Extreme buffeting response of long-span bridges under probabilistic wind field: Environmental contours vs. brute-force Monte Carlo approaches

ABSTRACT 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.

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

Existing guidelines recommend the use of the Environmental Contours method to estimate site-specific extreme environmental conditions, which critically influence both the operational efficiency and structural integrity of Floating Offshore Wind Turbines (FOWTs). While different approaches for computing such contours have been proposed in previous literature, the major source of uncertainty is still represented by the extreme data selection. In this respect, this study extracts Environmental Contour surfaces at four locations in the Mediterranean Sea relying on three different approaches, i.e., annual maxima, peak over threshold, and complete dataset, for environmental parameters representing the primary loading sources, namely hourly mean wind speed (Uw), significant wave height (Hs), and peak period (Tp). Results across all sites indicate that Hs peaks on the contour surfaces are overestimated when using the entire dataset compared to selecting only extreme values, underscoring the influence of the marginal distribution on environmental contour computations. Finally, the comparison of excitation levels across different sites supports the calibration of FOWT designs specifically for the Mediterranean Sea, as existing design criteria and recommendations are primarily tailored to the more severe conditions of the North Sea and the Atlantic Ocean. This approach offers potential cost-reduction benefits, aiding decision-making in a region with a high demand for renewable energy.

Environmental contour correction using Bayesian inference for areas with limited metocean data

Design sea state assessment via IFORM-based environmental contour approach derived with Gaussian mixture model

Reliable estimation of extreme waves is essential for offshore wind turbine system design; however, site-specific conditions limit the application of one-size-fits-all statistical methods. We analyzed extreme wave conditions at potential offshore wind farm sites in South Korea using high-resolution hindcast data (1979–2022) based on the Weather Research and Forecasting (WRF) model. While previous studies have typically relied on a limited combination of distribution types and parameter estimation methods, this study systematically applied various Weibull distribution models and parameter estimation techniques to the environmental contour (EC) method. The results show that the optimal statistical approach varied by site according to the tail characteristics of the wave height distribution. The inverse second-order reliability method (I-SORM) provided the highest accuracy in regions with rapidly decaying tails, achieving root mean square error (RMSE) values of 0.21 in Shinan (using the three-parameter Weibull distribution with maximum likelihood estimation, MLE) and 0.34 in Chujado (with the method of moments, MOM). In contrast, the inverse first-order reliability method (I-FORM) yielded superior performance in areas where the tail decays more gradually, such as Yokjido (RMSE = 0.47 with MLE using the exponentiated Weibull distribution) and Ulsan (RMSE = 0.29, with MLE using the exponentiated Weibull distribution). These findings underscore the importance of selecting site-specific combinations of statistical models and estimation techniques based on wave distribution characteristics, thereby improving the accuracy and reliability of extreme design wave predictions. The proposed framework can significantly contribute to the establishment of reliable design criteria for offshore wind turbine systems by reflecting region-specific marine environmental conditions.

Organic waste upsurges with rapid urbanization and incessant population growth. To restrain this trend and lessen environmental impacts, sustainable management of food waste imposes an ambitious target of resource recovery and mitigating greenhouse gas emissions. This study quantifies environmental impacts and socio-economic benefits of municipal solid waste (MSW) management as a proof-of-concept case study for the Lima, Peru. Economic feasibility and life cycle assessment studies were used to compare the efficiency of segregated organic waste for scenarios such as landfilling and composting. Resource recovery, GHG emissions, and revenues are assessed to decide the environmental contour and downstream strategies. It was found that composting has less environmental impact in 11 out of 12 life cycle impact categories (ReCiPe method) during collection, processing, and distribution of 1 ton of waste as functional unit. Composting resulted in 40% less climate impact with global warming potential (GWP) of 476.20 kg CO2eq whereas landfilling showed 785.81 kg CO2eq GWP. Additionally, organic waste to compost conversion rate of 13%, can bring positive upshots to economy and society. This study is instrumental in MSW management, decision-making, and mitigate climate change in line with United Nations Sustainable Development Goals (UN-SDGs) for sustainable society.

