A double-buoy floating breakwater and an oscillating buoy wave energy power converter were coupled into an integrated device. Based on the CFD technology, a numerical water tank was established to calculate and explore the energy capture efficiency and wave-dissipating performance of the integrated device. The results show that the floater motion in the cavity has strong nonlinearity. When the incident wave period is near the natural period of the cavity, the floater has the best energy capture efficiency, and the power generation and energy absorption efficiency can reach 2.4 times that of a single floater. In addition, the wave-dissipating performance of the integrated device is related to the wave steepness and the natural period of the structure. When the incident period is less than the natural period of the structure, the floating breakwater has a better wave dissipating effect on large steep waves; beyond the natural period, the floating breakwater will lose its excellent wave dissipation ability.

The sediment particles mixed in the lubricant of water-lubricated bearing will make the working environment of the bearing worse and reduce the lubrication performance of the bearing. In order to study the elastohydrodynamic lubrication performance of water-lubricated bearings under the condition of lubrication water containing impurities, the multigrid method was used to analyze the effects of different spindle speed variation forms, impurity particle sizes and shapes on the maximum pressure and minimum film thickness of water film. The results were then compared with that in the pure water lubrication condition. It is shown that sine acceleration and cosine deceleration are the most favorable for the lubrication performance of the bearing among the three speed change conditions of the spindle. The existence of impurity particles will increase the maximum pressure of lubricating water film and decrease the minimum film thickness. The larger the size of the impurity particles, the closer the shape is to the circle, the lower the thickness of the lubricating water film formed, and the greater the pressure. The existence of impurity particles will cause the sudden change of pressure, and bubbles will escape from the water film and form cavitation.

Fatigue damage assessment for marine structures subjected to various random environmental loadings is an important issue at the design stage. In many situations, the responses of marine structures present wide-band and non-Gaussian properties. In this paper, a neural network model was developed to predict the fatigue damage caused by wide-band non-Gaussian random processes. Many power spectra with different values of bandwidth parameters, inverse slope of the S-N curve, and skewness and kurtosis of non-Gaussian processes were used to train and validate the developed neural network model. In order to determine the optimal neural network structure, the effects of input neurons, the numbers of hidden layer neutrons and hidden layers on the prediction accuracy were investigated. Through case studies with realistic bimodal spectra, by taking the fatigue damage estimated by time-domain rain-flow counting method as reference, it is demonstrated that the developed neural network model is more accurate and robust than the existing frequency-domain methods for fatigue damage assessment of wide-band non-Gaussian random processes.

The fracture failure of ship structures often results from the combination of low cycle fatigue and cumulative plasticity. In order to study the low cycle fatigue crack growth and plastic zone size of a marine high-strength steel considering cumulative plasticity, the low cycle fatigue crack growth test of a marine high-strength steel CT specimen under tension-tension-cyclic load was conducted according to ASTM E647 standard, and the plastic zone size of crack tip under different crack lengths was measured by DIC method. By introducing Chaboche model, an extended finite element simulation method was established to obtain the plastic zone size at the crack tip for exploring the influence of different load factors on the plastic zone size. Then the effectiveness of the method was verified based on test data. Finally, based on the results of finite element calculation and Irwin model, a prediction model of plastic zone size at crack tip considering cumulative plastic strain was proposed, and a crack growth rate model based on plastic zone size was established.

To cope with the difficulties of fault warning in current engineering practice, such as the challenges of constructing sensitive feature combinations, scarcity of complete fault samples, and inaccurate warning threshold settings, etc., a rolling bearing fault warning method based on Adversarial Autoencoder (AAE) and adaptive threshold was proposed. Firstly, the preprocessed normal sample spectrum data was utilized as AAE training data for autoencoder network and adversarial network training, and the autoencoder reconstruction error was calculated and the coding network was retained; Then, the low-dimensional features obeying the prior distribution was extracted layer by layer using the encoder, and the health indicator was constructed by combining the reconstruction error and similarity measure, and the probability density distribution of the health indicator was fitted based on the beta distribution to determine the threshold adaptively; Finally, the test data was processed by the same steps and compared with the threshold to discriminate abnormalities. The proposed method was verified by using two types of rolling bearing datasets, and the experimental results show that the proposed method has excellent fault warning performance and adaptability, and can realize early warning of weak fault.

