, P., Ramachnadran, R., & Ierapetritou, M. (2020) · Processes · doi:10.3390/pr8091088 · Cited by 295 · see citing works
The Convergence of Digital Twin, IoT, and Machine Learning: Transforming Data into Action
Kaur, M., Mishra, V., & Maheshwari, P. (2019) · Internet of Things · doi:10.1007/978-3-030-18732-3_1 · Cited by 209 · see citing works
Visualisation of the Digital Twin data in manufacturing by using Augmented Reality
Zhu, Z.-X., Liu, C., & Xu, X. (2019) · Procedia CIRP · doi:10.1016/j.procir.2019.03.223 · Cited by 241 · see citing works
Yin, Y., Zheng, P., Li, C., & Wang, L. (2022) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2022.102515 · Cited by 279 · see citing works
Delgado, J. M. D., & Oyedele, L. O. (2021) · Advanced Engineering Informatics · doi:10.1016/j.aei.2021.101332 · Cited by 272 · see citing works
Simulation-driven machine learning: Bearing fault classification
Sobie, C., Freitas, C., & Nicolai, M. (2017) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2017.06.025 · Cited by 257 · see citing works
Aivaliotis, P., Georgoulias, K., Arkouli, Z., & Makris, S. (2019) · Procedia CIRP · doi:10.1016/j.procir.2019.03.072 · Cited by 204 · see citing works
Ciano, M. P., Dallasega, P., Orzes, G., & Rossi, T. (2020) · International Journal of Production Research · doi:10.1080/00207543.2020.1821119 · Cited by 245 · see citing works
Pregnolato, M., Gunner, S., Voyagaki, E., De Risi, R., Carhart, N., Gavriel, G., Tully, P., Tryfonas, T., Macdonald, J., & Taylor, C. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104421 · Cited by 253 · see citing works
Scaling digital twins from the artisanal to the industrial
Niederer, S., Sacks, M. S., Girolami, M., & Willcox, K. E. (2021) · Nature Computational Science · doi:10.1038/s43588-021-00072-5 · Cited by 231 · see citing works
Systematic review of digital twin technology and applications
Yao, J.-F., Yang, Y., Wang, X.-C., & Zhang, X.-P. (2023) · Visual Computing for Industry, Biomedicine, and Art · doi:10.1186/s42492-023-00137-4 · Cited by 201 · see citing works
Enabling Technologies for Operator 4.0: A Survey
Ruppert, T., Jaskó, S., Holczinger, T., & Abonyi, J. (2018) · Applied Sciences · doi:10.3390/app8091650 · Cited by 236 · see citing works
A Simulation-Based Architecture for Smart Cyber-Physical Systems
Gabor, T., Belzner, L., Kiermeier, M., Beck, M. T., & Neitz, A. (2016) · 2016 IEEE International Conference on Autonomic Computing (ICAC) · doi:10.1109/icac.2016.29 · Cited by 298 · see citing works
Wagner, C., Grothoff, J., Epple, U., Drath, R., Malakuti, S., Grüner, S., Hoffmeister, M., & Zimermann, P. (2017) · IEEE International Conference on Emerging Technologies and Factory Automation · doi:10.1109/etfa.2017.8247583 · Cited by 178 · see citing works
Is Human Digital Twin possible?
Shengli, W. (2021) · Computer Methods and Programs in Biomedicine Update · doi:10.1016/j.cmpbup.2021.100014 · Cited by 233 · see citing works
Modular based flexible digital twin for factory design
Guo, J., Zhao, N., Sun, L., & Zhang, S. (2018) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-018-0953-6 · Cited by 205 · see citing works
Toward achieving precision health
Gambhir, S. S., Ge, T. J., Vermesh, O., & Spitler, R. (2018) · Science Translational Medicine · doi:10.1126/scitranslmed.aao3612 · Cited by 207 · see citing works
Zhuang, C., Miao, T., Liu, J., & Xiong, H. (2020) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2020.102075 · Cited by 202 · see citing works
Arowoiya, V., Moehler, R., & Fang, Y.-H. (2023) · Energy and Built Environment · doi:10.1016/j.enbenv.2023.05.004 · Cited by 173 · see citing works
The use of digital twins in healthcare: socio-ethical benefits and socio-ethical risks
Popa, E., van Hilten, M., Oosterkamp, E., & Bogaardt, M. (2021) · Life Sciences, Society and Policy · doi:10.1186/s40504-021-00113-x · Cited by 152 · see citing works
Improving aircraft performance using machine learning: A review
Clainche, S. L., Ferrer, E., Gibson, S., Cross, E. J., Parente, A., & Vinuesa, R. (2023) · Aerospace Science and Technology · doi:10.1016/j.ast.2023.108354 · Cited by 198 · see citing works
Peeking into the void: Digital twins for construction site logistics
Greif, T., Stein, N., & Flath, C. M. (2020) · Computers in industry (Print) · doi:10.1016/j.compind.2020.103264 · Cited by 162 · see citing works
Literature review of digital twins applications in construction workforce safety
Hou, L., Wu, S., Zhang, G. (., Tan, Y., & Wang, X. (2020) · Applied Sciences · doi:10.3390/app11010339 · Cited by 194 · see citing works
DT-II:Digital twin enhanced Industrial Internet reference framework towards smart manufacturing
Cheng, J., Zhang, H., Tao, F., & Juang, C. (2019) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2019.101881 · Cited by 194 · see citing works
A digital twin framework for civil engineering structures
Torzoni, M., Tezzele, M., Mariani, S., Manzoni, A., & Willcox, K. E. (2023) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2023.116584 · Cited by 192 · see citing works
