Abstract Synthetic fibers have become fundamental components in offshore mooring systems, particularly for deepwater applications. Among these, high-modulus polyethylene (HMPE) fibers, especially low-creep grades, stand out due to their high tenacity, low density, abrasion resistance, and buoyancy, enabling the design of lighter and more compact mooring lines. Nevertheless, their long-term mechanical behavior under sustained loading, especially creep, remains a critical concern for ensuring operational reliability. This study completes a trilogy of investigations dedicated to the creep behavior of low creep HMPE fibers used in offshore mooring ropes. The first study, presented at OTC Brasil 2023, focused on experimental methods; the second, at Oceans Halifax 2024, developed an analytical model for creep-life prediction. The present work, submitted to OTC Brasil 2025, advances the research through numerical simulation. Building on the prior experimental and analytical findings, this study employs a visco-hyperelastic phenomenological model based on continuum mechanics to simulate creep-rupture behavior. The numerical framework was calibrated using an extensive dataset of 31 rupture curves obtained from two commercial low creep HMPE multifilaments of different origins (European and Chinese), tested under varying load and temperature conditions. The proposed model effectively replicated time-dependent deformation, with low average errors, particularly at higher load levels. Although some deviations were observed at ramp transitions, overall results confirm the robustness and applicability of the model for engineering contexts. Model calibration produced consistent viscoelastic parameters, while phenomenological strain energy coefficients showed limited sensitivity to load and temperature variations. By integrating experimental data, analytical modeling, and numerical simulation, this multi-methodological approach offers a robust framework for accurately predicting the long-term creep behavior of synthetic fibers in offshore applications. This contributes to improved design practices, risk evaluation, and service life estimation for permanent mooring systems. Future developments will explore artificial intelligence techniques, such as neural networks, to detect hidden correlations among modeling parameters and extend numerical analysis to higher structural levels through finite element methods, enabling simulation of the geometric complexities present in complete mooring rope assemblies.
Cruz et al. (Tue,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: