A framework for 3D direct sampling-based environmental contours of wind, wave, and current using ABKP model and R-vine copula

In recent years, due to the significant advancements in hardware sensors and software technologies, 3D environmental point cloud modeling has gradually been applied in the automation industry, autonomous vehicles, and construction engineering. With the high-precision measurements of 3D LiDAR, its point clouds can clearly reflect the geometric structure and features of the environment, thus enabling the creation of high-density 3D environmental point cloud models. However, due to the enormous quantity of high-density 3D point clouds, storing and processing these 3D data requires a considerable amount of memory and computing time. In light of this, this paper proposes a real-time 3D point cloud environmental contour modeling technique. The study uses the point cloud distribution from the 3D LiDAR body frame point cloud to establish structured edge features, thereby creating a 3D environmental contour point cloud map. Additionally, unstable objects such as vehicles will appear during the mapping process; these specific objects will be regarded as not part of the stable environmental model in this study. To address this issue, the study will further remove these objects from the 3D point cloud through image recognition and LiDAR heterogeneous matching, resulting in a higher quality 3D environmental contour point cloud map. This 3D environmental contour point cloud not only retains the recognizability of the environmental structure but also solves the problems of massive data storage and processing. Moreover, the method proposed in this study can achieve real-time realization without requiring the 3D point cloud to be organized in a structured order, making it applicable to unorganized 3D point cloud LiDAR sensors. Finally, the feasibility of the proposed method in practical applications is also verified through actual experimental data.

When the long term behaviour of a floating unit is assessed, the environmental contour concept is often applied together with IFORM (Inverse First Order Reliability Method). This approach avoids direct computation on all sea-states, which is computationally very demanding, and most often simply not feasible. Instead, only a few conditions (the contour) are assessed and results in an accurate estimate of the long term extreme. However, most of available methods to derive the contour require the knowledge of the joint distribution of the different random variables (waves, wind, current...), which is often difficult to derive accurately. In fact, some complex dependences exist and are attempted to be simplified in too few coefficients. Another limitation of current environmental contour is its difficulty to deal with the dependence issue. Indeed, extreme sea-states arise by groups (storms, hurricanes...) and are not independent. While de-clustering techniques exist and are quite straightforward in univariate problems, this becomes difficult when the number of dimension increases. In an attempt to tackle those challenges, this paper presents a novel approach to derive IFORM contours. The method does not require any joint distribution and makes use of much more degrees of freedom to capture the dependence between variables. It also allows for an easy de-clustering. The approach is illustrated on two locations, using actual hindcast data of significant wave height and period; the resulting contours are compared to the ones obtained with more traditional methods.

Extreme responses of sea-crossing bridges subjected to offshore ground motion and correlated extreme wind and wave

Directional effects of correlated wind and waves on the dynamic response of long-span sea-crossing bridges

Long span sea-crossing bridges are often slender and sensitive to wind and wave loads. Nonlinear dynamic response analysis of the bridges under three-dimensional (3D) correlated wind and wave loads is performed in this study in consideration of both geometric and aerodynamic nonlinearities. An optimized C-vine copula is first used to construct a 3D joint probability distribution and environmental contour of mean wind speed, significant wave height and peak wave period. Multi-point fluctuating wind loads with Davenport coherence function and random wave loads with pile group effect are then determined using wind and wave spectra respectively. The nonlinear wind-wave-bridge system considering geometric and aerodynamic nonlinearities is solved by the Newmark-β method with the 3D correlated wind and wave parameters as an input. The proposed approach is finally applied to a real sea-crossing cable-stayed bridge with the measured wind and wave data. The results show that the nonlinear response of the bridge is higher than its linear response with the same input. The bridge response is significantly reduced if the 3D correlated wind and wave loads other than conventional uncorrelated wind and wave loads are considered.

Efficient and resilient design of tidal turbines requires knowledge of the environmental conditions which they will be exposed to over the course of their design life. Several sites in the English Channel have been identified by technology developers for potential deployments of tidal farms. These sites are exposed to strong tidal currents and large wave conditions. At sites of interest for tidal energy extraction, the largest currents are primarily driven by astronomical forcing and can be predicted from harmonic analysis of relatively short datasets. In contrast, wave conditions are stochastic in nature and require long hindcasts to accurately estimate extreme conditions. Moreover, at sites relevant to tidal energy, currents have a significant influence on the wave conditions. This necessitates that extremes of waves and currents are assessed using joint probabilistic models, in order to specify combinations of waves and currents to be used in the structural design of tidal turbines. In this work we use a coupled wave-current model of the English Channel to create a 31-year hindcast of conditions. We examine the joint distribution of wave and current conditions for tidal energy sites near the Isle of Wight (UK) and in the Alderney Race, off the coast of France. We construct 3D environmental contours of current speed, significant wave height and relative direction between the waves and currents. It is shown that the largest waves occur when waves and currents are in opposing directions. The directional misalignment between waves and currents is examined and the potential impact that this may have on the design of tidal turbines is discussed.

In order to forecast the extreme dynamic responses of a floating wind turbine, this paper proposes a novel strategy using a transformation KDE (Kernel Density Estimation) method. Through fits to the probability distribution tails of a measured wave dataset at the National Data Buoy Center station 46061, we have found that our new transformation KDE method will increase the accuracy and reliability of 50-year environmental contour lines. This is due to its superior performance over the conventional KDE technique as well as the three-parameter Weibull probability distribution. In this study, the IEA 15 MW floating wind turbine’s dynamic responses are then calculated for the following 50 years. Compared to the current contour method, our novel contour methodology outperforms it by 168% in calculating the 50-year extreme rolling angle value based on the extreme sea conditions. These findings have important implications for the development of safer floating wind turbines.