摘要
试验验证和数值仿真是评估结构强度的两种典型方法,然而基于离散传感器的试验验证方法难以保证结构应力监测覆盖度,数值仿真方法又因为对物理实体的简化和理想化处理而导致应力结果精度不足,如何综合利用两种强度评估方法的优势并进行数据融合以实现结构应力场监测是一个具有挑战性的问题。提出一种面向结构静力试验监测的数字孪生(DT-SSTM)方法,可获得高精度的结构静力试验数字孪生模型,以实现结构应力场的实时监测与强度评估。DT-SSTM方法包括离线、在线2个阶段。离线阶段,采用梯度提升树(GBDT)算法对仿真数据进行训练,建立预训练模型。在线阶段,基于集成学习理论,采用Stacking算法对试验数据响应值与预训练模型响应值之间的残差进行训练并建立残差模型。通过叠加预训练模型与残差模型实现多源数据融合,建立高精度的数字孪生模型。最后,开展了开口矩形壁板轴向拉伸试验来验证DT-SSTM方法的有效性。结果表明,DT-SSTM方法能够建立高精度的结构静力试验数字孪生模型,且相比同类数据融合方法具有更高的全局、局部预测精度以及融合效率,为结构应力场实时监测提供了一种新颖的解决方案。
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 re⁃sults due to the simplification and idealization of physical entities.Therefore,it is a challenging issue to comprehen⁃sively utilize the advantages of these two methods of strength evaluation and carry out data fusion to achieve the fullfield 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 monitor⁃ing of the structural stress fields and the structural strength evaluation.The DT-SSTM method includes two stages:of⁃fline 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 re⁃sponse 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.Re⁃sults 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.
作者
田阔
孙志勇
李增聪
TIAN Kuo;SUN Zhiyong;LI Zengcong(Department of Engineering Mechanics,State Key Laboratory of Structural Analysis for Industrial Equipment,Dalian University of Technology,Dalian 116024,China)
出处
《航空学报》
EI
CAS
CSCD
北大核心
2024年第7期283-294,共12页
Acta Aeronautica et Astronautica Sinica
基金
国家自然科学基金(11902065)
国家重点研发计划(2022YFB3404700)。
关键词
数字孪生
壁板结构
数据融合
应力场监测
集成学习
digital twin
panel structure
data fusion
stress field monitoring
ensemble learning
