Xiufeng Yue

In recent years, the Arctic region, with its abundant oil and gas resources, has become a new focus of global resource development. However, the complex natural environment, especially the effect of sea ice, poses a serious threat and challenge to the navigation safety. Accordingly, this paper focuses on the navigation risks of drilling ships in five sea areas of the Northeast Passage of the Arctic under the influence of environmental factors. A dynamic Bayesian network structure was established using the Interpretative Structural Model–Bayesian network method. Since some risk elements cannot be directly measured, the combined weight method is adopted to fill the sample data. The navigation risk situations of the five sea areas is analyzed by forward causal reasoning. Through reverse diagnostic reasoning, the main risk factors affecting navigation are obtained, and relevant suggestions are given. This has important implications for improving the ability of accident prevention and emergency handling in practical applications. The model was verified through instance verification based on scenario analysis and model verification based on sample data. The average accuracy rate of the obtained model is 83.4%. The results show that the model has certain validity and practicability in the analysis of navigation risks in Arctic shipping routes.

Flexible risers are subjected to significant tensile loads during manufacturing, installation, and in-service phases, and they experience multiple cyclic tensile loads throughout their entire service life. Whether the armour wires can achieve shakedown under cyclic tensile loads remains an open question to be investigated. In this study, first, the winding process of the tensile armour layers was explored, and the residual stress distribution in the cross-section of the armour wires after the winding process was obtained. Subsequently, a numerical simulation model of the flexible riser that considers residual stress was established based on the ABAQUS 2021 software to study the shakedown behaviour of the flexible riser under cyclic tensile loads. The results show that, during the initial loading–unloading process of the example pipe, the stress in the armour wire cross-section undergoes obvious redistribution. When cyclic loading is applied with a tensile force range of 0–16.1 kN, the armour wire cross-section tends to reach a shakedown state as the number of loading cycles increases. However, when cyclic loading is applied with a tensile force range of 0–30.2 kN, the strain of the armour wire cross-section gradually increases with each loading–unloading cycle, thus exhibiting a ratcheting effect. The cyclic tensile shakedown prediction model proposed in this study can provide a reference for the design of armour layers in deepwater flexible risers.

In the extreme polar marine environment, safety risks pose a significant threat to drilling vessels. By conducting a safety risk assessment, potential hazards can be predicted and identified, thereby significantly reducing the frequency of accidents and promoting the sustained stability of economic activities. This paper investigates a Bayesian-network-based risk assessment model for polar drilling operations. Grey relational analysis was employed to identify the main risk factors. The model is trained using 525 valid incident sample data and is combined with expert knowledge. The accuracy rate is above 88%. Additionally, corresponding decision-making recommendations are provided through sensitivity analysis. The three most sensitive elements to fire nodes are human error, other causes, and equipment damage, with sensitivity coefficients of 0.046, 0.042, and 0.022, respectively. In terms of deck/handrail collision nodes, the highly sensitive elements are related to lifting (totally more than 0.1). For the events that have already transpired, the probabilities of most related nodes are 0.73 and 0.74, both of which are above 0.5, thereby validating the accuracy of forward and backward reasoning. Risk assessments based on Bayesian networks can offer pertinent decision-making recommendations and preventive measures.