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Wind energy is foundational for achieving 100 % renewable electricity production, and significant innovation is required as the grid expands and accommodates hybrid plant systems, energy-intensive products such as fuels, and a transitioning transportation sector. The sizable investments required for wind power plant development and integration make the financial and operational risks of change very high in all applications but especially offshore. Dependence on a high level of modeling and simulation accuracy to mitigate risk and ensure operational performance is essential. Therefore, the modeling chain from the large-scale inflow down to the material microstructure, and all the steps in between, needs to predict how the wind turbine system will respond and perform to allow innovative solutions to enter commercial application. Critical unknowns in the design, manufacturing, and operability of future turbine and plant systems are articulated, and recommendations for research action are laid out. This article focuses on the many unknowns that affect the ability to push the frontiers in the design of turbine and plant systems. Modern turbine rotors operate through the entire atmospheric boundary layer, outside the bounds of historic design assumptions, which requires reassessing design processes and approaches. Traditional aerodynamics and aeroelastic modeling approaches are pressing against the limits of applicability for the size and flexibility of future architectures and flow physics fundamentals. Offshore wind turbines have additional motion and hydrodynamic load drivers that are formidable modeling challenges. Uncertainty in turbine wakes complicates structural loading and energy production estimates, both around a single plant and for downstream plants, which requires innovation in plant operations and flow control to achieve full energy capture and load alleviation potential. Opportunities in co-design can bring controls upstream into design optimization if captured in design-level models of the physical phenomena. It is a research challenge to integrate improved materials into the manufacture of ever-larger components while maintaining quality and reducing cost. High-performance computing used in high-fidelity, physics-resolving simulations offer opportunities to improve design tools through artificial intelligence and machine learning, but even the high-fidelity tools are yet to be fully validated. Finally, key actions needed to continue the progress of wind energy technology toward even lower cost and greater functionality are recommended.

Low-frequency vibration in a multi-story wood building is of concern because failure in mitigation of such vibration may lead to occupant discomfort. Ultimately, it may cause damage to their health. Variability in material properties of the members of a wooden floor system significantly influences vibration response characteristics such as eigenfrequencies and mode shapes. In this study, we investigate the uncertainty in low-frequency characteristics of a selected wood floor system, resulting from uncertainty in the members’ material properties. We use a surrogate model developed using polynomial chaos expansion, based on a few training samples computed using a finite element model of an experimental-scale structure comprised of seven load-bearing joists of spruce. We estimate the hyperparameters needed for accurate surrogate model development and, with these, we predict probability distributions for each of the floor-system modal frequencies. Also, we perform local and global sensitivity analyses to assess how material property variation affects the modal frequencies. Results confirm the accuracy and efficiency of the developed surrogate models in prediction of the modal frequencies, resulting from uncertainty in wood material properties. The accuracy of the results is comparable to those based on more expensive Monte Carlo simulation.

A porous-media-tuned liquid damper (PMTLD) can serve as an effective and economical dynamic vibration absorber. Placing porous media within a water tank can improve the capacity for energy dissipation and optimize the performance by varying its material properties. Two numerical models are adopted to simulate the sloshing problem in PMTLD and the dynamics of a floating platform in waves. Besides, the effectiveness of response mitigation can be verified numerically. The first potential-based approach employs a mixed-type boundary value problem (BVP) solver and a free-surface particle tracker. This approach not only simulates the inviscid water wave but also includes the nonlinear damping of the PMTLD via a quadratic Forchheimer term. Another equivalent mechanical model is used to reduce the degree-of-freedom of the PMTLD system. The Newmark method is incorporated to solve the rigid-body dynamics. The second viscous approach uses the finite element method (FEM) to spatially discretize the Navier–Stokes (NS) equations and handles the free surface via the volume of fluid (VOF) and the level set (LS) equations. The multiphase simulation is implemented by computational modeling toolkits, Proteus and Chrono, for the fluid and solid phases, respectively. The correlations between potential flow and two-phase NS models are presented. The PMTLD is designed by analogy with the tuned mass damper (TMD). Numerical results show that the PMTLD can effectively reduce the structure's dynamic response in terms of vibration amplitude around resonance. Such damping devices have great potential for offshore platforms and wind turbine design.

