The accurate prediction of short-term platform motions in a real environment is crucial for the safe design, operation, and maintenance of floating offshore wind turbines (FOWTs). Numerical simulations of motions are typically associated with high uncertainties due to abstracted theoretical models, empirical parameters, initial environment parameters, etc. Therefore, it is necessary to integrate other sources of information associated with less uncertainty, e.g., monitoring data, for accurate predictions. In this paper, we propose a probabilistic prediction based on the Bayesian approach that logically integrates motion monitoring data with simulated motion predictions of FOWTs, considering uncertainties in the environment model, structural properties, motion prediction method, monitoring data, etc. The approach consists of constructing a prior probability density function (PDF) of a random variable (which characterizes the largest value of the initial motion response) via numerical simulations and a likelihood function based on platform motion monitoring data and deriving a posterior PDF of the random variable by Bayesian updating. Then, posterior distributions of short-term extreme motion responses are derived using the posterior PDF of the random variable, representing lower uncertainty and improved accuracy. A Metropolis–Hastings algorithm is adopted to obtain PDFs of complex probability distributions. The effectiveness of the approach is demonstrated on a real FOWT platform in Scotland. The proposed probabilistic prediction approach results in posterior distributions of short-term extreme platform motions associated with less uncertainty and higher accuracy, which is attributed to integrating prior knowledge with monitoring data.

In this work, the vortex-induced vibration of a rectangular prism with a side ratio of 4 is numerically investigated using a two-dimensional spectral-element method for several structural damping. The prism is constrained to vibrate only in the cross-flow direction. The vibration is governed by an impinging leading-edge vortex instability, while the trailing-edge vortex between two leading-edge vortices also contributes to the amplitude. The results show that increasing the damping reduces not only the displacement amplitude but also the range of lock-in. It is found that the phase difference between the lift force and motion plays an important role that is related to the added mass. After suppressing the trailing-edge vortex using a splitter plate, the vibration amplitude varies almost linearly with the phase difference. In addition, the essence of the lock-in reduction is that there is a critical phase difference above which lock-in disappears for a certain reduced velocity and the critical phase difference changes linearly with the reduced velocity. A new semi-empirical model based on the phase difference is proposed to predict the vibration amplitude and lock-in regime under different damping values. Rectangular prism experiments with two different side ratios are used to verify the effectiveness of the model.

During urban design activity, it is necessary to emphasize the stage of Urban Design Scheming as a important link and find a reliable method. According to the characteristics of present urban design and scheming, the semantic web method is put forward as a definite and standardization platform for the more and more complex urban design problems. Because the semantic web is suitable to the characteristics of the human mind and computer, it is possible to realize the computer assist design and scheming for the designer. Thus urban design can be guided and construction of digital city can be realized.

Fatigue damage can lead to failures of structural systems. To reduce the failure risk and enhance the reliability of structural systems, inspection and maintenance interventions are required, and it is important to develop an efficient inspection strategy. This study, for the first time, develops a system-level reliability growth model to establish efficient inspection planning. System-level reliability growth is defined as an increase in the percentage of the system reliability index with and without inspection. The probabilistic S-N approach is used to obtain the reliability index without inspection. Moreover, advanced risk analysis and Bayesian inference techniques are used to obtain the reliability index with inspection. The optimal inspection planning is obtained by maximizing system-level reliability growth. This model is applied to an offshore wind farm. The results show that inspection efficiency can be improved by increasing the number of repair objects in response to a ‘detection’ inspection outcome, changing the inspection object for each inspection, and increasing the inspection quality. The maximum system-level reliability growth gained from one additional inspection decreases as the number of inspections increases. This study quantifies the inspection efficiency of offshore wind farms by explicit system-level reliability growth computation, offering valuable insights for promoting sustainable energy solutions.