Cyclic uniaxial stress-controlled ratcheting behaviors of 35CrMo structural steel were investigated at 500 °C. The effects of stressing rates and stress amplitudes were discussed in detail. Results present that the accumulated ratcheting strain increases with the increment of stress amplitude, while decreases with the reduction of stress rate at elevated temperature. Moreover, the ratcheting evolution curves with increasing number of cycles are similar to traditional static creep curves, and can be mainly divided into three stages. Especially, the ratcheting strain rate during the steady stage can be easily obtained to estimate the ratcheting or shakedown behavior. Furthermore, the steady ratcheting strain rate significantly decreases with increasing the stressing rate when the stressing rate is relatively slow. However, when the applied stressing rate is greater than a critical value, such as ≥ 2·5 MPa s⁻¹ for = 200 MPa and ≥ 25 MPa s⁻¹ for σ_max = 500 MPa, the effect of stressing rate has little effect on the cyclic ratcheting strain rate.

Analytical ratchet limits of cylindrical pipelines subjected to several typical cyclic nonproportional loading combinations, such as internal pressure, tension, compression, moment, torsion, and thermal loading in practical engineering, were deduced theoretically according to the noncyclic method. The effects of the axial compression stress and radius ratio on the ratchet limit of structures were investigated intensively. Results revealed that the ratchet limit reduces significantly when axial compression stress is applied, while it clearly increases with the increment of radius ratio. Moreover, several Bree-like diagrams under the corresponding loading conditions of this paper were obtained, which can be used to conveniently estimate the ratchet limit of cylindrical pressure vessels and piping for engineering design.

Aiming at the problem of Transformers lack of local spatial receptive field and discontinuous boundary loss in rotating object detection, in this paper, we propose a Transformer-based high-precision rotating object detection model (RODFormer). Firstly, RODFormer uses a structured transformer architecture to collect feature information of different resolutions to improve the collection range of feature information. Secondly, a new feed-forward network (spatial-FFN) is constructed. Spatial-FFN fuses the local spatial features of 3 × 3 depthwise separable convolutions with the global channel features of multilayer perceptron (MLP) to solve the deficiencies of FFN in local spatial modeling. Finally, based on the space-FFN architecture, a detection head is built using the CIOU-smooth L1 loss function and only returns to the horizontal frame when the rotating frame is close to the horizontal, so as to alleviate the loss discontinuity of the rotating frame. Ablation experiments of RODFormer on the DOTA dataset show that the Transformer-structured module, the spatial-FFN module and the CIOU-smooth L1 loss function module are all effective in improving the detection accuracy of RODFormer. Compared with 12 rotating object detection models on the DOTA dataset, RODFormer has the highest average detection accuracy (up to 75.60%), that is, RODFormer is more competitive in rotating object detection accuracy.