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2023 | OriginalPaper | Chapter

10. A Physics-Based Reduction with Monitoring Data Assimilation for Adaptive Representations in Structural Systems

Authors : Konstantinos Vlachas, Konstantinos Tatsis, Carianne Martinez, Eleni Chatzi

Published in: Model Validation and Uncertainty Quantification, Volume 3

Publisher: Springer International Publishing

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Abstract

Digital twin representations have become an indispensable tool for delivering data-informed virtualizations of operating systems, especially in structural health monitoring applications. In this context, challenges arise when the response often shifts beyond regular operating conditions due to extreme events such as earthquakes or structural damage. Our work proposes a reduced order modeling for adaptive digital twins, for systems undergoing damage, condition deterioration, or experiencing stochastic excitation. Our approach initiates by featuring a projection-based reduced order model (ROM), relying on proper orthogonal decomposition (POD) and local subspaces to form a low-cost surrogate of the parametrized high-fidelity system that retains a physical connotation. However, extreme events induce loading conditions and model states that challenge the accuracy of such representations. To this end, we propose adopting the derived ROM as a forward simulator and adapt the projection basis on-the-fly during operation via a Gaussian processes regressor (GPR) scheme. During operation, the ROM framework receives response monitoring information from a sparse number of nodes. It employs a suitable condition indicator to highlight the potential low precision of the initial surrogate. Subsequently, the GPR-based scheme utilizes the monitoring input to reconstruct the current deformed configuration of the whole system in an online manner. In turn, this approximation serves as a damaged mode that enriches the projection-based ROM and enables online adaptivity. This coupling yields a ROM equipped with critical features for health monitoring applications such as (near) real-time basis refinement, signaling potentially irreversible consequences, and estimation of the uncertainty in the enrichment mode and the adapted ROM prediction.

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Literature
1.
go back to reference Chellappa, S., Feng, L., Benner, P.: Adaptive basis construction and improved error estimation for parametric nonlinear dynamical systems. Int. J. Numer. Methods Eng. 121(23), 5320–5349 (2020)MathSciNetCrossRef Chellappa, S., Feng, L., Benner, P.: Adaptive basis construction and improved error estimation for parametric nonlinear dynamical systems. Int. J. Numer. Methods Eng. 121(23), 5320–5349 (2020)MathSciNetCrossRef
2.
go back to reference Cortinovis, A., Kressner, D., Massei, S., Peherstorfer, B.: Quasi-optimal sampling to learn basis updates for online adaptive model reduction with adaptive empirical interpolation. In: 2020 American Control Conference (ACC), pp. 2472–2477. IEEE (2020) Cortinovis, A., Kressner, D., Massei, S., Peherstorfer, B.: Quasi-optimal sampling to learn basis updates for online adaptive model reduction with adaptive empirical interpolation. In: 2020 American Control Conference (ACC), pp. 2472–2477. IEEE (2020)
3.
go back to reference Peherstorfer, B., Willcox, K.: Online adaptive model reduction for nonlinear systems via low-rank updates. SIAM J. Sci. Comput. 37(4), A2123–A2150 (2015)MathSciNetCrossRef Peherstorfer, B., Willcox, K.: Online adaptive model reduction for nonlinear systems via low-rank updates. SIAM J. Sci. Comput. 37(4), A2123–A2150 (2015)MathSciNetCrossRef
4.
go back to reference Rocha, I., van der Meer, F., Sluys, L.J.: An adaptive domain-based POD/ECM hyper-reduced modeling framework without offline training. Comput. Methods Appl. Mech. Eng. 358, 112650 (2020)MathSciNetCrossRef Rocha, I., van der Meer, F., Sluys, L.J.: An adaptive domain-based POD/ECM hyper-reduced modeling framework without offline training. Comput. Methods Appl. Mech. Eng. 358, 112650 (2020)MathSciNetCrossRef
5.
go back to reference Rocha, I.B., Van Der Meer, F.P., Mororó, L.A., Sluys, L.J.: Accelerating crack growth simulations through adaptive model order reduction. Int. J. Numer. Methods Eng. 121(10), 2147–2173 (2020)MathSciNetCrossRef Rocha, I.B., Van Der Meer, F.P., Mororó, L.A., Sluys, L.J.: Accelerating crack growth simulations through adaptive model order reduction. Int. J. Numer. Methods Eng. 121(10), 2147–2173 (2020)MathSciNetCrossRef
6.
go back to reference Carlberg, K.: Adaptive h-refinement for reduced-order models. Int. J. Numer. Methods Eng. 102(5), 1192–1210 (2015)MathSciNetCrossRef Carlberg, K.: Adaptive h-refinement for reduced-order models. Int. J. Numer. Methods Eng. 102(5), 1192–1210 (2015)MathSciNetCrossRef
7.
go back to reference Etter, P.A., Carlberg, K.T.: Online adaptive basis refinement and compression for reduced-order models via vector-space sieving. Comput. Methods Appl. Mech. Eng. 364, 112931 (2020)MathSciNetCrossRef Etter, P.A., Carlberg, K.T.: Online adaptive basis refinement and compression for reduced-order models via vector-space sieving. Comput. Methods Appl. Mech. Eng. 364, 112931 (2020)MathSciNetCrossRef
8.
go back to reference Vlachas, K., Tatsis, K., Agathos, K., Brink, A.R., Chatzi, E.: A local basis approximation approach for nonlinear parametric model order reduction. J. Sound Vibration 502, 116055 (2021)CrossRef Vlachas, K., Tatsis, K., Agathos, K., Brink, A.R., Chatzi, E.: A local basis approximation approach for nonlinear parametric model order reduction. J. Sound Vibration 502, 116055 (2021)CrossRef
9.
go back to reference Tatsis, K., Lourens, E.: A comparison of two Kalman-type filters for robust extrapolation of offshore wind turbine support structure response. In: Life-Cycle of Engineering Systems, pp. 209–216. CRC Press (2016) Tatsis, K., Lourens, E.: A comparison of two Kalman-type filters for robust extrapolation of offshore wind turbine support structure response. In: Life-Cycle of Engineering Systems, pp. 209–216. CRC Press (2016)
10.
go back to reference Agathos, K., Tatsis, K.E., Vlachas, K., Chatzi, E.: Parametric reduced order models for output-only vibration-based crack detection in shell structures. Mech. Syst. Signal Process. 162, 108051 (2022)CrossRef Agathos, K., Tatsis, K.E., Vlachas, K., Chatzi, E.: Parametric reduced order models for output-only vibration-based crack detection in shell structures. Mech. Syst. Signal Process. 162, 108051 (2022)CrossRef
Metadata
Title
A Physics-Based Reduction with Monitoring Data Assimilation for Adaptive Representations in Structural Systems
Authors
Konstantinos Vlachas
Konstantinos Tatsis
Carianne Martinez
Eleni Chatzi
Copyright Year
2023
DOI
https://doi.org/10.1007/978-3-031-04090-0_10