An offshore energy transition, even if only a gradual one, from carbon-emitting fossil fuel extraction to cleaner sources is recommended, if we are to slow the harmful impacts of climate change. The potential for sustainable reuse of decommissioned offshore jacket platforms to support wind turbines is being considered as an attractive proposition in such a transition. To maximize the benefits of such reuse of assets, what is needed is a rational optimization strategy that considers the remaining life of a repurposed platform, associated retrofit and construction costs, and a future period of gross renewable energy generation following installation of the wind turbine. We outline a study that employs a fatigue reliability-based framework, based on the global fatigue approach and Palmgren–Miner’s rule, to aid in such sustainable reuse planning and optimization. The framework proposed identifies an optimized reuse plan that incorporates metocean data analysis, structural analysis, life cycle evaluation, and revenue optimization. We employ a case study and sustainable reuse scenario for a site in the vicinity of Porto (Leixões), Portugal.

In an emerging “blue economy,” the use of large multi-purpose floating platforms in the open ocean is being considered. Such platforms could possibly support a diversified range of commercial activities including energy generation, aquaculture, seabed mining, transport, tourism, and sea-based laboratories. A Markov decision process (MDP) framework is proposed to deal with operations and maintenance (O&M) issues that are inevitable; challenges arise from the complex stochastic weather conditions that need to be accounted for. Using data as well as contrasting synthetic simulations of relevant weather variables, we demonstrate the robustness/versatility of the MDP model. Two case studies—one involving constant and another involving time-dependent downtime costs—are conducted to demonstrate how the proposed MDP framework incorporates weather patterns from available data and can offer optimal policies for distinct metocean conditions (i.e., temporal variations in the weather). A realistic example that illustrates the implementation of the proposed framework for multiple O&M issues involving salmon net pens and wave energy converters demonstrates how our optimal policies can minimize O&M costs and maximize crew safety almost as if the true future were known for scheduling.

The 2030 Sustainable Development Goals (SDGs) offer a blueprint for global peace and prosperity, while conserving natural ecosystems and resources for the planet. However, factors such as climate-induced weather extremes and other High-Impact Low-Probability (HILP) events on their own can devastate lives and livelihoods. When a pandemic affects us, as COVID-19 has, any concurrent hazards interacting with it highlight additional challenges to disaster and emergency management worldwide. Such amplified effects contribute to greater societal and environmental risks, with cross-cutting impacts and exposing inequities. Hence, understanding how a pandemic affects the management of concurrent hazards and HILP is vital in disaster risk reduction practice. This study reviews the contemporary literature and utilizes data from the Emergency Events Database (EM-DAT) to unpack how multiple extreme events have interacted with the coronavirus pandemic and affected the progress in achieving the SDGs. This study is especially urgent, given the multidimensional societal impacts of the COVID-19 pandemic amidst climate change. Results indicate that mainstreaming risk management into development planning can mitigate the adverse effects of disasters. Successes in addressing compound risks have helped us understand the value of new technologies, such as the use of drones and robots to limit human exposure. Enhancing data collection efforts to enable inclusive sentinel systems can improve surveillance and effective response to future risk challenges. Stay-at-home policies put in place during the pandemic for virus containment have highlighted the need to holistically consider the built environment and socio-economic exigencies when addressing the pandemic’s physical and mental health impacts, and could also aid in the context of increasing climate-induced extreme events. As we have seen, such policies, services, and technologies, along with good nutrition, can significantly help safeguard health and well-being in pandemic times, especially when simultaneously faced with ubiquitous climate-induced extreme events. In the final decade of SDG actions, these measures may help in efforts to “Leave No One Behind”, enhance human–environment relations, and propel society to embrace sustainable policies and lifestyles that facilitate building back better in a post-pandemic world. Concerted actions that directly target the compounding effects of different interacting hazards should be a critical priority of the Sendai Framework by 2030., This research received no external funding.

