Model Updating for Nonlinear Dynamic Digital Twins Using Data-Based Inverse Mapping Models

Bas M. Kessels; Rob H.B. Fey; Mohammad H. Abbasi; Nathan van de Wouw

doi:10.1007/978-3-031-04122-8_1

Model Updating for Nonlinear Dynamic Digital Twins Using Data-Based Inverse Mapping Models

Bas M. Kessels, Rob H.B. Fey, Mohammad H. Abbasi, Nathan van de Wouw

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

2 Citaten (Scopus)

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In order to ensure that a digital twin accurately describes the dynamic behavior of its corresponding physical system, model updating is typically applied. This chapter introduces a (near) real-time method that uses inverse mapping models to update first-principles-based nonlinear dynamics models. The inverse mapping model infers a set of physically interpretable updating parameter values on the basis of a set of time-domain features extracted from measurements on the real system. Here, the inverse model is given by an artificial neural network that is trained using simulated data. By using a simple nonlinear multibody model, it is illustrated that this method is able to accurately and precisely update parameter values with low computational effort.

Originele taal-2	Engels
Titel	Data Science in Engineering
Subtitel	Proceedings of the 40th IMAC, A Conference and Exposition on Structural Dynamics, 2022
Redacteuren	Ramin Madarshahian, Francois Hemez
Plaats van productie	Cham
Uitgeverij	Springer
Hoofdstuk	1
Pagina's	1-4
Aantal pagina's	4
Volume	9
ISBN van elektronische versie	978-3-031-04122-8
ISBN van geprinte versie	978-3-031-04121-1
DOI's	https://doi.org/10.1007/978-3-031-04122-8_1
Status	Gepubliceerd - 2022
Evenement	40th IMAC, A Conference and Exposition on Structural Dynamics, 2022 - Rosen Plaza Hotel, Orlando, Verenigde Staten van Amerika Duur: 7 feb. 2022 → 10 feb. 2022 Congresnummer: 40 https://sem.org/imac

Congres

Congres	40th IMAC, A Conference and Exposition on Structural Dynamics, 2022
Verkorte titel	IMAC-XL
Land/Regio	Verenigde Staten van Amerika
Stad	Orlando
Periode	7/02/22 → 10/02/22
Internet adres	https://sem.org/imac

Bibliografische nota

Funding Information:
This publication is part of the project Digital Twin (project 2.1) with project number P18-03 of the research program Perspectief, which is (mainly) financed by the Dutch Research Council (NWO).

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10.1007/978-3-031-04122-8_1

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Kessels, B. M., Fey, R. H. B., Abbasi, M. H., & van de Wouw, N. (2022). Model Updating for Nonlinear Dynamic Digital Twins Using Data-Based Inverse Mapping Models. In R. Madarshahian, & F. Hemez (editors), Data Science in Engineering: Proceedings of the 40th IMAC, A Conference and Exposition on Structural Dynamics, 2022 (Vol. 9, blz. 1-4). Springer. https://doi.org/10.1007/978-3-031-04122-8_1

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title = "Model Updating for Nonlinear Dynamic Digital Twins Using Data-Based Inverse Mapping Models",

abstract = "In order to ensure that a digital twin accurately describes the dynamic behavior of its corresponding physical system, model updating is typically applied. This chapter introduces a (near) real-time method that uses inverse mapping models to update first-principles-based nonlinear dynamics models. The inverse mapping model infers a set of physically interpretable updating parameter values on the basis of a set of time-domain features extracted from measurements on the real system. Here, the inverse model is given by an artificial neural network that is trained using simulated data. By using a simple nonlinear multibody model, it is illustrated that this method is able to accurately and precisely update parameter values with low computational effort.",

keywords = "Digital twin, Machine learning, Model updating, Nonlinear dynamics",

author = "Kessels, {Bas M.} and Fey, {Rob H.B.} and Abbasi, {Mohammad H.} and {van de Wouw}, Nathan",

note = "Funding Information: This publication is part of the project Digital Twin (project 2.1) with project number P18-03 of the research program Perspectief, which is (mainly) financed by the Dutch Research Council (NWO). ; 40th IMAC, A Conference and Exposition on Structural Dynamics, 2022, IMAC-XL ; Conference date: 07-02-2022 Through 10-02-2022",

year = "2022",

doi = "10.1007/978-3-031-04122-8_1",

language = "English",

isbn = "978-3-031-04121-1",

volume = "9",

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editor = "Ramin Madarshahian and Francois Hemez",

booktitle = "Data Science in Engineering",

publisher = "Springer",

url = "https://sem.org/imac",

}

Kessels, BM, Fey, RHB, Abbasi, MH & van de Wouw, N 2022, Model Updating for Nonlinear Dynamic Digital Twins Using Data-Based Inverse Mapping Models. in R Madarshahian & F Hemez (uitgave), Data Science in Engineering: Proceedings of the 40th IMAC, A Conference and Exposition on Structural Dynamics, 2022. vol. 9, Springer, Cham, blz. 1-4, 40th IMAC, A Conference and Exposition on Structural Dynamics, 2022, Orlando, Florida, Verenigde Staten van Amerika, 7/02/22. https://doi.org/10.1007/978-3-031-04122-8_1

Model Updating for Nonlinear Dynamic Digital Twins Using Data-Based Inverse Mapping Models. / Kessels, Bas M.; Fey, Rob H.B.; Abbasi, Mohammad H. et al.
Data Science in Engineering: Proceedings of the 40th IMAC, A Conference and Exposition on Structural Dynamics, 2022. uitgave / Ramin Madarshahian; Francois Hemez. Vol. 9 Cham: Springer, 2022. blz. 1-4.

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

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AB - In order to ensure that a digital twin accurately describes the dynamic behavior of its corresponding physical system, model updating is typically applied. This chapter introduces a (near) real-time method that uses inverse mapping models to update first-principles-based nonlinear dynamics models. The inverse mapping model infers a set of physically interpretable updating parameter values on the basis of a set of time-domain features extracted from measurements on the real system. Here, the inverse model is given by an artificial neural network that is trained using simulated data. By using a simple nonlinear multibody model, it is illustrated that this method is able to accurately and precisely update parameter values with low computational effort.

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Model Updating for Nonlinear Dynamic Digital Twins Using Data-Based Inverse Mapping Models

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