Fatigue damage prediction is essential for safety of contemporary offshore energy industrial projects, like offshore wind turbines, that are to be designed for sufficiently long operational period of time, with minimal operational disruptions. Offshore structures being designed to withstand environmental loadings due to winds and waves. Due to accumulated fatigue damage, offshore wind floating turbines may develop material cracks in their critical locations sooner than expected. Dataset needed for an accurate assessment of fatigue damage may be produced by either extensive numerical modeling, or direct measurements. However, in reality, temporal length of the underlying dataset being typically too short to provide an accurate calculation of direct fatigue damage and fatigue life. Hence, the objective of this work is to contribute to the development of novel fatigue assessment methods, making better use of limited underlying dataset. In this study, in-situ environmental conditions were incorporated to assess offshore FWT tower base stresses; then structural cumulative fatigue damage has been assessed. Novel deconvolution extrapolation method has been introduced in this study, and it was shown to be able to accurately predict long-term fatigue damage. The latter technique was validated, using artificially reduced dataset, and resulted in fatigue damage that was shown to be close to the damage, calculated from the full original underlying dataset. Recommended method has been shown to utilize available dataset much more efficiently, compared to direct fatigue estimation. Accurate fatigue assessment of offshore wind turbine structural characteristics is essential for structural reliability, design, and operational safety.

Wind turbines are designed to withstand extreme wind- and wave-induced loads, hence a reliability study is vital. This study presents a bivariate reliability approach, suitable for accurate assessment of critical forces and moments, occurring within the wind turbine’s critical mechanical parts, such as the drivetrain. A ecently developed bivariate modified Weibull method has been utilized in this study. Multivariate statistical analysis is more appropriate than a univariate one, as it accounts for cross-correlations between different system components. This study employed a bivariate modified Weibull method to estimate extreme operational loads acting on a 10-mega watt (MW) semi-submersible type floating wind turbine (FWT). Longitudinal, bending, twisting, and cyclic loads being among typical load types that FWTs and associated parts are susceptible to. Furthermore, environmental loads acting on an operating FWT being impacted by incoming wind’s stochastic behavior in terms of wind speed, direction, shear, vorticity, necessitates accurate nonlinear extreme load analysis for FWT critical parts such as the drivetrain. Appropriate numerical methods were used in this study to model dynamic, structural, aerodynamic, and control aspects of the FWT system. Bending moments acting on the FWT drivetrain have been obtained from SIMPACK (Multibody Simulation Method), given realistic in-situ environmental conditions. For a 5-year return period of interest, a bivariate modified Weibull method offered robust assessment of FWT’s coupled drivetrain’s bending moments.

Modern offshore and onshore green energy engineering includes energy harvesting—as a result, extensive experimental investigations, as well as safety and reliability analysis are crucial for design and engineering. For this study, several wind-tunnel experiments under realistic in situ wind speed conditions have been conducted to examine the performance of galloping energy harvester. Next, a novel structural reliability approach is presented here that is especially well suited for multi-dimensional energy harvesting systems that have been either numerically simulated or analog observed during the representative time lapse, yielding an ergodic system time record. As demonstrated in this study, the advocated methodology may be used for risk assessment of dynamic system structural damage or failure. Furthermore, traditional reliability methodologies dealing with time series do not easily cope with the system’s high dimensionality, along with nonlinear cross-correlations between the system’s components. This study’s objective was to assess state-of-the-art reliability method, allowing efficient extraction of relevant statistical information, even from a limited underlying dataset. The methodology described in this study aims to assist designers when assessing nonlinear multidimensional dynamic energy harvesting system’s failure and hazard risks.

Wind turbines and their associated parts are subjected to cyclical loads, such as bending, torque, longitudinal stresses, and twisting moments. The novel spatiotemporal reliability technique described in this research is especially useful for high-dimensional structural systems that are either measured or numerically simulated during representative observational time span. As this study demonstrates, it is possible to predict risks of dynamic system failure or damage given the in situ environmental load pattern. As an engineering example for this reliability, the authors have chosen 10-MW floating wind turbines and their dynamic responses, under environmental loadings, caused by wind and waves. The aim of this study was to benchmark a state-of-the-art approach suitable for the reliable study of offshore wind turbines. Existing reliability methods do not easily cope with dynamic system high dimensionality. The advocated reliability technique enables accurate and efficient assessment of dynamic system failure probability, accounting for system nonlinearities and high dimensionality as well as cross-correlations between different system components.

As marine renewable energy technologies developing, there is a growing need for energy transportation systems. During offshore operations, deep sea risers can be subjected to excessive environmental loadings, causing operational risks. In this study, hydrodynamic loads, caused by in situ sea currents, acting on a riser under real-world sea conditions were modelled and examined, with experimental data being used as a calibration tool. Major safety problems for various offshore energy systems being an accurate assessment of excessive riser external loads, under influence of local sea currents, and hence resulting vortex induced vibrations (VIV).The method outlined in this study may be applied to complex sustainable energy systems, that are exposed to environmental loads, throughout the whole period of their intended service life. Approach advocated in this study offers practical way to estimate failure risks for nonlinear multidimensional dynamic offshore riser systems in an easy yet accurate manner.With regard to defense technology, risers and umbilicals play an important role for modern submarine operations.