The generation of wind fields is of interest in the study of the structural performance of wind turbines in critical events, such as thunderstorm downbursts. Various methods ranging from the use of empirical data to employing computational simulations are typically adopted to study the response of wind turbines in downburst flow fields. While the former approach is limited in the ability to account for accurate and spatially resolved details of the flow field, the latter is expensive and, therefore, has limitations in its use. As an alternative, in this work, we propose a paused downburst model in which a snapshot of a time-dependent computational fluid dynamics (CFD) simulation is used to generate “mean” wind fields during thunderstorm downbursts. The developed model for the mean wind field is validated against recorded downburst data in the literature. The turbulent component of the wind field is generated using computationally inexpensive techniques based on Fourier-based power spectral density functions and coherence functions. In an illustrative example, the combined mean and turbulence wind fields are generated and applied on a utility-scale wind turbine to study structural load characteristics during a downburst event.

An offshore energy transition, even if only a gradual one, from carbon-emitting fossil fuel extraction to cleaner sources is recommended, if we are to slow the harmful impacts of climate change. The potential for sustainable reuse of decommissioned offshore jacket platforms to support wind turbines is being considered as an attractive proposition in such a transition. To maximize the benefits of such reuse of assets, what is needed is a rational optimization strategy that considers the remaining life of a repurposed platform, associated retrofit and construction costs, and a future period of gross renewable energy generation following installation of the wind turbine. We outline a study that employs a fatigue reliability-based framework to aid in such sustainable reuse planning and optimization. The framework proposed identifies an optimized reuse plan that incorporates metocean data analysis, structural analysis, life-cycle evaluation, and revenue optimization. We employ a case study and sustainable reuse scenario for a site in the vicinity of Porto, Portugal.

In an emerging “blue economy,” large multipurpose floating platforms in the open ocean could possibly support a range of activities including energy generation, aquaculture, seabed mining, transport, tourism, and sea-based laboratories. A Markov Decision Process (MDP) framework is developed to deal with operations and maintenance issues that are inevitable; challenges arise from the complex stochastic weather conditions that need to be accounted for. Using data as well as contrasting synthetic simulations of relevant weather variables, we demonstrate the robustness/versatility of the MDP model.

A porous-media tuned liquid damper (PMTLD) can serve as an eco-friendly, economical, and effective dynamic vibration absorber. Placing porous media within a water tank can improve the capacity for energy dissipation and optimize the performance by varying its material properties. The numerical simulations of the floating platform dynamics in waves coupled with the sloshing problem in PMTLD are emphasized. Two numerical methods are adopted for fluid computation. The first potential-based approach employs a mixed-type boundary value problem (BVP) solver, which is implemented by the boundary element method (BEM), coupled with a free surface particle tracker, which includes the nonlinear damping effects via a quadratic Forchheimer term for PMTLD. Another equivalent mechanical model is also used exclusively for solving the linear PMTLD. The coupling behavior between fluid and structure is solved by the Newmark method. The second viscous approach uses the finite element method (FEM) to spatially discretize the Navier-Stokes equations and handles the free surface via the volume of fluid (VOF) and the level set (LS) equations. The multiphase simulation is implemented by a computational modeling toolkit Proteus for the fluid phase and Chrono for the solid phase. The correlations between potential flow and Navier-Stokes (NS) models are presented. The PMTLD is designed for a floating platform by analogy with the tuned mass damper (TMD). Numerical results show that the PMTLD can effectively reduce the structure’s dynamic response in terms of vibration amplitude around resonance. Such damping devices have great potential for practical applications in offshore platforms and wind turbine design.

