A general digital twin modeling framework for full-field structural strength assessment and prediction

A general digital twin modeling framework that can achieve full-field structural strength assessment and prediction is proposed in this paper, which mainly comprises the physical layer, the simulation layer, the data layer, the model layer, and the service layer. In the framework, two key technologies are proposed. Firstly, to standardize the multi-source heterogeneous simulation data and sensor data, the load-response-coordinate data association model is created, laying the foundation for digital twin modeling. Secondly, the hierarchical multi-source data fusion method is proposed to achieve the fusion of simulation data and sensor data, thereby establishing a digital twin model and realizing high overall precision as well as accurate interpolation at sensor local positions. To demonstrate the effectiveness of the proposed digital twin modeling framework, two experimental studies on the open-hole plate and the hierarchical stiffened plate are carried out. Results indicate that the proposed framework can establish a high-precision digital twin model that ensures real-time monitoring and obtains better prediction and decision-making for the loading control, and provides an intelligent solution for full-field structural strength assessment and prediction.