Lattanzi, L., Raffaeli, R., Peruzzini, M., & Pellicciari, M. (2021) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192x.2021.1911003 · Cited by 205 · see citing works
Digital Twins: Review and Challenges
Juarez, M. G., Botti, V. J., & Giret, A. S. (2021) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.4050244 · Cited by 187 · see citing works
Meraghni, S., Terrissa, L. S., Yue, M., Ma, J., Jemeï, S., & Noureddine Zerhouni, A. (2020) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2020.10.108 · Cited by 186 · see citing works
Real-time mixed reality-based visual warning for construction workforce safety
Wu, S., Hou, L., Zhang, G., & Chen, H. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104252 · Cited by 189 · see citing works
Hu, Z., Lou, S., Xing, Y., Wang, X., Cao, D., & Lv, C. (2022) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2022.3195635 · Cited by 178 · see citing works
The Development of Digital Twin Technology Review
Wu, J., Yang, Y., Cheng, X., Zuo, H., & Cheng, Z. (2020) · ACM Cloud and Autonomic Computing Conference · doi:10.1109/cac51589.2020.9327756 · Cited by 114 · see citing works
A Digital Twin approach based on nonparametric Bayesian network for complex system health monitoring
Yu, J., Song, Y., Tang, D., & Dai, J. (2020) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2020.07.005 · Cited by 170 · see citing works
Data construction method for the applications of workshop digital twin system
Kong, T., Hu, T., Zhou, T., & Ye, Y. (2021) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2020.02.003 · Cited by 169 · see citing works
Blockchain-Based Digital Twins: Research Trends, Issues, and Future Challenges
Suhail, S., Hussain, R., Jurdak, R., Oracevic, A., Salah, K., Hong, C. S., & Matulevičius, R. (2022) · ACM Computing Surveys · doi:10.1145/3517189 · Cited by 179 · see citing works
Mardanshahi, A., Sreekumar, A., Yang, X. P. O., Barman, S. K., & Chronopoulos, D. (2025) · Sensors · doi:10.3390/s25051424 · Cited by 189 · see citing works
Application of Digital Twin in Smart Battery Management Systems
Wang, W., Wang, J., Tian, J., Lu, J.-H., & Xiong, R. (2021) · Chinese Journal of Mechanical Engineering · doi:10.1186/s10033-021-00577-0 · Cited by 149 · see citing works
A digital twin approach for tunnel construction safety early warning and management
Ye, Z., Ye, Y., Zhang, C., Zhang, Z., Li, W., Wang, X., Wang, L., & Wang, L. (2022) · Computers in Industry · doi:10.1016/j.compind.2022.103783 · Cited by 167 · see citing works
Digital twin applications in aviation industry: A review
Xiong, M., & Wang, H. (2022) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-022-09717-9 · Cited by 171 · see citing works
Digital twin-based assembly data management and process traceability for complex products
Zhuang, C., Gong, J., & Liu, J. (2020) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2020.05.011 · Cited by 163 · see citing works
Cheng, Y., Zhang, Y., Ji, P., Xu, W., Zhou, Z., & Tao, F. (2018) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-018-2001-2 · Cited by 161 · see citing works
Discrete Event Simulation and Digital Twins: Review and Challenges for Logistics
Agalianos, K., Ponis, S., Aretoulaki, E., Plakas, G., & Efthymiou, O. (2020) · Procedia Manufacturing · doi:10.1016/j.promfg.2020.10.228 · Cited by 140 · see citing works
Digital Twins for Cyber-Physical Systems Security: State of the Art and Outlook
Eckhart, M., & Ekelhart, A. (2019) · Security and Quality in Cyber-Physical Systems Engineering · doi:10.1007/978-3-030-25312-7_14 · Cited by 158 · see citing works
Zheng, P., & Sivabalan, A. S. (2020) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2020.101958 · Cited by 157 · see citing works
Wu, C., Zhou, Y., Pessôa, M. V. P., Peng, Q., & Tan, R. (2020) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2020.07.006 · Cited by 156 · see citing works
West, T., & Blackburn, M. (2017) · Procedia Computer Science · doi:10.1016/j.procs.2017.09.003 · Cited by 133 · see citing works
- Authors
- Tuegel, E. J., Ingraffea, A. R., Eason, T. G., & Spottswood, S. M.
- Affiliations
- United States Air Force Research Laboratory (US)
- Cornell University (US)
- Corresponding author
- Tuegel, Eric J.
- Venue
- International Journal of Aerospace Engineering
- Publisher
- Wiley
- Published
- 2011
- Volume
- 2011
- Pages
- 1-14
- ISSN
- 1687-5966 (ISSN-L)
- 1687-5974
- OpenAlex
- W2108366027
- Cited by
- 1,119
- References
- 45
- Citations by year
- 2026: 75 · 2025: 119 · 2024: 173 · 2023: 158 · 2022: 179 · 2021: 129 · 2020: 110 · 2019: 77 · 2018: 50 · 2017: 20 · 2016: 12 · 2015: 6 · 2014: 4 · 2013: 3 · 2012: 4
- FWCI
- 10.15
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- International Journal of Aerospace Engineering (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
- Physical Sciences › Engineering › Mechanics of Materials › Fatigue and fracture mechanics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Keywords
- digital twin, structural integrity, structural deflection, temperature prediction, local damage, flight conditions, aircraft structural health monitoring
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
