Data‐driven physics‐based digital twins via a library of component‐based reduced‐order models
Summary This work proposes an approach that combines a library of component‐based reduced‐order models with Bayesian state estimation in order to create data‐driven physics‐based digital twins. Reduced‐order modeling produces physics‐based computational models that are reliable enough for predictive...
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| Veröffentlicht in: | International journal for numerical methods in engineering Jg. 123; H. 13; S. 2986 - 3003 |
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| Hauptverfasser: | , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
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Hoboken, USA
John Wiley & Sons, Inc
15.07.2022
Wiley Subscription Services, Inc |
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| ISSN: | 0029-5981, 1097-0207 |
| Online-Zugang: | Volltext |
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| Abstract | Summary
This work proposes an approach that combines a library of component‐based reduced‐order models with Bayesian state estimation in order to create data‐driven physics‐based digital twins. Reduced‐order modeling produces physics‐based computational models that are reliable enough for predictive digital twins, while still being fast to evaluate. In contrast with traditional monolithic techniques for model reduction, the component‐based approach scales efficiently to large complex systems, and provides a flexible and expressive framework for rapid model adaptation—both critical features in the digital twin context. Data‐driven model adaptation and uncertainty quantification are formulated as a Bayesian state estimation problem, in which sensor data are used to infer which models in the model library are the best candidates for the digital twin. This approach is demonstrated through the development of a digital twin for a 12‐ft wingspan unmanned aerial vehicle. Offline, we construct a library of pristine and damaged aircraft components. Online, we use structural sensor data to rapidly adapt a physics‐based digital twin of the aircraft structure. The data‐driven digital twin enables the aircraft to dynamically replan a safe mission in response to structural damage or degradation. |
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| AbstractList | Summary
This work proposes an approach that combines a library of component‐based reduced‐order models with Bayesian state estimation in order to create data‐driven physics‐based digital twins. Reduced‐order modeling produces physics‐based computational models that are reliable enough for predictive digital twins, while still being fast to evaluate. In contrast with traditional monolithic techniques for model reduction, the component‐based approach scales efficiently to large complex systems, and provides a flexible and expressive framework for rapid model adaptation—both critical features in the digital twin context. Data‐driven model adaptation and uncertainty quantification are formulated as a Bayesian state estimation problem, in which sensor data are used to infer which models in the model library are the best candidates for the digital twin. This approach is demonstrated through the development of a digital twin for a 12‐ft wingspan unmanned aerial vehicle. Offline, we construct a library of pristine and damaged aircraft components. Online, we use structural sensor data to rapidly adapt a physics‐based digital twin of the aircraft structure. The data‐driven digital twin enables the aircraft to dynamically replan a safe mission in response to structural damage or degradation. This work proposes an approach that combines a library of component‐based reduced‐order models with Bayesian state estimation in order to create data‐driven physics‐based digital twins. Reduced‐order modeling produces physics‐based computational models that are reliable enough for predictive digital twins, while still being fast to evaluate. In contrast with traditional monolithic techniques for model reduction, the component‐based approach scales efficiently to large complex systems, and provides a flexible and expressive framework for rapid model adaptation—both critical features in the digital twin context. Data‐driven model adaptation and uncertainty quantification are formulated as a Bayesian state estimation problem, in which sensor data are used to infer which models in the model library are the best candidates for the digital twin. This approach is demonstrated through the development of a digital twin for a 12‐ft wingspan unmanned aerial vehicle. Offline, we construct a library of pristine and damaged aircraft components. Online, we use structural sensor data to rapidly adapt a physics‐based digital twin of the aircraft structure. The data‐driven digital twin enables the aircraft to dynamically replan a safe mission in response to structural damage or degradation. |