Background: The intensity of ground shaking and the demand on structures during earthquakes have been generally characterized using parameters such as peak ground acceleration as well as strength-based parameters such as response spectrum ordinates (e.g., spectral acceleration), which represent the maximum amplitude of shaking for structures with specified natural period and damping values. Methods: It has long been recognized that to assess the demands on structures during earthquakes, one might employ an energy-based approach (as an alternative to the more common strength-based one), especially when there is an interest in assessing the damage potential of ground motions. The study focuses on the correlation between Ground Motion Parameters (GMPs) and structural damage indicators for reinforced concrete frames. The relationship between ground motion intensity and structural damage is of primary concern in seismic regions. Here, we consider GMPs that are strength-based (such as spectral acceleration, Sa) and energy-based (such as input energy-equivalent acceleration, Ai, and absorbed energy-equivalent acceleration, Aa), as defined in the study. In order to evaluate Ground Motion Parameters (GMPs) for efficiency, power-law format regression studies of damage measures (DM) on each GMP are utilized. Results: The general objective of this paper is to investigate if energy-based parameters can correlate well with structural damage. Thus, power-law formats in regression of damage on GMP are considered. In regression studies of this type, a lower standard deviation of the damage measure given the value of the ground motion parameter (GMP) is considered an indication of a stronger correlation between that damage measure and the corresponding ground motion parameter. Conclusion: These extensive regression studies are carried out for both low-rise and high-rise reinforced concrete buildings. Conclusions are discussed following these analyses.

Predicting a wind turbine's responses that correspond to a complex wind field is challenging because the responses are caused by the complex interaction between a dynamically operating mechanical system and a spatially and temporally coupled stochastic wind field. We propose a physics-inspired, data-driven prediction model called stacked dilated convolutional LSTMs (SDCL) that uses a sequence of wind fields (snapshots) as an input to predict future wind turbine responses. A SDCL is composed of a set of dilated convolutional neural networks (CNNs) combined with a long short-term memory (LSTM) to capture the spatial and temporal evolution of the turbulence structure in the input wind field. Notably, a dilated CNN with different dilation ratios along with a corresponding LSTM module, a single component of SDCL, is designed to capture the evolution of an eddy of a certain size in the turbulent wind field. Then SDCL effectively models the evolution of multiple eddies of different sizes. Through a simulation study, we have demonstrated that such a physics-inspired network architecture is effective in processing a complex wind field and thus predicting two representative future wind turbine responses, energy generation and blade root out-of-plane bending moment, more accurately than other standard deep learning architectures.

SUMMARY The horizontal-to-vertical spectral ratio (HVSR) of ambient noise is commonly used to infer a site's resonance frequency (${f_{0,site}}$). HVSR calculations are performed most commonly using the Fourier amplitude spectrum obtained from a single merged horizontal component (e.g. the geometric mean component) from a three-component sensor. However, the use of a single merged horizontal component implicitly relies on the assumptions of azimuthally isotropic seismic noise and 1-D surface and subsurface conditions. These assumptions may not be justified at many sites, leading to azimuthal variability in HVSR measurements that cannot be accounted for using a single merged component. This paper proposes a new statistical method to account for azimuthal variability in the peak frequency of HVSR curves (${f_{0,HVSR}}$). The method uses rotated horizontal components at evenly distributed azimuthal intervals to investigate and quantify azimuthal variability. To ensure unbiased statistics for ${f_{0,HVSR}}$ are obtained, a frequency-domain window-rejection algorithm is applied at each azimuth to automatically remove contaminated time windows in which the ${f_{0,HVSR}}$ values are statistical outliers relative to those obtained from the majority of windows at that azimuth. Then, a weighting scheme is used to account for different numbers of accepted time windows at each azimuth. The new method is applied to a data set of 114 HVSR measurements with significant azimuthal variability in ${f_{0,HVSR}}$, and is shown to reliably account for this variability. The methodology is also extended to the estimation of a complete lognormal-median HVSR curve that accounts for azimuthal variability. To encourage the adoption of this statistical approach to accounting for azimuthal variability in single-station HVSR measurements, the methods presented in this paper have been incorporated into hvsrpy, an open-source Python package for HVSR processing.

