AI Driven TTS Procedure to Predict Sailcloth Mechanical Behaviour for Wind Assisted Propulsion in Marine Navigation

Abstract

[Abstract]: The shipping industry is transitioning towards more sustainable propulsion strategies due to increasingly stringent greenhouse gas regulations, with wind-assisted propulsion emerging as a viable near-term solution to improve energy efficiency, reduce emissions and boost blue economy. However, the long-term mechanical performance of sail materials under combined environmental and mechanical degradation remains insufficiently understood. This study investigates the effects of marine exposure and mechanical abrasion on the dynamic and creep behaviour of Dacron sailcloth. Dynamic Mechanical Analysis (DMA) shows that marine ageing reduces the storage modulus by up to 17.7% (weft) and 11.2% (warp), while abrasion causes a reduction of 8.1%, with no significant changes in glass transition temperature. Short-term creep tests (30–100 °C) are extended using the Time–Temperature Superposition (TTS) principle to construct creep compliance master curves at 30 °C, revealing a clear increase in compliance for aged materials. A Generalised Additive Model (GAM) is applied to capture nonlinear behaviour and quantify the influence of predictors, showing excellent agreement (R² > 0.99 for individual curves; R² = 0.933 for the global model). The results indicate that creep compliance increases significantly with ageing, with marine exposure producing a vertical shift of the master curve and abrasion inducing a horizontal shift, suggesting increased chain mobility. Time (83.41%) and ageing level (10.63%) are identified as the dominant factors governing deformation, while fabric orientation confirms orthotropic behaviour, with lower compliance in the weft direction. The combination of TTS and GAM provides a robust framework for predicting long-term sailcloth performance within the validated time domain (∼10⁸ s), although extrapolation beyond this range should be treated with caution.