| Author | Tran, M. Willcox, K.E. Kapteyn, M.G. Huynh, D.B.P. Knezevic, D.J. |
| Author_xml | – sequence: 1 givenname: M.G. orcidid: 0000-0002-3594-682X surname: Kapteyn fullname: Kapteyn, M.G. email: mkapteyn@mit.edu organization: Massachusetts Institute of Technology – sequence: 2 givenname: D.J. surname: Knezevic fullname: Knezevic, D.J. organization: Akselos Inc – sequence: 3 givenname: D.B.P. surname: Huynh fullname: Huynh, D.B.P. organization: Akselos S.A – sequence: 4 givenname: M. surname: Tran fullname: Tran, M. organization: Akselos S.A – sequence: 5 givenname: K.E. surname: Willcox fullname: Willcox, K.E. organization: University of Texas at Austin |
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| Cites_doi | 10.2514/1.J053893 10.2514/6.2003-3847 10.2514/8.3664 10.1137/100795772 10.2514/3.2874 10.2514/3.50778 10.2514/6.2012-1812 10.1007/s11831-011-9064-7 10.3182/20120215-3-AT-3016.00123 10.1007/978-3-319-02090-7 10.2514/1.J055201 10.1137/15M1009603 10.1016/j.crma.2004.08.006 10.2514/6.2013-1578 10.1002/nme.4669 10.1186/2213‐7467‐1‐3 10.1016/j.jcp.2012.07.022 10.1016/j.cma.2014.09.020 10.1016/j.procs.2012.04.130 10.1137/1.9780898718713 10.1115/GT2017-63336 10.2514/1.J057255 10.1007/BF03024948 10.1109/TCNS.2016.2607420 10.1137/130932715 10.2514/6.2012-1818 10.1007/s11831‐018‐9301‐4 10.1016/j.crme.2019.11.004 10.1051/m2an/2012022 10.2514/6.1999-1394 10.1137/140995817 10.1090/S0025-5718-1985-0804937-0 10.1002/nme.4543 10.1007/11564096_59 10.1111/1467-9868.00294 10.2514/3.7539 10.1109/TCNS.2016.2606880 |
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| References | 2015; 57 2015; 283 2013a; 96 2013; 47 2012 2011 2015; 102 2015; 53 2019; 347 2008 1978; 16 2005 2018; 329 2003 1985; 45 2016; 38 2011; 18 2018; 27 2001; 63 2007; 15 1999 2016; 4 1965; 3 2017; 53 2012; 231 1980; 18 2017; 55 1956; 23 2017 2005; 6 2011; 43 2016 2013b; 1 2013 2016a; 38 2014; 9 2018; 56 2004; 339 2012; 45 2012; 9 e_1_2_8_28_1 Ross S (e_1_2_8_40_1) 2008 e_1_2_8_24_1 e_1_2_8_25_1 e_1_2_8_26_1 e_1_2_8_27_1 e_1_2_8_3_1 e_1_2_8_2_1 e_1_2_8_5_1 e_1_2_8_7_1 e_1_2_8_6_1 e_1_2_8_9_1 e_1_2_8_8_1 e_1_2_8_20_1 e_1_2_8_43_1 e_1_2_8_21_1 e_1_2_8_42_1 e_1_2_8_22_1 e_1_2_8_45_1 e_1_2_8_23_1 e_1_2_8_44_1 Tuegel EJ (e_1_2_8_4_1) 2011 e_1_2_8_41_1 e_1_2_8_17_1 e_1_2_8_18_1 e_1_2_8_39_1 e_1_2_8_19_1 e_1_2_8_13_1 e_1_2_8_36_1 e_1_2_8_14_1 Russell SJ (e_1_2_8_35_1) 2016 e_1_2_8_15_1 e_1_2_8_38_1 e_1_2_8_16_1 e_1_2_8_37_1 Ballani J (e_1_2_8_29_1) 2018; 329 e_1_2_8_32_1 e_1_2_8_10_1 e_1_2_8_31_1 e_1_2_8_11_1 e_1_2_8_34_1 e_1_2_8_12_1 e_1_2_8_33_1 e_1_2_8_30_1 |
| References_xml | – year: 2011 – volume: 96 start-page: 269 issue: 5 year: 2013a end-page: 302 article-title: Port reduction in parametrized component static condensation: Approximation and a posteriori error estimation publication-title: Int J Numer Methods Eng – volume: 23 start-page: 805 year: 1956 end-page: 823 article-title: Stiffness and deflection analysis of complex structures publication-title: J Aeronaut Sci – volume: 47 start-page: 213 issue: 1 year: 2013 end-page: 251 article-title: A static condensation reduced basis element method: approximation and a posteriori error estimation publication-title: ESAIM Math Modell Numer Anal – volume: 9 start-page: 1206 year: 2012 end-page: 1210 article-title: Dynamic data driven methods for self‐aware aerospace vehicles publication-title: Proc Comput Sci – volume: 4 start-page: 49 issue: 1 year: 2016 end-page: 59 article-title: Secure state estimation against sensor attacks in the presence of noise publication-title: IEEE Trans Control Netw Syst – year: 2005 – volume: 56 start-page: 4515 issue: 11 year: 2018 end-page: 4528 article-title: Engineering design with digital thread publication-title: