Marine Structures最新文献

The number and size of aluminium non-monohull ships have been steadily increasing over time. This raises growing concerns regarding their structural strength, especially considering the adverse effects of the heat-affected-zone (HAZ) on welding connections in aluminium structures. This paper investigates the ultimate strength of welded aluminium stiffened panels under combined biaxial compressive loads and lateral pressure through the application of numerical simulations. Altogether 360 cases are simulated with varied panel lengths, welding patterns and load combinations. The results are presented and discussed with respect to force end-shortening curves, failure modes and ultimate strength. Influences of the combined loads and HAZ effects are summarized. The numerical results are compared to two commonly used design methods in the marine industry, the International Association of Classification Societies (IACS) rule and the Panel Ultimate Limit States (PULS) approach. Their applicability to welded aluminium stiffened panels is discussed, and modifications are suggested with respect to the transverse loads, lateral pressure, and HAZ effects.

In this research, the effect of different water aging effect on the crashworthiness of intraply glass/basalt fiber-reinforced composite pipes made using the wet filament winding process was examined. Using both theoretical and practical methods, the water uptake behavior of pipes with winding angles of ±55° and ±70° was examined in the initial section of the investigation. In the later division of this research, crushing tests for composite samples exposed to hydrothermal aging were performed and compared with their dry state. According to the practical results for the water sorption curves, the group that absorbs the most water was non-hybrid basalt fiber reinforced pipes. However, the presence of glass which absorbs water relatively less in hybrid composites, caused basalt fibers to absorb less water in both water types. In quasi-static axial compression experiments, non-hybrid glass pipes exhibited the highest specific energy absorption compared to others. Aging leading to material degradations resulted with decreases in crashworthiness indicators such as energy absorption, load-bearing capability. However, hybridized pipes having more progressive crushing behavior contributed to fixing crushing stability compared to non-hybrid glass fiber reinforced pipes. Also, the increase in the winding angle, whether dry or aged, showed a decrease in the energy absorption values.

Given the considerable volume of materials used in jacket platforms, structural optimization of these structures is always of interest. In the design optimization of offshore jacket platforms, the objective function is iteratively evaluated through a number of complex and time-consuming analyses making the optimization process computationally expensive. To reduce the computational costs, therefore, it is imperative to investigate efficient optimization algorithms with a high convergence rate to achieve optimal solutions for offshore jacket structures as a large-scale and complex problem. Accordingly, this research studies the application of a novel metaheuristic algorithm called Enhanced Colliding Bodies Optimization (ECBO) for the design optimization of a real jacket platform, SPD19A. The optimization constraints comprise stress and buckling in the members, horizontal displacements at the working point, and structural adequacy control of connections. The optimization results are subsequently compared to a design optimized by the Genetic Algorithm (GA), as an example, to evaluate the efficiency of the ECBO algorithm for the offshore jacket structure. The outcomes indicate that ECBO optimizes the jacket more effectively by 15%, while the optimization ratio of GA is 11%. Hence, the results confirm that ECBO has great and favorable efficiency and can potently escape from local optima to reach a better design for the jacket structure.

Corrosion is considered an important aspect in assessing the integrity of offshore marine structures. It is a process that involves the risk of keeping floating production storage and offloading (FPSO) tanks out of operation for a long time, incurring undue costs for the operator. Additionally, repairs inside tanks take a long time, especially when material purchases, such as certified steel plates, are required. Therefore, operators are interested in being able to accurately predict when structural elements must be repaired. Despite recent efforts to address this problem, accurate modeling of corrosion growth remains a challenge, mainly due to its complexity and inherent uncertainties. This work proposes the use of a regression tree model, which is a well-known machine learning technique, with the purpose of predicting when and what structural elements of FPSO tanks should be repaired. A prediction model was created by learning and testing from a real data set to estimate corrosion loss as a function of the type of structural element, age, and the fluids surrounding it. The Classification and Regression Trees (CART) algorithm was employed. The results show potential application in the material purchase planning process, minimizing the critical inspection and repair path of the FPSO cargo tank, and preventing loss of storage capacity during operation.

Large-diameter steel pipe pile foundations are widely used to support offshore wind turbines subjected to horizontal loads during operation. The pile horizontal displacement must be restricted within a certain value to ensure its safety. Therefore, an accurate prediction of pile horizontal displacement is of great significance. The finite element method (FEM) has been prevailing in such prediction, with its accuracy remaining unquantified. This study compiles a large database containing 959 pile horizontal displacement measurements from 14 offshore wind turbines. Numerical models are then built using FEM to predict horizontal displacements of these piles. A bias defined as the ratio of measured to predicted horizontal displacement is used to quantify the accuracy of the FEM. Results showed that the FEM is moderately risky as it underestimates the pile horizontal displacement by about 40 % on average. The dispersion in prediction accuracy is about 40 % ranked as moderately dispersive. An empirical constant of 1.41 is introduced to the predicted displacement for model calibration, making the prediction unbiased on average. The probability density functions for the biases are characterized as 2-order Gaussian functions. Last, analysis of a monopile from a real project is presented to highlight the significance of the calibrated finite element model.

