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Experimental Mechanics

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06-03-2024 | Research paper

An Improved Strain Field Reconstruction Method Based on Digital Twin for Test Monitoring

Authors: B. Wang, X. Ke, Z. Song, K. Du, X. Bi, P. Hao, C. Zhou

Published in: Experimental Mechanics | Issue 4/2024

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Abstract

Background

For the static loading test in the aerospace field, conventional strain field reconstruction methods relying on finite element analysis (FEA) or test data are difficult to meet the accuracy requirements of test monitoring.

Objective

This study aims to construct a high-accuracy strain field for real-time test monitoring.

Methods

An improved strain field reconstruction method based on digital twin (DT) named as DT-SFRM is proposed. The DT is built by data fusion of FEA results and test data, which combines the benefits of these data. The FEA conducted before formal test provides approximate strain field distribution, and the strain gauges data with high accuracy are used to modify FEA strain fields in real time. After that, the real-time DT is used to determine the possible risk regions of test articles. Finally, a large opening cylindrical shell (LOCS) buckling test is conducted to validate the advantages of DT-SFRM.

Results

Results show that the accuracy of DT-SFRM is much higher and less affected by the nonlinearity of test data than that of conventional methods. Compared with the time cost by conventional real-time FEA (about 50 min), the DT method only takes 9s to reconstruct strain field, and the possible risk regions predicted by DT-SFRM are more consistent with test buckling regions of LOCS than conventional methods.

Conclusions

The DT-SFRM is validated to have a higher accuracy and better monitoring effect, and it is more suitable for test monitoring of complex structures.

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Title: An Improved Strain Field Reconstruction Method Based on Digital Twin for Test Monitoring
Authors: B. Wang
X. Ke
Z. Song
K. Du
X. Bi
P. Hao
C. Zhou
Publication date: 06-03-2024
Publisher: Springer US
Published in: Experimental Mechanics / Issue 4/2024
Print ISSN: 0014-4851
Electronic ISSN: 1741-2765
DOI: https://doi.org/10.1007/s11340-024-01035-3

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Abstract

Background

Objective

Methods

Results

Conclusions

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