As the key load-bearing component of spacecraft, the strength evaluation of stiffened plate structures faces two challenges. On the one hand, the simulation results are sometimes inaccurate, due to the simplification of the true loading conditions and modeling details. On the other hand, data from the sensors cannot provide the full-field strength information of the structure, which may result in the misjudgment of the structural state. To this end, a digital twin modeling method of multi-source data fusion based on transfer learning is proposed in this paper. In transfer learning, simulation data and sensor data are utilized as the source dataset and the target dataset, respectively. First, a pre-trained deep neural network (DNN) model is established based on the source dataset. Then, the pre-trained DNN model is fine-tuned based on the target dataset using a lower learning rate and fewer training epochs. Finally, a digital twin model can be built, which is capable of visualizing the full-field strength information of the stiffened plate structure. To verify the effectiveness of the proposed method, an experimental study on a hierarchical stiffened plate is carried out. Compared with the traditional data fusion method, the results verify the high prediction accuracy and efficiency of the proposed method, demonstrating its potential for the strength health monitoring of spacecraft in orbit.
As one of the most complex and critical components of spacecraft, the structural design of the curved cabin door faces two challenges. On the one hand, it is difficult to obtain innovative configurations for the cabin door in the preliminary design stage. On the other hand, the traditional optimization design algorithm is inefficient in the detailed design stage. In this paper, a two-stage intelligent method for the layout design of the curved cabin door is proposed. In the first stage, the innovative stiffener layout of the cabin door is obtained based on the topology optimization method. Then the mesh deformation method is used for rapid modeling and geometric reconstruction. In the second stage, a recently proposed powerful evolutionary algorithm, named elite-driven surrogate-assisted Covariance Matrix Adaptation Evolution Strategy (ES-CMA-ES), is employed to optimize the parameters of the cabin door and its surrounding thin-wall structure. To verify the effectiveness of the proposed method, a curved cabin door example from the spacecraft (cargo spaceship) is carried out. Compared with the traditional orthogrid stiffener design, the mass of the optimal design is reduced by 52.21% while satisfying the constraints, which indicates the excellent optimization ability of the proposed method and demonstrates huge potential for improving the carrying capacity and efficiency of the spacecraft.
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