ABSTRACT The design wave method is widely used in the strength assessment for ships and offshore structures. The present study aims to verify if the design wave method’s design loads are conservative and if the design wave method can be used in the strength assessment of the floating offshore wind turbine platform in the preliminary design phase. An OC4 5MW semi-submersible platform is analysed in the frequency domain to determine the extreme design loads implied by the design wave method. Short-term responses of the integrated system are studied based on time-domain simulations for sea states identified by the environmental contour method for a specified return period. The long-term extreme loads are estimated and compared with those of the design wave method. It has been shown that the extreme responses of the support structure determined by the design wave methods are more conservative than those determined based on time-domain simulations.

A joint distribution of significant wave heights (Hs) and peak wave periods (Tp) in the northwestern South China Sea is created using a conditional distribution model in this work. An unstructured triangular mesh wave model covering the northwestern South China Sea is established based on the third-generation spectral wave model SWAN. This wave model has been extensively validated against field data and was run from 1979 to 2020 to generate long enough one-hourly Hs and Tp. Four probability density functions including Normal, Lognormal, Gamma and 3P Weibull distributions are adopted to construct the marginal independent distribution of Hs. The results show that the 3P Weibull distribution is more suitable in fitting the marginal distribution of Hs compared to the other three distributions. Three combinations of dependence functions (μ and σ), namely, power3 and exp3, insquare2 and asymdecrease3, and logistics4 and alpha3, are used to create the Normal and Lognormal distributions for Tp. The estimations of dependence functions and corresponding fitted Tp demonstrate that the μ and σ using power3 and exp3 perform best in producing the conditional distribution of Tp. In addition, the environmental contours derived by an IFORM are used to generate the extreme sea states with return periods of 1, 5, 10, 25, 50 and 100 years.

Abstract: In the high painted desert of northern Arizona lie the longest continually inhabited settlements in North America. There, below the Hopi Mesas, rows of blue corn emerge from dry farmed fields cultivated for more than one thousand years. Dependent on monsoon storms, snow, and seeds selected by Hopi women across generations, an agricultural system with one of the world’s highest rates of biodiversity thrived well into the twentieth century. At the turn of the twenty-first century, a megadrought amplified by climate change emerged in southwestern North America, marking the driest twenty-two-year period of the past 1200 years. Scant precipitation and intensifying heat created historic drought conditions, limiting the ability of Hopi farmers to produce crops with traditional agricultural methods. Ecological oral history interviews conducted collaboratively with the Hopi Tribe over a fifteen-year period reveal the impact of climate change on Hopi agriculture, biodiversity, and food security during the first two decades of the twenty-first century. As archives of the environment and repositories of ecological memory, Hopi oral histories of seeds and snow invite us to explore how environmental historians can effectively collaborate with communities to document the cultural, social, and environmental contours of the climate crisis.

Abstract The long-term responses of offshore wind turbines (OWTs) are critical in the design phase, where precise assessments ensure structural reliability and operational efficiency. The environmental contour method (ECM) enables efficient analysis of design responses by focusing on a selected set of critical environmental conditions that predominantly drive long-term extreme responses. Despite its extensive use in offshore engineering, ECM’s application in the structural design and strength assessment of OWTs remains underexplored. This study offers a comprehensive overview of the utilization of ECM in the context of OWT design, incorporating a bibliometric analysis of publications from the Web of Science to identify research trends and key topics. The analysis highlights diverse approaches for estimating long-term extreme responses and constructing environmental contours using statistical distributions. Additionally, the study explores the application of ECM and its modified versions in the design and strength assessment of OWTs. Challenges and opportunities associated with ECM implementation in OWTs are critically analyzed, providing insights into ECM’s potential for enhancing the efficiency and reliability of OWT structural design.

During its service life, a deep-sea floating structure is likely to encounter extreme marine disasters. The combined action of wind and wave loads poses a threat to its structural safety. In this study, elliptical copula, Archimedean copula, and vine copula models are employed to depict the intricate dependence structure between wind and waves in a specific sea area of the Shandong Peninsula. Moreover, hourly significant wave height, spectral peak period, and 10 m average wind speed hindcast data from 2004 to 2023 are utilized to explore the joint distribution of multidimensional parameters and environmental design values. The results indicate the following: (1) There exists a significant correlation between wind speed and wave parameters. Among them, the C-vine copula model represents the optimal trivariate joint distribution, followed by the Gaussian copula, while the Frank copula exhibits the poorest fit. (2) Compared with the high-dimensional symmetric copula models, the vine copula model has distinct advantages in describing the dependence structure among several variables. The wave height and period demonstrate upper tail dependence characteristics and follow the Gumbel copula distribution. The optimal joint distribution of wave height and wind speed is the t copula distribution. (3) The identification of extreme environmental parameters based on the joint probability distribution derived from environmental contour lines is more in line with the actual sea conditions. Compared with the design values of independent variables with target return periods, it can significantly reduce engineering costs. In conclusion, the vine copula model can accurately identify the complex dependency characteristics among marine variables, offering scientific support for the reliability-based design of floating structures.

