Comparative Assessment of Hyperelastic Models for Simulating Mechanical Hysteresis in Synthetic Yarns for Offshore Mooring

Synthetic fibers are widely employed in offshore mooring systems due to their high specific strength, low weight, corrosion resistance, and favorable fatigue performance. However, their mechanical response under cyclic loading is complex, involving nonlinear elasticity, viscoelasticity, energy dissipation, stiffness evolution, and load-history dependence. Reliable constitutive models are therefore essential for predictive analysis and design. This study presents a systematic comparative assessment of phenomenological hyperelastic strain energy models for simulating mechanical hysteresis in synthetic yarns. The constitutive framework combines finite deformations, incompressibility, and viscoelastic memory through an internal variable, with model parameters identified by constrained numerical optimization using stabilized experimental hysteresis curves as reference data. Seven fiber classes were investigated: aramid, high-modulus polyethylene (HMPE), low-creep HMPE, liquid crystal polymer (LCP), polyamide (PA), polyester (PET), and high-elongation polyester (HE PET). Twenty strain energy models were evaluated under the same computational framework, resulting in 140 material-model combinations. The simulations reproduced the experimental hysteresis responses with good overall consistency, yielding a global mean error of 3.325%. Stumpf-Marczak provided the best global performance, followed by Davies-De-Thomas, Yeoh Modified, and Bechir-Boufala-Chevalier. Aramid and LCP presented the lowest material mean errors, whereas PA exhibited the highest, associated with its more complex stress–strain curvature changes during cycling. The results demonstrate that model selection depends on the material response and that greater mathematical complexity does not necessarily ensure better fitting. The study provides an exploratory comparative baseline for constitutive model selection and highlights the need to balance fitting accuracy, numerical robustness, and computational effort in offshore mooring applications.