SUMMARYThe horizontal-to-vertical spectral ratio (HVSR) of ambient noise measurement is commonly used to estimate a site's resonance frequency (${f_0}$). For sites with a strong impedance contrast, the HVSR peak frequency (${f_{0,\mathrm{ HVSR}}}$) has been shown to be a good estimate of ${f_0}$. However, the random nature of ambient noise (both in time and space), in conjunction with variable environmental conditions and sensor coupling issues, can lead to uncertainty in ${f_{0,\mathrm{ HVSR}}}$ estimates. Hence, it is important to report ${f_{0,\mathrm{ HVSR}}}$ in a statistical manner (e.g. as a mean or median value with standard deviation). In this paper, we first discuss widely accepted procedures to process HVSR data and estimate the variance in ${f_{0,\mathrm{ HVSR}}}$. Then, we propose modifications to improve these procedures in two specific ways. First, we propose using a lognormal distribution to describe ${f_{0,\mathrm{ HVSR}}}$ rather than the more commonly used normal distribution. The use of a lognormal distribution for ${f_{0,\mathrm{ HVSR}}}$ has several advantages, including consistency with earthquake ground motion processing and allowing for a seamless transition between HVSR statistics in terms of both frequency and its reciprocal, period. Second, we introduce a new frequency-domain window-rejection algorithm to decrease variance and enhance data quality. Finally, we use examples of 114 high-variance HVSR measurements and 77 low-variance HVSR measurements collected at two case study sites to demonstrate the effectiveness of the new rejection algorithm and the proposed statistical approach. To encourage their adoption, and promote standardization, the rejection algorithm and lognormal statistics presented in this paper have been incorporated into hvsrpy, an open-source Python package for HVSR processing.

Fatigue damage is treacherous and can lead to the failure of offshore structures. The purpose of this research is to evaluate and discuss the stress concentration factor (SCF) values for tw...

The dynamic response of a jacket-supported offshore wind turbine under coupled wind, wave, and current fields during Hurricane Sandy is the subject of this study. To illustrate the detailed procedure related to the response evaluation of a 5-MW offshore wind turbine with a jacket support structure, we consider a single site where the water depth is 50 m. Loads are computed using two simulation tools, fast and abaqus, with partial coupling. Aerodynamic loads on the turbine rotor are first evaluated using a wind turbine model in fast with a fixed base; then, these rotor aerodynamic loads are applied as point loads at the top of a model of a tower that is supported by a jacket structure in abaqus. On the basis of stochastic simulations, we discuss the applicability of the abaqus jacket modeling and describe the characteristics and comparisons of the aerodynamic and hydrodynamic effects on loads on the jacket members. Details related to the structural model and soil–pile interaction model employed in the analyses are also discussed.

Estimating extreme environmental conditions remains a key challenge in the design of offshore structures. This paper describes an exercise for benchmarking methods for extreme environmental conditions, which follows on from an initial benchmarking exercise introduced at OMAE 2019. In this second exercise, we address the problem of estimating extreme metocean conditions in a variable and changing climate. The study makes use of several very long datasets from a global climate model, including a 165-year historical run, a 700-year pre-industrial control run, which represents a quasi-steady state climate, and several runs under various future emissions scenarios. The availability of the long datasets allows for an in-depth analysis of the uncertainties in the estimated extreme conditions and an attribution of the relative importance of uncertainties resulting from modelling choices, natural climate variability, and potential future changes to the climate. This paper outlines the methodology for the second collaborative benchmarking exercise as well as presenting baseline results for the selected datasets.

