Novel multivariate design concept for floating wind turbines by Gaidai multivariate reliability method and deconvolution scheme

Development of novel risk and reliability assessment methods is intended to support safer construction of offshore structures, subjected to environmental wave loads. Current study investigated 10-MW FWT (i.e., Floating Wind Turbine), operating under realistic environmental conditions. While increasing operating safety, enhanced risk and reliability assessment methods may eventually help reduce manufacturing and maintenance costs. Excessive structural dynamics being usually caused by environmental stressors, acting on structural system. Environmental loads resulting from ambient wind and wave motions are typical for offshore structures. Current work advocates a novel risk and reliability assessment methodology that allows for reliable forecasting of failure/damage risks, arising from excessive FWT structural dynamics. Recently developed Gaidai multivariate reliability methodology along with state-of-the-art deconvolution method had been employed. Unlike existing reliability approaches such as Weibull-type, GP (i.e., Generalized Pareto), POT (i.e., Peaks Over the Threshold), etc., the recommended methodology does not rely on any pre-assumed functional class, when extrapolating failure probability functional tail. Practical advantages of the suggested multivariate reliability methodology combined with deconvolution scheme over, that is, 4-parameter Weibull’s extrapolation method had been demonstrated. Suggested methodology makes effective use of even limited underlying datasets, enabling robust and accurate projections of multidimensional structural system failure/damage risks. Overall methodological performance suggests that numerically stable and accurate extreme dynamics forecasts for FWT structural bending moments might be obtained, utilizing suggested multivariate reliability methodology. Deconvolution extrapolation approach being more numerically stable than parametric extrapolation techniques, due to its non-parametric nature.