Pipelines constitute a vital component in offshore oil and gas operations, subjected to prolonged exposure to a range of alternating loads. Safeguarding their integrity, particularly through meticulous leak detection, is essential for ensuring safe and reliable operation. Acoustic emission detection emerges as an effective approach for monitoring pipeline leaks, demanding subsequent rigorous data analysis. Traditional analysis techniques like wavelet analysis, empirical mode decomposition (EMD), variational mode decomposition (VMD), and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) often yield results with considerable randomness, adversely affecting leak detection accuracy. This study introduces an enhanced damage recognition methodology, integrating improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and probabilistic neural networks (PNN) for more accurate pipeline leak identification. This novel approach combines laboratory-acquired acoustic emission signals from leaks with ambient noise signals. Application of ICEEMDAN to these composite signals isolates eight intrinsic mode functions (IMFs), with subsequent time–frequency analysis providing insight into their frequency structures and feature vectors. These vectors are then employed to train a PNN, culminating in a robust neural network model tailored for leak detection. Conduct experimental research on pipeline leakage identification, focusing on the local structure of offshore platforms, experimental research validates the superiority of the ICEEMDAN–PNN model over existing methods like EMD, VMD, and CEEMDAN paired with PNN, particularly in terms of stability, anti-interference capabilities, and detection precision. Notably, even amidst integrated noise, the ICEEMDAN–PNN model maintains a remarkable 98% accuracy rate in identifying pipeline leaks.

Wind turbine towers experience complex dynamic loads during actual operation, and these loads are difficult to accurately predict in advance, which may lead to inaccurate structural fatigue and strength assessment during the structural design phase, thereby posing safety risks to the wind turbine tower. However, online monitoring of wind loads has become possible with the development of load identification technology. Therefore, an identification method for wind load exerted on wind turbine towers was developed in this study to estimate the wind loads using structural strain, which can be used for online monitoring of wind loads. The wind loads exerted on the wind turbine tower were simplified into six equivalent concentrated forces on the topside of the tower, and the initial mathematical model for wind load identification was established based on dynamic load identification theory in the frequency domain, in which many candidate sensor locations and directions were considered. Then, the initial mathematical model was expressed as a linear system of equations. A numerical example was used to verify the accuracy and stability of the initial mathematical model for the wind load identification, and the identification results indicate that the initial mathematical model combined with the Moore–Penrose inverse algorithm can provide stable and accurate reconstruction results. However, the initial mathematical model uses too many sensors, which is not conducive to engineering applications. Therefore, D-optimal and C-optimal design methods were used to reduce the dimension of the initial mathematical model and determine the location and direction of strain gauges. The C-optimal design method adopts a direct optimisation search strategy, while the D-optimal design method adopts an indirect optimisation search strategy. Then, four numerical examples of wind load identification show that dimensionality reduction of the mathematical model leads to high accuracy, in which the C-optimal design algorithm provides more robust identification results. Moreover, the fatigue damage calculated based on the load identification wind loads closely approximates that derived from finite element simulation wind load, with a relative error within 6%. Therefore, the load identification method developed in this study offers a pragmatic solution for the accurate acquisition of the actual wind load of a wind turbine tower.

Oil and gas pipelines are the lifelines of the energy market, but due to long-term use and environmental factors, these pipelines are prone to corrosion and leaks. Offshore oil and gas pipeline leaks, in particular, can lead to severe consequences such as platform fires and explosions. Therefore, it is crucial to accurately and swiftly identify oil and gas leaks on offshore platforms. This is of significant importance for improving early warning systems, enhancing maintenance efficiency, and reducing economic losses. Currently, the efficiency of identifying leaks in offshore platform pipelines still needs improvement. To address this, the present study first established an experimental platform to simulate pipeline leaks in a marine environment. Laboratory leakage signal data were collected, and on-site noise data were gathered from the “Liwan 3-1” offshore oil and gas platform. By integrating leakage signals with on-site noise data, this study aimed to closely mimic real-world application scenarios. Subsequently, several neural network-based leakage identification methods were applied to the integrated dataset, including a probabilistic neural network (PNN) combined with time-domain feature extraction, a Backpropagation Neural Network (BPNN) optimized with simulated annealing and particle swarm optimization, and a Long Short-Term Memory Network (LSTM) combined with Mel-Frequency Cepstral Coefficients (MFCC). Corresponding models were constructed, and the effectiveness of leak detection was validated using test sets. Additionally, this paper proposes an improved convolutional neural network (CNN) leakage detection technology named SART-1DCNN. This technology optimizes the network architecture by introducing attention mechanisms, transformer modules, residual blocks, and combining them with Dropout and optimization algorithms, which significantly enhances data recognition accuracy. It achieves a high accuracy rate of 99.44% on the dataset. This work is capable of detecting pipeline leaks with high accuracy.