The intensity of ground shaking and the demand on structures during earthquakes have been generally characterized using parameters such as peak ground acceleration as well as strength-based parameters such as response spectrum ordinates (e.g., spectral acceleration) that represent the maximum amplitude of shaking for structures with specified natural period and damping values. It has long been recognized that to assess the demands on structures during earthquakes, one might employ an energy-based approach (as an alternative to the more common strength-based one), especially when there is an interest in assessing damage potential of ground motions. An energy spectrum, obtained with the same level of effort required to construct a conventional response spectrum, is a convenient single-parameter description of both amplitude and duration of ground motion and can serve as a useful means by which to describe the performance of structures with different natural periods and damping ratios.In this study, attenuation models for Northwestern Turkey are developed for two parameters (defined herein) that are related to input energy and absorbed energy. The empirical models developed take advantage of the recent increase in the database on strong motion data for Northwestern Turkey. A total of 195 recordings from 17 recent seismic events are included in this database. The ground-motion prediction equations developed are for the geometric mean of the two horizontal components of the 5-percent damped energy parameters (elastic and inelastic input energy-equivalent acceleration, Ai, and absorbed energy-equivalent velocity, Aa) at various periods. Predictions of the energy-based parameters from the proposed attenuation model are compared with (strength-based) spectral acceleration levels predicted by Özbey et al [Soil Dyn. & Earthq. Eng., 24 (2004), pp. 115-125]. It is found that the energy demand parameters were generally greater with elastic Ai demands highest. In addition, the predicted energy-based parameter levels are compared with available Western U.S. attenuation model predictions for the same energy-based parameters. Western U.S. models predict similar energy demands to those with the proposed model. Finally, amplification factors for the energy-based parameters are proposed as a function of site class; these factors can be thought of as analogous to amplification factors for spectral acceleration as given in the NEHRP Seismic Provisions. The patterns related to the amplification are similar as with spectral acceleration in NEHRP (2001). A comparison of soil amplification effects for strength- and energy-based parameters is also discussed.

The dynamic response and reliability analysis of a 13.2 MW offshore wind turbine supported by a moored semi-submersible platform is the subject of this study. Loads data for the extreme response analysis involve time-domain simulations for a range of sea states representative of expected site-specific metocean conditions. To gain deeper insight into the dynamic behavior of this system and to obtain long-term loads efficiently and accurately, two studies are carried out sequentially. First, the short-term response of the integrated system is studied based on 1-h simulations for sea states identified using the Environmental Contour method for a 50-year return period. Response extremes for the integrated wind turbine system as well as system sensitivity to metocean conditions are studied. Next, the long-term response associated with the 50-year return period is estimated using statistical extrapolation based on loads derived from the 1-h simulations. Inverse First-Order Reliability Method procedures are employed to seek appropriate response quantile levels, e.g., the median response for 2D Inverse FORM. A more comprehensive 3D approach, which accounts for system response uncertainties, improves long-term response estimates. A proposed adaptive procedure in the 3D approach helps determine the number of simulations needed to guarantee accuracy in the long-term response estimation.

Marine risers are susceptible to sustained vortex-induced vibration (VIV) because of their slenderness and light damping. Commonly used tools for analyzing VIV and the associated fatigue damage are based on the finite element method and rely on simplifying assumptions on the riser's physical model, the flow conditions, and characteristics of the response. In order to assess the influence of VIV and to ensure the integrity of the riser, field monitoring campaigns are often undertaken wherein data loggers such as strain sensors and/or accelerometers are installed on such risers. Given the recorded riser's dynamic response, empirical techniques can be used in VIV-related fatigue estimation. These empirical techniques make direct use of the measurements and are intrinsically dependent on the actual current profiles. Damage estimation can be undertaken for the different current profiles encountered and can account explicitly even for complex riser response characteristics. With a significant amount of data, “short-term” fatigue damage probability distributions, conditional on current, can be established. If the relative frequency of different current types is known from a separate metocean study, the short-term fatigue damage distributions can be combined with the current distributions to yield an integrated “long-term” fatigue damage model, which then can be used to predict the long-term cumulative fatigue damage for the instrumented riser. Non-parametric statistical techniques (that do not assume a specific function for the underlying distribution as parametric techniques do) are employed to describe the short-term fatigue damage data. In this study, data from the Norwegian Deepwater Programme (NDP) model riser experiments are used to demonstrate the effectiveness of empirical procedures and non-parametric statistics applied to field measurements to predict long-term fatigue damage, life, and probability of fatigue failure.