This study explores the effects of doubler plates and alterations in joint configuration, on the static characteristics of X-joints made from circular hollow sections (CHS) with slender walls, concentrating on how they withstand compressive forces applied to the braces. A detailed finite element analysis (FEA) was launched. Its accuracy verified through experimental tests carried out by the research team and by comparing it with existing studies. The investigation included an extensive parametric analysis (by generating 204 models) to evaluate changes in initial stiffness, load capacity, and modes of failure, with a focus on the importance of interactions between the chord and plates and the impact of geometric and material nonlinearities. Findings revealed that the doubler plates significantly improve the maximum load bearing capacity and failure modes under various joint geometrical scenarios. While the benefits of doubler plates in enhancing the durability of X-joints are clear, their effectiveness under axial load was not studied. Based on these insights, the research introduces a new theoretical design equation, based on yield volume theory and nonlinear regression, to accurately forecast the ultimate load capacity of the joints.

Titanium alloy deep-sea pressure hull suffers from room-temperature creep-fatigue problem. Room-temperature creep damage significantly reduces the fatigue life of titanium alloy deep-sea pressure hull. In order to analyze the room-temperature creep-fatigue failure process of titanium alloy pressure hull, a room-temperature creep-fatigue damage model in three-dimensional stress space is proposed. Numerical simulation of the model is implemented based on the finite element method. The accuracy of the proposed damage model as well as the numerical method is verified by carrying out the creep-fatigue crack propagation test on CT specimen of titanium alloy at room temperature. It is concluded that the proposed damage model and numerical method can accurately describe the room-temperature creep-fatigue failure process of titanium alloy structures. Creep-fatigue hotspot of the cone–cylinder pressure hull is located on the tensile side of the outer wall. The creep damage accumulated during the crack initiation stage needs to be considered when analysing creep-fatigue crack propagation process of pressure hull. The creep-fatigue cracks propagate in the circumferential and radial directions from the point of initiation. In the later stage, the circumferential propagation rate is significantly faster than the radial propagation rate. The crack surface develops into a long stripe.

The empirical correlation equation is developed to predict the correlation between two intensity measures (IMs). Previous studies on empirical correlation equations have primarily relied on onshore ground motion, with limited consideration of offshore ground motion. The equation is not only applicable for selecting ground motion records based on the Generalized Conditional Intensity Measure (GCIM), but also for vector-based IMs probabilistic seismic hazard analysis. This paper is based on K-NET strong ground motions, including 892 offshore and 4033 onshore ground motions. Firstly, the Ground Motion Models (GMMs) for 0.1–10.0 s acceleration response spectra (Sa) and other four categories of IMs (amplitude, duration, frequency-content and accumulative effect) were established. Furthermore, empirical correlations between Sa and other IMs were analyzed based on GMMs and Fisher-z transformation. After conducting thorough research, we established the empirical correlation equations between Sa and other IMs of offshore ground motions, revealing significant differences in the empirical correlation equations based on offshore and onshore ground motions. Therefore, when applying the empirical correlation to the ground motion selection based on GCIM in ocean engineering, it is necessary to establish the empirical correlation equation using offshore ground motions rather than directly applying the empirical correlation equation based on onshore ground motions.

Accurate and fast estimating the residual strength for corroded pressurized pipelines is crucial for integrity management. Owing to harsh marine environments, realistic corrosion defects for offshore pipelines are random and non-uniform, substantially affecting burst failure behaviours. Addressing this point, based on the random field (RF), finite element analysis (FEA) and convolution neural network (CNN), an integrated residual strength assessment model was developed — through coupling RF and FEA, a theoretical-numerical approach was derived to generate random corrosion morphologies of defects (input) and solve the corresponding residual strengths (output), which subsequently constituted the datasets for training and evaluation of the CNN-based prediction models. The results indicate that, mechanical behaviours during the failure development caused by corrosion morphologies were well captured in the developed models, including stress concentration and redistribution, restrictions to hoop tensile and interacting effects. On this basis, the models showed good performance in predicting residual strengths for both isolated and interacting random defects. Furthermore, detailed influences from related factors on model performance were discussed and explained from mechanics and machine learning principles. Besides, for engineering safety designs, the models exhibited promising capabilities in quantifying the probabilistic characteristics of residual strengths, with an improved computation efficiency of over 30, 000 times.

Spherical pressure-resistant shells, as a universal structural component of deep-sea submersibles, provide a safe and normal operating environment for personnel and internal equipment. In the paper it presented and optimized the BP neural network model based on a genetic algorithm (GA) accordingly, and the method and accuracy are validated through by a beam model. Simultaneously focusing on steel spherical shells, the study proposed a dataset that captures the influence of the primary dimension of the shell (radius-to-thickness ratio, R/t) on the critical pressure response. The genetic algorithm is employed to optimize the back propagation (BP) neural network model for predicting critical pressure. The structural reliability is adopted as a design criterion to determinate and optimize the geometric parameters and critical pressure of the spherical shell structure. Finally, an ultra-high-strength steel spherical model is designed, constructed and meanwhile collapse pressure tests are accomplished to verify the accuracy of the presented improved BP neural network model based on the computational reliability method. The results reveal that the machine learning optimization design method proposed in this paper can effectively enhance the accuracy of critical pressure predictions and the precision of reliability assessments for deep-sea spherical shells.