AIAA J – volume: 339 start-page: 667 issue: 9 year: 2004 end-page: 672 article-title: An 'imempirical interpolation' method: application to efficient reduced‐basis discretization of partial differential equations publication-title: Comptes Rendus Mathematique – volume: 4 start-page: 380 issue: 1 year: 2016 end-page: 412 article-title: Accurate solution of Bayesian inverse uncertainty quantification problems combining reduced basis methods and reduction error models publication-title: SIAM/ASA J Uncert Quantif – year: 2008 article-title: Bayes‐adaptive POMDPs publication-title: In Advances in Neural Information Processing Systems – year: 2003 – volume: 329 start-page: 498 year: 2018 end-page: 531 article-title: A component‐based hybrid reduced basis/finite element method for solid mechanics with local nonlinearities publication-title: SIAM J Sci Comput – volume: 53 start-page: 3073 issue: 10 year: 2015 end-page: 3087 article-title: Methodology for dynamic data‐driven online flight capability estimation publication-title: AIAA J – volume: 15 start-page: 1 issue: 3 year: 2007 article-title: Reduced basis approximation and a posteriori error estimation for affinely parametrized elliptic coercive partial differential equations publication-title: Arch Comput Methods Eng – volume: 102 start-page: 379 issue: 3‐4 year: 2015 end-page: 403 article-title: A computational framework for dynamic data‐driven material damage control, based on Bayesian inference and model selection publication-title: Int J Numer Methods Eng – volume: 347 start-page: 762 issue: 11 year: 2019 end-page: 779 article-title: Real‐time Bayesian data assimilation with data selection, correction of model bias, and on‐the‐fly uncertainty propagation publication-title: Comptes Rendus Mécanique – year: 2016 – volume: 38 start-page: A3318 issue: 5 year: 2016 end-page: A3356 article-title: Optimal local approximation spaces for component‐based static condensation procedures publication-title: SIAM Journal on Scientific Computing – year: 2012 – volume: 55 start-page: 930 issue: 3 year: 2017 end-page: 941 article-title: Dynamic Bayesian network for aircraft wing health monitoring digital twin publication-title: AIAA J – volume: 6 year: 2005 – volume: 3 start-page: 380 year: 1965 end-page: 380 article-title: Reduction of stiffness and mass matrices publication-title: AIAA J – volume: 9 year: 2014 – volume: 53 start-page: 3073 issue: 10 year: 2017 end-page: 3087 article-title: Methodology for path planning with dynamic data‐driven flight capability estimation publication-title: AIAA Journal – volume: 18 start-page: 455 issue: 4 year: 1980 end-page: 462 article-title: Reduced basis technique for nonlinear analysis of structures publication-title: AIAA J – volume: 63 start-page: 425 issue: 3 year: 2001 end-page: 464 article-title: Bayesian calibration of computer models publication-title: J Royal Stat Soc Ser B (Stat Methodol) – volume: 231 start-page: 7815 issue: 23 year: 2012 end-page: 7850 article-title: Bayesian inference with optimal maps publication-title: J Comput Phys – volume: 43 start-page: 1457 issue: 3 year: 2011 end-page: 1472 article-title: Convergence rates for greedy algorithms in reduced basis methods publication-title: SIAM J Math Anal – volume: 4 start-page: 82 issue: 1 year: 2016 end-page: 92 article-title: Attack‐resilient state estimation for noisy dynamical systems publication-title: IEEE Trans Control Netw Syst – volume: 45 start-page: 695 issue: 2 year: 2012 end-page: 699 article-title: Adaptive port reduction in static condensation publication-title: IFAC Proc Vol – volume: 1 start-page: 1 issue: 3 year: 2013b end-page: 49 article-title: A port‐reduced static condensation reduced basis element method for large component‐synthesized structures: approximation and a posteriori error estimation publication-title: Advanced Modeling and Simulation in Engineering Sciences – volume: 16 start-page: 525 issue: 5 year: 1978 end-page: 528 article-title: Automatic choice of global shape functions in structural analysis publication-title: AIAAl J – volume: 18 start-page: 395 year: 2011 end-page: 404 article-title: A short review on model order reduction based on proper generalized decomposition publication-title: Arch Comput Methods Eng – year: 2017 – volume: 45 start-page: 487 issue: 172 year: 1985 end-page: 496 