The fatigue resistance of welded connections made of high strength steel (HSS) is one of the most important topics for the application of HSS in the construction sector. One of the most challenging issues is how to predict the fatigue life of welded structures with complex geometry based on the test results from relatively simple coupon specimens. However, there are generally pre-existing residual stresses in the welded coupon specimens during fatigue tests, and these residual stresses vary greatly in welded structures with complex geometry. This increases the difficulty in predicting the fatigue behaviour of welded structures based on results at coupon scale. Hence, it is important to establish a relationship between the residual stress independent material characteristics and fatigue life. The fatigue behaviour of complex welded structures can be predicted by this residual stress independent material characteristics calibrated at the coupon level and simulated local residual stress distribution. In this paper, the residual stress-free characteristics, hardness, is employed to predict the fatigue life of butt-welded joints. Besides, the residual stress of V-shaped butt welds on a plate made of high strength steels are analysed by modelling of the welding process based on subsequent thermal analysis and mechanical stress analysis by implementing kill/birth strategies. The results show that it contributes to a better prediction compared with experimental results after considering the residual stress effects.

Environmental contours are used to simplify the process of design response analysis. A wide variety of contour methods exist; however, there have been a very limited number of comparisons of these methods to date. This paper is the output of an open benchmarking exercise, in which contributors developed contours based on their preferred methods and submitted them for a blind comparison study. The exercise had two components—one, focusing on the robustness of contour methods across different offshore sites and, the other, focusing on characterizing sampling uncertainty. Nine teams of researchers contributed to the benchmark. The analysis of the submitted contours highlighted significant differences between contours derived via different methods. For example, the highest wave height value along a contour varied by as much as a factor of two between some submissions while the number of metocean data points or observations that fell outside a contour deviated by an order of magnitude between the contributions (even for contours with a return period shorter than the duration of the record). These differences arose from both different joint distribution models and different contour construction methods, however, variability from joint distribution models appeared to be higher than variability from contour construction methods.

The wind resource offshore is often excellent due to a higher average wind speed and lower turbulence intensity. Jacket structures are the most common offshore platforms for extraction of oil and natural gas in relative low water depths. When the offshore resources run out, these structures must be displaced to another area containing underground resources or removed in the case of reaching their design life. Therefore, one possible procedure to reduce the carbon footprint on the planet, allowing society to rely on promising sources of ’clean’ energy while salvaging these oil and gas platforms, is to consider the transformation of these oil and gas platforms into offshore wind turbine support structures. The present research focus on the possibility of converting such structures for gas extraction into offshore platforms for wind turbines. In this study, a comparison between the behavior of horizontal and vertical axis wind turbines on the same offshore platform is presented. In this comparison, two different software programs are used: MATLAB and SAP2000. The model proposed is a new simplified tool used to study the structural analysis of the jacket structures, developed and summarized in 10 steps, adopted to evaluate the behavior of the platform with the wind tower configurations.

Accurate and efficient power demand forecasting in urban settings is essential for making decisions related to planning, managing and operations in electricity supply. This task, however, is complicated due to many sources of uncertainty such as due to the variation in weather conditions and household or other needs that influence the inherent stochastic and nonlinear characteristics of electricity demand. Due to the modeling flexibility and computational efficiency afforded by it, a Gaussian process model is employed in this study for energy demand prediction as a function of temperature. A Gaussian process model is a Bayesian non-parametric regression method that models data using a joint Gaussian distribution with mean and covariance functions. The selected mean function is modeled as a polynomial function of temperature, whereas the covariance function is appropriately selected to reflect the actual data patterns. We employ real data sets of daily temperature and electricity demand from Austin, Texas, USA to assess the effectiveness of the proposed method for load forecasting. The accuracy of the model prediction is evaluated using metrics such as mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE) and 95% confidence interval (95% CI). A numerical study undertaken demonstrates that the proposed method has promise for energy demand prediction.

