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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Bibliographic Details
Published in:International journal for numerical methods in engineering Vol. 123; no. 13; pp. 2986 - 3003
Main Authors: Kapteyn, M.G., Knezevic, D.J., Huynh, D.B.P., Tran, M., Willcox, K.E.
Format: Journal Article
Language:English
Published: Hoboken, USA John Wiley & Sons, Inc 15.07.2022
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Subjects:
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
ISSN:0029-5981, 1097-0207
Online Access:Get full text
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Summary: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.
Bibliography:Funding information
Air Force Office of Scientific Research, FA9550‐16‐1‐0108; Boeing, SUTD‐MIT International Design Center
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ISSN:0029-5981
1097-0207
DOI:10.1002/nme.6423