Riser deployment is an important step in deepwater drilling, during which the spider is the primary support of the riser system. At the same time, the harsh deepwater environment leads to a high risk of riser deployment. To ensure the safety of riser deployment, firstly, a joint distribution model of environmental parameters was constructed. Then, an intelligent prediction model of structural response based on IAGA-BRNN was determined. Finally, the method of structure reliability assessment for riser deployment was established combining Monte Carlo, and a case study was carried out. Results show that most parameters in the joint distribution model of environment obey Weibull distribution and Beta distribution. The prediction model proposed in this paper performs well in all the prediction indicators, and has a stronger prediction ability compared with the conventional prediction model. The equivalent stress and maximum axial force are the first and secondary limitation factors of the riser deployment. In addition, as the number of hang-off riser increases, the reliability is on the decline, and wave height is the main limiting factor of operational reliability.

An umbilical cable serves as a crucial link between the water surface and subsea units in offshore engineering. In this paper, a model test method for the lateral buckling of an umbilical cable armor layer under bending and compression was proposed, and the model was constrained by a sheath to induce lateral buckling. Model tests were carried out under the conditions of straight and bending radii of 2 m, 1.7 m, and 1.4 m. The accuracy of the proposed model test method was verified by the comparison between the theoretical values and experimental results in previous literature, and comparing with the numerical simulation results of this paper. The experimental study revealed that the critical displacement and critical buckling load for lateral buckling of the umbilical cable armor layer increased as the bending radius decreased, and the location of lateral buckling moved toward the fixed end with decreasing bending radius. For the umbilical cable in a bent and compressed state, lateral buckling of the armor wires with the initial winding position on the concave surface of the bend was more pronounced. The lateral buckling model test method proposed in this paper can be used to study the failure mechanism and mode of the umbilical cable armor layer under lateral buckling and to validate numerical simulation analysis.

For low-frequency line spectrum control in naval and other engineering fields, a nonlinear energy sink vibration isolation system dynamics model considering a flexible foundation was established, and the nonlinear dynamics of the system was analyzed using the harmonic balance method and Runge-Kutta method. The influence of the foundational parameters on the amplitude-frequency characteristics of the system was explored, and the nonlinear dynamical behavior of the system at the resonant frequency was analyzed. The influence law of mass ratio, cubic stiffness, and damping on the damping effect of nonlinear energy sink was explored with vibration power flow as the evaluation parameter. For the computational model analyzed, research shows: when the foundation stiffness is weak, the nonlinear energy sink has a large effect on the amplitude and frequency response characteristics of the equipment; for specific parameters, the state of motion of the base and the equipment at the first order resonant frequency may be different; the nonlinear energy sink has a superior damping effect, reducing the peak power flow to the base by 13.95 dB in this example.

Cavitation is a widely-existing fluid dynamics phenomenon in nature, and its theoretical modeling and numerical simulation methods have long formed an important direction in the field of fluid mechanics. In this paper, the essence of phase transition in cavitation, the mathematical and physical description of cavitation flows, especially the main research results of cavitation models for numerical simulation of cavitation flows are reviewed, providing support for the study of cavitation flow.

Ship motions induced by waves have a significant impact on the efficiency and safety of offshore operations. Real-time prediction of ship motions in the next few seconds plays a crucial role in performing sensitive activities. However, the obvious memory effect of ship motion time series brings certain difficulty to rapid and accurate prediction. Therefore, a real-time framework based on the Long-Short Term Memory (LSTM) neural network model is proposed to predict ship motions in regular and irregular head waves. A 15000 TEU container ship model is employed to illustrate the proposed framework. The numerical implementation and the real-time ship motion prediction in irregular head waves corresponding to the different time scales are carried out based on the container ship model. The related experimental data were employed to verify the numerical simulation results. The results show that the proposed method is more robust than the classical extreme short-term prediction method based on potential flow theory in the prediction of nonlinear ship motions.