article-title: Estimation of the error in the reduced basis method solution of nonlinear equations publication-title: Math Comput – volume: 27 start-page: 105 issue: 1 year: 2018 end-page: 134 article-title: Virtual, digital and hybrid twins: a new paradigm in data‐based engineering and engineered data publication-title: Archives of Computational Methods in Engineering – volume: 283 start-page: 352 year: 2015 end-page: 383 article-title: A new certification framework for the port reduced static condensation reduced basis element method publication-title: Comput Methods Appl Mech Eng – year: 2013 – volume: 57 start-page: 483 issue: 4 year: 2015 end-page: 531 article-title: A survey of projection‐based model reduction methods for parametric dynamical systems publication-title: SIAM Rev – volume: 38 start-page: A3318 issue: 5 year: 2016a end-page: A3356 article-title: Optimal local approximation spaces for component‐based static condensation procedures publication-title: SIAM J Sci Comput – year: 1999 – ident: e_1_2_8_18_1 doi: 10.2514/1.J053893 – ident: e_1_2_8_27_1 doi: 10.2514/6.2003-3847 – ident: e_1_2_8_30_1 doi: 10.2514/8.3664 – ident: e_1_2_8_28_1 doi: 10.1137/100795772 – ident: e_1_2_8_31_1 doi: 10.2514/3.2874 – ident: e_1_2_8_23_1 doi: 10.2514/3.50778 – ident: e_1_2_8_7_1 doi: 10.2514/6.2012-1812 – ident: e_1_2_8_11_1 doi: 10.1007/s11831-011-9064-7 – volume-title: International Journal of Aerospace Engineering year: 2011 ident: e_1_2_8_4_1 – ident: e_1_2_8_20_1 doi: 10.3182/20120215-3-AT-3016.00123 – ident: e_1_2_8_10_1 doi: 10.1007/978-3-319-02090-7 – ident: e_1_2_8_3_1 doi: 10.2514/1.J055201 – ident: e_1_2_8_22_1 doi: 10.1137/15M1009603 – ident: e_1_2_8_19_1 doi: 10.2514/1.J053893 – ident: e_1_2_8_42_1 doi: 10.1016/j.crma.2004.08.006 – ident: e_1_2_8_6_1 doi: 10.2514/6.2013-1578 – ident: e_1_2_8_37_1 doi: 10.1002/nme.4669 – ident: e_1_2_8_32_1 doi: 10.1186/2213‐7467‐1‐3 – ident: e_1_2_8_38_1 doi: 10.1016/j.jcp.2012.07.022 – volume-title: Artificial Intelligence: A Modern Approach year: 2016 ident: e_1_2_8_35_1 – ident: e_1_2_8_33_1 doi: 10.1016/j.cma.2014.09.020 – ident: e_1_2_8_17_1 doi: 10.1016/j.procs.2012.04.130 – year: 2008 ident: e_1_2_8_40_1 article-title: Bayes‐adaptive POMDPs publication-title: In Advances in Neural Information Processing Systems – ident: e_1_2_8_12_1 doi: 10.1137/1.9780898718713 – ident: e_1_2_8_34_1 doi: 10.1137/15M1009603 – ident: e_1_2_8_41_1 – ident: e_1_2_8_5_1 doi: 10.1115/GT2017-63336 – ident: e_1_2_8_36_1 doi: 10.2514/1.J057255 – ident: e_1_2_8_26_1 doi: 10.1007/BF03024948 – ident: e_1_2_8_45_1 doi: 10.1109/TCNS.2016.2607420 – ident: e_1_2_8_9_1 doi: 10.1137/130932715 – ident: e_1_2_8_2_1 doi: 10.2514/6.2012-1818 – ident: e_1_2_8_8_1 doi: 10.1007/s11831‐018‐9301‐4 – ident: e_1_2_8_15_1 doi: 10.1016/j.crme.2019.11.004 – ident: e_1_2_8_13_1 doi: 10.1051/m2an/2012022 – ident: e_1_2_8_43_1 doi: 10.2514/6.1999-1394 – ident: e_1_2_8_16_1 doi: 10.1137/140995817 – ident: e_1_2_8_25_1 doi: 10.1090/S0025-5718-1985-0804937-0 – ident: e_1_2_8_21_1 doi: 10.1002/nme.4543 – ident: e_1_2_8_39_1 doi: 10.1007/11564096_59 – ident: e_1_2_8_14_1 doi: 10.1111/1467-9868.00294 – ident: e_1_2_8_24_1 doi: 10.2514/3.7539 – volume: 329 start-page: 498 year: 2018 ident: e_1_2_8_29_1 article-title: A component‐based hybrid reduced basis/finite element method for solid mechanics with local nonlinearities publication-title: SIAM J Sci Comput – ident: e_1_2_8_44_1 doi: 10.1109/TCNS.2016.2606880 |
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This work proposes an approach that combines a library of component‐based reduced‐order models with Bayesian state estimation in order to create... This work proposes an approach that combines a library of component‐based reduced‐order models with Bayesian state estimation in order to create data‐driven... |
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| SubjectTerms | Adaptation Aircraft Aircraft components Aircraft structures Bayesian analysis Complex systems data‐model fusion digital twin Digital twins Libraries Model reduction model updating Physics reduced‐order model State estimation Structural damage unmanned aerial vehicle Unmanned aerial vehicles Wing span |
| Title | Data‐driven physics‐based digital twins via a library of component‐based reduced‐order models |
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