To ensure acceptable operation and/or survival of floating structures in extreme conditions, nonlinear time-domain simulations are often used to predict the structural response at the design stage. An environmental contour (EC) is commonly employed to identify critical sea states that serve as input for numerical simulations to assess the safety and performance of marine structures. In many studies, marginal and conditional distributions are defined to construct bivariate joint probability distributions for variables such as significant wave height and zero-crossing period; then, environmental contours can be constructed using the inverse first-order reliability method (IFORM). This study adopts alternative models to describe the generalized dependence structure between the environmental variables using copulas; the Nataf transformation is also discussed as a special case. Environmental contours are constructed, making use of measured wave data from moored buoys. Derived design loads are applied on a semi-submersible platform to assess possible differences. In addition, the long-term extremes of the tension of the mooring lines are estimated, considering uncertainties in the structural response using a 3D model (that includes response variability, ignored with the EC approach) to help establish more accurate design loads using Monte Carlo simulation. Results offer a clear indication of the extreme response of the floating structure based on the different models.

This study focuses on the development of efficient surrogate models by polynomial chaos expansion (PCE) for the prediction of the long-term extreme surge motion of a moored floating offshore structure. The structure is subjected to first-order and second-order (difference-frequency) wave loading. Uncertainty in the long-term response arises from contrasting sea state conditions, characterized by significant wave height, Hs, and spectral peak period, Tp, and their relative likelihood of occurrence; these two variables are explicitly included in the PCE-based uncertainty quantification (UQ). In a given sea state, however, response simulations must be run for the associated Hs and Tp; in such simulations, typically, a set of random amplitudes and phases define an irregular wave train consistent with that sea state. These random amplitudes and phases for all the frequency components in the wave train introduce additional uncertainty in the simulated waves and in the response. The UQ framework treats these two sources of uncertainty—from Hs and Tp on the one hand, and the amplitude and phase vectors on the other—in a manner that efficiently yields long-term surge motion extreme predictions consistent with more expensive Monte Carlo simulations (MCS) that serve as the “truth” system. To reduce uncertainty in response extremes that result from sea states with a low likelihood of occurrence, importance sampling is employed with both MCS- and PCE-based extreme response predictions. Satisfactory performance with such efficient surrogate models can help in assessing the long-term response of various offshore structures.

Thunderstorm downbursts have been reported to cause damage or failure to wind turbine arrays. We extend a large-eddy simulation model used in previous work to generate downburst-related inflow fields with a view toward defining correlated wind fields that all turbines in an array would experience together during a downburst. We are also interested in establishing what role contrasting atmospheric stability conditions can play on the structural demands on the turbines. This interest is because the evening transition period, when thunderstorms are most common, is also when there is generally acknowledged time-varying stability in the atmospheric boundary layer. Our results reveal that the structure of a downburst’s ring vortices and dissipation of its outflow play important roles in the separate inflow fields for turbines located at different parts of the array; these effects vary with stability. Interacting with the ambient winds, the outflow of a downburst is found to have greater impacts in an “average” sense on structural loads for turbines farther from the touchdown center in the stable cases. Worst-case analyses show that the largest extreme loads, although somewhat dependent on the specific structural load variable considered, depend on the location of the turbine and on the prevailing atmospheric stability. The results of our calculations show the highest simulated foreaft tower bending moment to be 85.4 MN-m, which occurs at a unit sited in the array farther from touchdown center of the downburst initiated in a stable boundary layer.