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Digital twins for monitoring purposes - uncertainty, model bias and model order reduction

  • A safe and robust performance is a key criterion when building and maintaining structures and component. Ensuring this criterion at different stages of the lifetime can be supported by applying continuous monitoring concepts. The latter usually can serve multiple purposes, including the determination of material parameters for the design phase, the evaluation of the actual loading/environmental conditions (instead of using conservative estimates that are usually larger) and evaluating or predicting the true performance of the structure (thus decreasing the model bias). In this context, a digital twin of the structure has many benefits. It allows to introduce virtual sensors to “measure” sensor information that is e.g. inaccessible or unmeasureable. In order to efficiently use monitoring techniques in the context of a digital twin, it is important to consider the complete chain of information including the choice of sensors, the data processing and structuring, the modellingA safe and robust performance is a key criterion when building and maintaining structures and component. Ensuring this criterion at different stages of the lifetime can be supported by applying continuous monitoring concepts. The latter usually can serve multiple purposes, including the determination of material parameters for the design phase, the evaluation of the actual loading/environmental conditions (instead of using conservative estimates that are usually larger) and evaluating or predicting the true performance of the structure (thus decreasing the model bias). In this context, a digital twin of the structure has many benefits. It allows to introduce virtual sensors to “measure” sensor information that is e.g. inaccessible or unmeasureable. In order to efficiently use monitoring techniques in the context of a digital twin, it is important to consider the complete chain of information including the choice of sensors, the data processing and structuring, the modelling assumptions, the numerical simulation and finally the stochastic nature of the model prediction. In this presentation, challenges in this context are discussed with a specific focus on Bayesian model updating of the digital twin, accounting for both parameter updates as well as model bias that results from the limitations of modelling assumption. A bottleneck in this approach is the computational effort related to sampling methods such as Markov chain Monte Carlo methods that require many evaluations of the forward model. An alternative to the expensive computation of the forward model for updating the digital twin is the combination with model reduction techniques such as the Proper General Decomposition [1, 2]. The results are illustrated for several examples and scale, ranging from digitals twin for material tests in the lab over lab scale structural digital twins up to damage identification in field experiments.zeige mehrzeige weniger

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Metadaten
Autor*innen:Jörg F. UngerORCiD
Koautor*innen:Isabela Coehlo Lima, Abbas Jafari, Thomas Titscher, Annika Robens-Radermacher
Dokumenttyp:Vortrag
Veröffentlichungsform:Präsentation
Sprache:Englisch
Jahr der Erstveröffentlichung:2021
Organisationseinheit der BAM:7 Bauwerkssicherheit
7 Bauwerkssicherheit / 7.7 Modellierung und Simulation
DDC-Klassifikation:Technik, Medizin, angewandte Wissenschaften / Ingenieurwissenschaften / Ingenieurbau
Freie Schlagwörter:Digital twin for monitoring purposes; Model bias; Model order reduction
Themenfelder/Aktivitätsfelder der BAM:Infrastruktur
Veranstaltung:MMLDT-CSET 2021 Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering & Technology
Veranstaltungsort:San Diego, CA, USA
Beginndatum der Veranstaltung:26.09.2021
Enddatum der Veranstaltung:29.09.2021
Zugehöriger Identifikator:https://mmldt.eng.ucsd.edu/home
Verfügbarkeit des Dokuments:Datei im Netzwerk der BAM verfügbar ("Closed Access")
Datum der Freischaltung:02.12.2021
Referierte Publikation:Nein
Eingeladener Vortrag:Nein
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