With the increasing stringency of the Energy Efficiency Design Index (EEDI) requirements, improving the efficiency of the propeller has emerged as a significant challenge in the development of eco-friendly ships. Cavitation inevitably occurs, and it reduces the hydrodynamic performance of the propeller and erodes the blade surface, leading to increased fuel consumption. Therefore, reducing cavitation is crucial for ships to meet the EEDI requirement. This paper investigates the fatigue life and hydrodynamic performance of the propeller under different cavitation numbers and speeds. The relationship between propeller fatigue life and propulsion efficiency under cavitation conditions is explored. In simulation, the Schnerr–Sauer theoretical model is employed as the cavitation model. The nominal stress method (S-N method) is used to calculate the blade fatigue strength. The KP957 propeller is taken as the research object. The hydrodynamic performance of the propellor under different cavitation numbers is studied by means of the finite volume method. The surface pressure and wall shear stress of the blade within the cycle are calculated, and they are conveniently loaded in the dynamic process to calculate the stress and strain of the propeller using the finite element method. Subsequently, the fatigue life of the propeller is determined based on the S-N curve of the blade material. The validity of the study is established by comparing the cavitation results with the experimental results from the Korean Ocean Engineering Research Institute (KORDI) for the KS1295 ship at a speed of 15.7 knots, where the cavitation number in the wake field is 2.5553, and a good consistency is obtained. The findings emphasize the significant impact of cavitation on blade service life and vibration.

The reactor pressure vessel (RPV) in onshore nuclear power plants is typically analysed for fatigue life by considering the temperature, internal pressure, and seismic effects using a simplified time-domain fatigue analysis. In contrast, the frequency-domain fatigue analysis method is commonly employed to assess the fatigue life of ship structures. The RPV of a floating nuclear power plant (FNPP) is subjected to a combination of temperature, internal pressure, and wave loads in the marine environment. Consequently, it is essential to effectively integrate the frequency-domain fatigue analysis method used for hull structures with the time-domain fatigue analysis method for RPVs in FNPPs or, alternatively, to develop a suitable method that effectively accounts for the temperature, internal pressure, and wave loads. In this study, a quasi-time-domain method is proposed for the fatigue analysis of RPVs in FNPPs. In this method, secondary components of marine environmental loads are filtered out using principal component analysis. Subsequently, the stress spectrum induced by waves is transformed into a stress time history. Fatigue stress under the combined influence of temperature, internal pressure, and wave loads is then obtained through a stress component superposition method. Finally, the accuracy of the quasi-time-domain method was validated through three numerical examples. The results indicate that the calculated values obtained by the quasi-time-domain method are slightly higher than those obtained by the traditional time-domain method, with a maximum deviation of no more than 24%. Additionally, the computation time of the quasi-time-domain method is reduced by 98.67% compared to the traditional time-domain method.

The operation of floating nuclear power plants is subject to a number of environmental factors in addition to the typical working temperature and pressure loads. These include marine environmental loads, which can cause fatigue damage and therefore must be taken into account. The fatigue analysis of marine structures frequently employs frequency domain methods, whereas the fatigue analysis of pressure equipment predominantly utilizes time-domain methods. At present, there is no comprehensive and accessible approach for conducting a fatigue analysis of pressure equipment in floating nuclear power plants. In light of the aforementioned considerations, this paper puts forth a novel approach to evaluating fatigue damage based on the principle of damage combination. This article presents a finite element model of pressure equipment and a methodology for calculating the transfer function of such equipment under wave loads. The frequency domain method is employed to calculate the fatigue damage caused by wave loads, with consideration given to both the working temperature and pressure load. The stress time history curve of pressure-bearing equipment is then calculated using the time-domain method. Subsequently, the fatigue damage caused by thermal pressure loads is obtained through a combination of the rainflow counting method and cumulative damage theory, with verification conducted using time-domain calculations. In comparison to alternative damage combination methodologies, the novel approach offers more precise and straightforward damage calculations, with promising potential for integration into engineering design.