@article{TIAN2024, 
author = {Kuo TIAN and Zhiyong SUN and Zengcong LI},
title = {High-precision digital twin method for structural static test monitoring},
year = {2024},
journal = {Acta Aeronautica et Astronautica Sinica},
volume = {45},
number = {7},
pages = {429134},
keywords = {digital twin, panel structure, data fusion, stress field monitoring, ensemble learning},
url = {https://www.sciopen.com/article/10.7527/S1000-6893.2023.29134},
doi = {10.7527/S1000-6893.2023.29134},
abstract = {Experimental validation and numerical simulation are two typical methods for evaluating the structural strength. However, the experimental validation method based on sparse sensors is difficult to ensure the coverage of the structural stress monitoring, and the numerical simulation method may lead to the insufficient accuracy of stress results due to the simplification and idealization of physical entities. Therefore, it is a challenging issue to comprehensively utilize the advantages of these two methods of strength evaluation and carry out data fusion to achieve the full-field structural stress monitoring. In this study, the Digital Twin for Structural Static Test Monitoring (DT-SSTM) method is proposed, which can obtain a high-precision digital twin model of structural static test to realize the real-time monitoring of the structural stress fields and the structural strength evaluation. The DT-SSTM method includes two stages: offline and online stages. In the offline stage, the Gradient Boosting Decision Tree (GBDT) algorithm is used to train the simulation data and build a pre-trained model. In the online stage, based on the ensemble learning concept, the Stacking algorithm is used to train the residuals between the response values of the experimental data and the response values of the pre-training model, and then the residual model is established. Multi-source data fusion is carried out by combining the pre-trained model with the residual model to establish a high-precision digital twin model. Finally, the open-hole rectangular plate under axial tension is tested to validate the effectiveness of the DT-SSTM method.Results show that the DT-SSTM method can establish a high-precision digital twin model of the structural static test with higher global prediction accuracy, local prediction accuracy and data fusion efficiency compared with the similar data fusion methods, providing a novel solution for the real-time monitoring of structural stress fields.}
}