Understanding the interaction between erosion and corrosion of pipeline steel in acid solution of different pH

Special Relativity Search for applied mechanics and engineering

Experimental measurement and numerical simulation are two typical methods to monitor the strength variation of structures. However, the former method is difficult to lay sufficient sensors on structures with large sizes or complex curved surfaces, and the latter one suffers from low prediction accuracy due to the simplification and idealization of the physical entity. How to efficiently utilize and combine these two kinds of methods remains a challenging problem. In this study, a novel digital twin modeling method via transfer learning-based multi-source data fusion (DTM-TL-MSDF) is proposed to make full use of the experiment data and the simulation data, aiming to establish an accurate digital twin model for structural strength monitoring in real-time. In the off-line stage, the clustering algorithm is used to pre-process the huge simulation data to relieve the computational burden, and the deep neural network (DNN) model is then pre-trained using the pre-processed simulation data. In the on-line stage, the pre-trained DNN model is fine-tuned using the experimental data to carry out transfer learning. Moreover, the bagging algorithm is employed in the fine-tuning process to improve the robustness and prediction accuracy due to its ability to address the dataset with only a small number of training points. To illustrate the effectiveness of the DTM-TL-MSDF method, a one-dimensional test function and an experimental study of a rectangular plate with hole under axial tension are studied. Results indicate that the DTM-TL-MSDF method can build an accurate digital twin model by integrating the simulation data and the experimental data with excellent global and local accuracy, providing a novel solution to monitor the variations of the structural full-field strength in real-time. 

Digital twin modeling for structural strength monitoring via transfer learning-based multi-source data fusion

Integrated uncertain optimal design strategy for truss configuration and attitude–vibration control in rigid–flexible coupling structure with interval uncertainties

The supply-demand-based optimization (SDO) is among the recent stochastic approaches that have proven its capability in solving challenging engineering tasks. Owing to the non-linearity and complexity of the real-world IEEE optimal power flow (OPF) in modern power system issues and like the existing algorithms, the SDO optimizer necessitates some enhancement to satisfy the required OPF characteristics integrating hybrid wind and solar powers. Thus, a SDO variant namely leader supply-demand-based optimization (LSDO) is proposed in this research. The LSDO is suggested to improve the exploration based on the simultaneous crossover and mutation mechanisms and thereby reduce the probability of trapping in local optima. The LSDO effectiveness has been first tested on 23 benchmark functions and has been assessed through a comparison with well-regarded state-of-the-art competitors. Afterward, Three well-known constrained IEEE 30, 57, and 118-bus test systems incorporating both wind and solar power sources were investigated in order to authenticate the performance of the LSDO considering a constraint handling technique called superiority of feasible solutions (SF). The statistical outcomes reveal that the LSDO offers promising competitive results not only for its first version but also for the other competitors. 

A leader supply-demand-based optimization for large scale optimal power flow problem considering renewable energy generations.

On-line transfer learning for multi-fidelity data fusion with ensemble of deep neural networks

A data-driven modelling and optimization framework for variable-thickness integrally stiffened shells

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.

/pdf/digital-twin-modeling-method-for-hierarchical-stiffened-24ihu1hy.pdf

Digital Twin Modeling Method for Hierarchical Stiffened Plate Based on Transfer Learning

Efficient buckling analysis and optimization method for rotationally periodic stiffened shells accelerated by Bloch wave method

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