The aerodynamic wake effect serves as a governing mechanism in wind farm operation, influencing both the energy harvest and the structural loading of downstream assets through altered flow dynamics. This interaction becomes particularly complex for large-scale floating offshore wind turbines, where wake deficits are coupled with stochastic wind–wave loading and long-term marine corrosion. In such systems, wakes modify the fatigue loading history of turbines, while seawater exposure progressively weakens structural resistance, together leading to highly nonlinear deterioration and failure trajectories that remain poorly understood at the system level. To bridge this gap, this study proposes a multi-physics reliability framework synthesizing aero-hydro-servo-elastic simulations and the Probability density function-informed method. The framework evaluates spatiotemporal reliability under varying climate-change scenarios and spatial correlation assumptions. Results indicate a non-monotonic spatial evolution of asset reliability with a clear mid-array optimum. Wake-induced reduction in mean aerodynamic loading extends the fatigue life of mid-field units by approximately 80%, but farther downstream this benefit becomes limited because reduced aerodynamic damping and continued marine deterioration progressively offset the mechanical relief. Furthermore, the study shows that assuming statistical independence among turbines can significantly distort system-level risk estimates in spatially correlated environments. The findings support a shift beyond energy-centric design paradigms toward reliability-informed asset management for floating offshore wind farms. • Multi-physics framework couples wake with corrosion-fatigue for farm reliability. • Wind farm modelled as a stochastic dynamical system for spatiotemporal reliability. • Reveals a non-monotonic mid-array optimum bounded by degradation penalties. • Quantifies the energy capture and structural longevity trade-off at system level.
Zhang et al. (Fri,) studied this question.
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