Digital Twin Method for Dynamic Structures Based on Reduced Order Models and Data Driving

WANG Qingshan; YAN Bo; CHEN Yan; DENG Mao; CAI Yuanbin

doi:10.21656/1000-0887.430384

Volume 44 Issue 7

Jul. 2023

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WANG Qingshan, YAN Bo, CHEN Yan, DENG Mao, CAI Yuanbin. Digital Twin Method for Dynamic Structures Based on Reduced Order Models and Data Driving[J]. Applied Mathematics and Mechanics, 2023, 44(7): 757-768. doi: 10.21656/1000-0887.430384

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Digital Twin Method for Dynamic Structures Based on Reduced Order Models and Data Driving

doi: 10.21656/1000-0887.430384

College of Aerospace Engineering, Chongqing University, Chongqing 400044, P.R.China

Received Date: 2022-11-29
Rev Recd Date: 2022-12-09
Publish Date: 2023-07-01

Abstract

Abstract

A digital twin construction method based on the reduced order model library and machine learning was proposed for structures under dynamic loads. Firstly, the high-fidelity finite element models were established according to the possible damage states occurring during the service of the physical structures. Secondly, the Krylov subspace order reduction method was used to reduce the orders of the models and the reduced order models were assembled to a library. Finally, the random forest machine learning algorithm was used to train the model selector, infer the current state of the physical structure through the sensor data from the structure, and then drive the digital twin to evolve with the physical structure. A physical frame structure was designed and manufactured to simulate the damages of different degrees at different points, and verify the proposed digital twin construction method for dynamic structures.
- structural digital twin,
- reduced order model library,
- Krylov subspace model order reduction,
- random forest,
- data-driven method

(Contributed by YAN Bo, M.AMM Editorial Board)

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References(29)

References

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