Fiber-reinforced flexible pipes are subjected to large axial tension loads in deep-water applications, which may result in the excessive deformation of the pipes. Owing to the anisotropy of the composite materials, accurately describing the tensile behavior of these pipes is difficult. Theoretical, numerical, and experimental methods are employed in this study to investigate the mechanical characteristics of a glass fiber-reinforced unbonded flexible pipe under axial tensile loads. Based on the load–strain relationship of each pipe layer, analytical equations considering the effect of anisotropy and radial deformation are first proposed to calculate the axial tensile stiffness of the pipe. A detailed numerical model is established to simulate the tensile behavior of the pipe. A prototype test is performed on a 4500 mm long sample using a tensile testing machine. The leading roles of outer tensile reinforcement layers in axial tensile capacity are illustrated by the strain energy of the pipe layers obtained by the numerical model. Subsequently, a comparison analysis of the mean fiber direction strains of the selected sections are performed between numerical and experimental results, which validates the numerical model. Additionally, the stress distributions of different pipe layers are discussed based on the results of the numerical analysis. Finally, the comparison of axial tensile stiffness results validates the accuracy of the analytical model considering radial deformation. This study proposes effective theoretical and numerical models to predict the tensile behavior of a fiber-reinforced flexible pipe, which provides useful references for the design and structural analysis of these pipes.

With the increasing severity of climate change, Carbon Capture, Utilization, and Storage (CCUS) technology has become essential for reducing atmospheric CO₂. Marine carbon sequestration, which stores CO₂ in seabed geological structures, offers advantages such as large storage capacity and high stability. Cryogenic hoses are critical for the ship-to-ship transfer of liquid CO₂ from transportation vessels to offshore carbon sequestration platforms, but their design methods and mechanical analysis remain inadequately understood. This study reviews existing cryogenic hose designs, including reinforced corrugated hoses, vacuum-insulated hoses, and composite hoses, to assess their suitability for liquid CO₂ transfer. Based on CO₂’s physicochemical properties, a conceptual composite hose structure is proposed, featuring a double-spring-supported internal composite hose, thermal insulation layer, and outer sheath. Practical recommendations for material selection, corrosion prevention, and monitoring strategies are provided to improve flexibility, pressure resistance, and thermal insulation, enabling reliable long-distance tandem transfer. A mechanical analysis framework is developed to evaluate structural performance under conditions including mechanical loads, thermal stress, and dynamic responses. This manuscript includes an introduction to the background, the methodology for data collection, a review of existing designs, an analysis of CO₂ characteristics, the proposed design methods, the mechanical analysis framework, a discussion of challenges, and the conclusions.

Accurate and rapid thermal load identification based on limited measurement points is crucial for spacecraft on-orbit monitoring. This study proposes a stepwise identification method based on deep learning for identifying structural thermal loads that efficiently map the local responses and overall thermal load of a box structure. To determine the location and magnitude of the thermal load accurately, the proposed method segments a structure into several subregions and applies a cascade of deep learning models to gradually reduce the solution domain. The generalization ability of the model is significantly enhanced by the inclusion of boundary conditions in the deep learning models. In this study, a large simulated dataset was generated by varying the load application position and intensity for each sample. The input variables encompass a small set of structural displacements, while the outputs include parameters related to the thermal load, such as the position and magnitude of the load. Ablation experiments are conducted to validate the effectiveness of this approach. The results show that this method reduces the identification error of the thermal load parameters by more than 45% compared with a single deep learning network. The proposed method holds promise for optimizing the design and analysis of spacecraft structures, contributing to improved performance and reliability in future space missions.

Helical carcass-supported composite flexible cryogenic pipes (HC-FCP) for liquefied natural gas (LNG) are critical components in floating liquefied natural gas (FLNG) storage and offloading systems. The complex cross-sectional structure of HC-FCP must withstand cryogenic temperatures as low as −163 °C, which significantly affects the mechanical properties of the pipe materials. Predicting the temperature distribution within the pipe is essential for evaluating its performance under operational conditions. In this study, a three-dimensional axisymmetric steady-state heat transfer numerical model of HC-FCP is developed, achieving a maximum deviation of only 5.1% when compared to experimental temperature measurements. The temperature field at operational conditions exhibits a gradient distribution along the radial direction and a corrugated distribution along the axial direction. Additionally, the influence of external environmental factors on the pipe's temperature field is analyzed. The results indicate that the temperature difference between the inner and outer surfaces increases with rising ambient temperature. Similarly, the temperature change between the inner and outer surfaces grows as wind speed increases, although the effect of wind speed on the pipe's temperature diminishes at higher wind speeds.