To investigate the interfacial separation mechanism and mechanical response of bimetallic composite pipes under stratum fault movement, a pipe-soil coupling model under stratum fault displacement was established. The effects of strike-slip fault displacement, liner wall thickness, stratum elastic modulus, and internal pressure on the interfacial separation and mechanical behavior of composite pipes were systematically studied. The results indicate that: the liner deformation increases with fault displacement and elastic modulus, but decreases with increasing liner wall thickness, while internal pressure shows opposite effects on liner deformation at both sides of the fault; the interfacial separation increases with fault displacement, but decreases with wall thickness, stratum elastic modulus, and internal pressure; and the axial strain of the liner increases with fault displacement and elastic modulus, but decreases with increasing wall thickness. Notably, low pressure can reduce compressive strain by 33.33%, while high pressure may increase liner strain.
Fatigue life of high-pressure hydrogen storage vessel with design pressure of 45 MPa and 99.9 MPa was investigated. The feasibility of improving fatigue life with crack grinding strategy was verified. First, fatigue assessment with fracture mechanics and S-N curve method was performed. The result indicates crack grinding is beneficial to the fatigue life of cracked hydrogen storage vessel. Then, slow strain rate tensile test of 4130X steel in 100 MPa hydrogen environment was performed. A fracture criterion that maximum principal stress equals the ultimate strength of 4130X was suggested for failure in 100 MPa hydrogen environment. At last, numerical burst pressure simulation was performed. The results suggested that burst pressure of hydrogen storage vessel with grinding groove was not influenced by crack grinding. The outcome of this work suggests a grinding strategy that repairs immediately upon crack detection for hydrogen storage vessel.
Major equipment plays a vital role in national infrastructure and industrial development, and accurate fatigue load prediction is essential for ensuring its operational safety and service life. This paper proposes a deep learning-based framework for advanced fatigue load prediction by integrating a convolutional neural network (CNN), an Efficient channel attention for deep convolutional neural networks (ECA-net), and a long short-term memory network (LSTM). Using actual strain data collected from a tunnel boring machine (TBM) cutter head-sampled at 200 Hz-the study first preprocesses the raw signals through comparative filtering, with median filtering selected for optimal noise reduction. The preprocessed data are converted into supervised learning samples via sliding windows and standardized before model training. The proposed CNN-ECA-LSTM model extracts high-dimensional temporal features through CNN layers, enhances feature relevance via ECA-net, and captures long-term dependencies using LSTM. A multicondition recognition module is also incorporated to adapt to varying operational states. Hyperparameter tuning is performed systematically, and Smooth L1 loss is adopted to improve robustness against outliers. Experimental results demonstrate that the model achieves a root-mean-square error (RMSE) of 33.2626 and a mean absolute percentage error (MAPE) of 15.37677 in predicting 200 future time steps, with 44.5% of predictions reaching over 90% accuracy. Ablation studies confirm the contributions of each component, showing that the integrated model outperforms standalone CNN, LSTM, and CNN-LSTM variants. The study confirms that the proposed approach effectively predicts strain-based load trends, providing a reliable basis for fatigue load spectrum construction and remaining useful life estimation of major mechanical equipment.
The unique operational mode of coiled tubing (CT) inevitably subjects it to bending and damage, making its collapse strength difficult to predict and making it prone to collapse failure. In this study, the tensile mechanical properties of CT material (CT130 steel) under different prestrains were tested, and a method for calculating the yield strength of CT130 steel after bending was established. The mechanical response of CT during the bending-straightening process was simulated using nonlinear finite element analysis (NLFEA). Calculation models for ovality, eccentricity, and residual stress induced by bending were established and introduced into the collapse strength calculation model as the defect influence factor. Based on the full-wall yield failure principle and the Unified Strength Theory, combined with the methods for calculating the yield strength and the defect influence factors of CT130 steel, a collapse strength calculation model for CT based on four typical yield criteria was constructed. A comparison of the model's calculation results with field data and simulation results shows that the model based on the geometric midline (GM) yield criteria is the most accurate model. This model improves the predictive accuracy of collapse strength and helps reduce the probability of collapse failure in CT.
Due to different material parameters between the cladding layer and the base metal, significant tensile residual stresses still remain within the internal cladding layer after traditional heat treatment, seriously affecting the service safety of pressure vessels. Local rapid cooling heat treatment is an effective method to reduce the tensile residual stress in the cladding layer until a compressive stress state is achieved. In this paper, the influence of local rapid cooling heat treatment process parameters on the effectiveness of residual stress reduction was analyzed. Local rapid cooling heat treatment is more effective in reducing residual stress than conventional heat treatment. As the rapid cooling start temperature increases, the cooling rate increases, and the rapid cooling end temperature decreases, the effectiveness in reducing residual stress within the welding layer of strip clad welding structure improves. A mechanical model clarified the basic mechanism that local rapid cooling heat treatment reduces residual stress in the cladding layer through the contraction effect of the base metal during the cooling process. Theoretical analysis indicated that the control effect is related to the temperature difference of the base metal. And the influence of process parameter changes on the temperature difference magnitude is consistent with the change in control effect, which confirms the validity of the conclusions derived from finite element analysis.
