Mooring ropes are essential components of ships and offshore floating structures and they are subjected to cyclic axial loads. This study investigates the evolution of the full-cycle stiffness of fibre polyester ropes under long-term static and dynamic loading. First, the static stiffness characteristics of the ropes, including the rope elongation properties at different stages, shrinkage rates, and creep coefficients after an idle period, are examined under static loads; an empirical formula for static stiffness is established. Second, the dynamic stiffness characteristics of the ropes are investigated under cyclic loads that are typical of platform production operations. The stabilities of the structure under different tensions are compared; the effects of mean tension, tension amplitude, and load cycle on the dynamic stiffness of the ropes are analysed and an empirical formula is established to predict the dynamic stiffness during the engineering design phase. The results of this study can be helpful for the rational design of deep-sea taut-leg mooring systems because they present the evolution of the full-cycle stiffness characteristics of mooring ropes.

Traditional steel wires are being replaced with mooring ropes made from synthetic fibers in offshore industries. However, under the long-term influence of a wave load, the dynamic response of a rope is extremely complex. This paper presents a comprehensive experimental study of the nonlinear dynamic stiffness of polyester fiber mooring ropes under cyclic loading. The stress–strain characteristics of the fiber ropes were investigated based on experimental data. Moreover, cyclic load tests with different mean loads, load amplitudes, and cycles were conducted to study the effects of these three parameters on dynamic stiffness. In previous studies, the dynamic stiffness of fiber ropes was estimated using empirical equations. In this study, the self-learning ability of a radial basis function (RBF) neural network was used to predict the dynamic stiffness of polyester fiber ropes. The predicted results were compared with the empirical formula calculations and experimental measurement results. The RBF neural network has better prediction results than the empirical formulation. In addition, the autonomic learning of the neural network was better when the sample data were in a disordered input state than in an ordered input state. The research findings of this study provide new approaches for solving the dynamic stiffness of ropes and lay a theoretical foundation for the mechanical analysis in the engineering design stage of mooring systems.

在列举分析现行纺织品燃烧性能测试标准的基础上,探究绳索耐火性测试方法及标准制定依据,明确区分阻燃和耐火测试的显著差异,扫除绳索耐火测试盲点.提出绳索耐火测试不仅要用表观指标如续燃时间、阴燃时间、易点燃性、燃烧速率等表征绳索的燃烧性能,还要依据其具体用途测试绳索点燃时和点燃后的力学性能等耐火指标,以便能更深入有效地表征力学损失状况,评判绳索的安全性与可用性.

Yarns of fiber assemblies such as ropes would abrade with each other during repeated stretching or bending. The yarn on yarn abrasion failure is a main reason for the final assembly failure as the result of the relative movement to each other. To explore the influencing factors and failure mechanism, this work, taking the Ultra High Molecular Weight Polyethylene Fiber (UHMWPE) as the research object, discussed the influences of abrading frequency and the yarn tension on its abrasion life. Based on the observation and analysis of the rising temperatures from abrasion, the abrasion fragments, and morphology of failed yarns, the heating failure and crack propagation mechanisms were proposed, which provide insights into a variety of UHMWPE product designs and applications.

Intelligent/smart concepts have been adapted in many industrial fields including the variety of marine applications. With the increasing needs, people started to investigate smart rope (and nettings) systems with embedding sensors for real time monitoring, life prediction, retirement criteria and safer operation, etc. In this study, as the first one of a series papers in the subject of smart cordage systems, we report our efforts to investigate using RFID innovatively as the tool for identification, monitoring and diagnostic purposes, as well as our findings of its limitations in ropes.