This paper aims to develop a reliability assessment method for monopile offshore wind turbines, focusing on a serviceability limit state wherein the rotation of the pile head should not exceed a specified threshold. The modified environmental contour method is used to estimate the 50-year extreme distribution of the pile head forces. The finite element method is utilized to calculate the pile head rotation, taking into account the pile-soil interaction through the p-y curve method. Given the uncertainties associated with the extreme pile head forces and soil conditions, the First Order Reliability Method (FORM) is applied to evaluate the reliability of the monopile offshore wind turbine. The findings indicate that the short-term extreme pile head forces conform to the generalized extreme value distribution, and that the extreme bending moment and extreme shear force do not necessarily occur simultaneously. For a comprehensive reliability assessment, it is crucial to consider the 50-year extreme bending moment and the corresponding shear force that occur concurrently.

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.

The article substantiates the key role of active implementation of the new paradigm of the digital technological order by domestic high-tech companies in ensuring the technological leadership of the state, allowing to create innovative products and services, increase the efficiency of industrial production, form new skills and competencies of human capital, stimulate the growth of science and education, improve the quality of life of the population, the competitiveness of the national economy. It has been established that the formation of environmental contours for the development of high technologies is a priority of the Russian policy in the field of innovation. The scientific approach to the formation of the strategy for the development of the innovative environment of high-tech industries, based on the presentation of the innovative environment in two contours - internal and external, is substantiated. The author’s methodological tools for grouping industries by four types of strategic behavior (sustainable leadership, alternative breakthrough, strengthening of advantages, radical transformations) are presented. To form a line of strategic behavior in the interests of developing an innovative environment, the article proposes an algorithm for implementing actions.

An ULS reliability-based design method for mooring lines using an efficient full long-term approach

Joint probability analysis and mapping of typhoon-induced wind, wave, and surge hazards along southeast China

Extreme value analysis (EVA) uses data to estimate long-term extreme environmental conditions for variables such as significant wave height and period, for the design of marine structures. Together with models for the short-term evolution of the ocean environment and for wave–structure interaction, EVA provides a basis for full probabilistic design analysis. Alternatively, environmental contours provide an approximate approach to estimating structural integrity, without requiring structural knowledge. These contour methods also exploit statistical models, including EVA, but avoid the need for structural modelling by making what are believed to be conservative assumptions about the shape of the structural failure boundary in the environment space. These assumptions, however, may not always be appropriate, or may lead to unnecessary wasted resources from over design. We demonstrate a methodology for efficient fully probabilistic analysis of structural failure. From this, we estimate the joint conditional probability density of the environment (CDE), given the occurrence of an extreme structural response. We use CDE as a diagnostic to highlight the deficiencies of environmental contour methods for design; none of the IFORM environmental contours considered characterise CDE well for three example structures.

The probabilistic design of offshore wind turbines aims to ensure structural safety in a cost-effective way. This involves conducting structural reliability assessments for different design options and considering different structural responses. There are several structural reliability methods, and this paper will apply and compare different approaches in some simplified case studies. In particular, the well known environmental contour method will be compared to a more novel approach based on sequential sampling and Gaussian processes regression for an ultimate limit state case study on the maximum flapwise blade root bending moment. For one of the case studies, results will also be compared to results from a brute force simulation approach. Interestingly, the comparison is very different from the two case studies. In one of the cases the environmental contours method agrees well with the sequential sampling method but in the other, results vary considerably. Probably, this can be explained by the violation of some of the assumptions associated with the environmental contour approach, i.e. that the short-term variability of the response is large compared to the long-term variability of the environmental conditions. Results from this simple comparison study suggests that the sequential sampling method can be a robust and computationally effective approach for structural reliability assessment.