Support condition monitoring of monopile-supported offshore wind turbines in layered soil based on model updating

Monopile-supported offshore wind turbines (OWTs) are dynamically sensitive structures whose fundamental frequencies may be close to those of environmental and turbine-related excitations. The changes in fundamental frequencies caused by pile-soil interaction (PSI) may result in unwanted resonance and serious O&M (Operation and Maintenance) issues, which have been identified as major challenges in the research field. Therefore, a novel model updating framework with an implicit objective function is proposed to monitor both the stiffness and damping variation of the OWT system based on the measured vibration characteristics, which is further verified by laboratory tests. In particular, layered soil was considered in the tests to simulate the practical soil conditions of Chinese seas. Different pile lengths were introduced to consider the long-term PSI effects for rigid piles and slender piles. The results showed that the variation in the fundamental frequency is significantly reduced in layered soil compared with the pure sand scenario. For the OWT systems in layered soil, the variation in foundation stiffness is negatively related to the burial depth under cyclic loading. The proposed model updating framework is proven reliable for support condition monitoring of OWT systems in complicated soil conditions.     

Fatigue assessment of ship structures based on equivalent wave probability (EWP) concept (1st report): Proposal of EWP concept and its verification by 8600TEU container ship's onboard hull monitoring

A long-term fatigue assessment method based on EWP concept is proposed. ‘Equivalent wave probability (EWP)’ is the fictitious (H_S, T_m)'s joint probability distribution function (JPDF), for which the frequency distribution of the stress variance R², f(R²), calculated by spectral fatigue assessment agrees with the observed one. By choosing probability function p(R²) to fit f(R²), the R²'s statistical model (R2SM) which represents the relation between the EWP parameters and R²'s population parameters is developed, and the Bayesian inference, which can estimate the EWP parameters from the measured R² data is developed. The EWP at the reference position (RP) can be determined by Bayesian inference from the measured R² through the R2SM at RP. To accurately estimate the measured f(R²) at the target position (TP) from the EWP at RP, an R2SM correction factor at TP, denoted by α_TP, is introduced in the process of assimilating R2SM. The resulting R2SM, which has been assimilated by Bayesian inference using measured data, is referred to as data-assimilated R2SM (DAR2SM). The fatigue assessment using EWP at RP as the input of DAR2SM at TP is called Bayes-EWP-DAR2SM analysis. The validity of Bayes-EWP-DAR2SM analysis is verified by using the long-term (about four years) multi(12)-position hull monitoring (HM) data of an 8,600TEU container ship. The fatigue damages estimated by Bayes-EWP-DAR2SM based solely on the stress history of a single sensor are in agreement with measurements with sufficient accuracy, independent of the chosen data assimilation period. This demonstrates that the multi-position fatigue assessment solely through HM at one RP based on EWP concept is realized.