Site-specific ultimate limit state fragility of offshore wind turbines on monopile substructures

Assessing the risk posed by severe storms to offshore wind turbines (OWTs) is a challenging task. Stochastic environmental conditions represent the main source of variable loading; consequently, a high level of uncertainty is associated with assessing structural demands on OWT structures. Failure of any of the primary structural components implies both complete loss of the OWT and loss of earnings associated with production stoppage. In this paper, we propose the use of a probabilistic risk modelling framework to assess the structural risk posed by extreme weather conditions to OWTs. To achieve this, fragility functions are developed for OWTs on monopile foundations exposed to extreme metocean conditions using dynamic aero-elastic simulations. Structural fragility represents a key component of any probabilistic risk model and expresses the likelihood of different levels of damage experienced by an OWT over a range of wind and wave hazard intensities. We compare the effect of various modelling and analysis choices on the obtained fragility functions and investigate potential interdependencies between failure modes of OWT structural components. Results from this study highlight how different assumptions affect the estimated structural performance and the resulting structural fragility of a case-study OWT. We apply the proposed framework to two case-study sites, one in the USA East Coast and one in the North Sea, discussing possible outcomes of the proposed framework

Optimal control of the heave motion of marine cable subsea-unit systems

One of the key problems associated with subsea operations involving tethered subsea units is the motions of support vessels on the ocean surface which can be transmitted to the subsea unit through the cable and increase the tension. In this paper, a theoretical approach for heave compensation is developed. After proper modelling of each element of the system, which includes the cable/subsea-unit, the onboard winch, control theory is applied to design an optimal control law. Numerical simulations are carried out, and it is found that the proposed active control scheme appears to be a promising solution to the problem of heave compensation

Advancing probabilistic risk assessment of offshore wind turbines on monopiles

Offshore Wind Turbines (OWTs) are a unique type of engineered structure. Their design spans all engineering disciplines, ranging from structural engineering for the substructure and foundation to electrical or mechanical engineering for the generating equipment. Consequently, the different components of an OWT are commonly designed independently using codified standards. Within the OWT design process, financial cost plays an important role as a constraint on decision making, because of the competition between prospective wind farm operators and with other forms of electricity generation. However, the current, independent design process does not allow for a combined assessment of OWT system financial loss. Nor does it allow for quantification of the uncertainties (e.g., wind and wave loading, materials properties) that characterise an OWT’s operations and which may have a strong impact on decision making. This thesis proposes quantifying financial losses associated with an OWT exposed to stochastic wind and wave conditions using a probabilistic risk modelling framework, as a first step towards evaluating Offshore Wind Farm (OWF) resilience. The proposed modelling framework includes a number of novel elements, including the development of site-specific fragility functions (relationships between the likelihood of different levels of damage experienced by an OWT over a range of hazard intensities), which account for uncertainties in both structural capacity and demands. As a further element of novelty, fragility functions are implemented in a closed-form assessment of financial loss, based on a combinatorial system reliability approach, which considers both structural and non-structural components. Two important structural performance objectives (or limit states) are evaluated in this thesis: 1) the Ultimate Limit State (ULS) which assesses the collapse of an OWT due to extreme wind and wave conditions, such as those resulting from hurricanes; and 2) the Fatigue Limit State (FLS), which addresses the cumulative effects of operational loading, i.e., cracks growing over the life of the structure until they threaten its integrity. This latter limit state is assessed using a novel machine learning technique, Gaussian Process (GP) regression, to develop a computationally-efficient surrogate model that emulates the output from computationally-expensive time-domain structural analyses. The consequence of the OWT failing is evaluated by computing annualised financial losses for the full OWT system. This provides a metric which is easily communicable to project stakeholders, and can also be used to compare the relative importance of different components and design strategies. Illustrative applications at case-study sites are presented as a walk-through of the calculation steps in the proposed framework and its various components. The calculation of losses provides a foundation from which a more detailed assessment of OWT and OWF resilience could be developed

Extreme event risk assessment for offshore systems design and operation in harsh environments

Operations of the offshore systems in harsh environments require better understanding, precise assessment, and effective management of risks. The harsh environmental conditions, such as strong ocean currents, extreme wave conditions, complex subsurface geology, frigid temperatures, and icebergs, exert extreme load on the offshore systems. Environmental factors are interconnected, and when they occur at a higher rate or in extreme conditions, they are likely to cause a catastrophic event. Such scenarios are prone to occur in the current changing conditions of climate. Assessment of extreme loads that may cause a rare event situation is critical to define risk scenarios. This study focuses on the assessment of these extreme event risk scenarios. By integrating extreme load and its likelihood of occurring, this research investigates the current state of knowledge in extreme event risk analysis. The extreme load consideration task considers three dominating aspects: stationary and non-stationary conditions; univariate and multivariate analysis; and dependence of the variables. This study also focuses on the flexible risk-based design methodology that integrates the traditional Extreme Value Theory (EVT) with climate change. The key environmental parameters considered in this study are iceberg speed, wind speed, and wave height. The developed methodologies use the above parameters from the Atlantic Continental Shelf, specifically the Flemish Pass basin, Grand Bank, and the Jeanne d’Arc basin. Due to limited data for certain environmental phenomena, such as large iceberg data in the Flemish Pass basin, the iceberg load assessment problem is treated as a rare event scenario. Traditional methods, including Peak Over Threshold (POT) based Generalized Pareto Distribution (GPD) and Block Maxima (BM) based Generalized Extreme Value (GEV), were found to be inadequate to capture the present-day extreme characteristics in the rare event cases. As an alternative, this study proposes and validates the use of POT-based Heavy Right Tail Distribution (HRTD) for iceberg load analysis at the Flemish Pass basin. The research also observes that Maximum Likelihood Estimator (MLE) provides a biased estimate for model parameter estimation in rare event scenarios, whereas the Hill, SmooHill, and Bayesian approaches offer better estimates. The methodology is extended to multivariate settings to capture extreme dependencies using extreme value copula function for investigating rare event risk profiles. The proposed low-resolution risk profile methodology offers a more efficient and cost-effective alternative to computationally expensive numerical models in the offshore domain. Climate change is observed to have an impact on the correlation between various environmental factors, including wind speed and wave height. Because of climate change, 100-year events are becoming more frequent. Consequently, the study adopts a 1000-year time frame to adjust for the increasing frequency of 100-year events under the influence of climate change, enabling predictions beyond standard lifetimes. The conditional return level function is utilized to construct rare events return level predictions under climate change threats. Finally, a non-stationary process is considered to generate a dynamic risk profile. Outcomes of this research provide a clear understanding of how climate change affects the Newfoundland offshore region. By incorporating predicted extreme loads and their likelihood of occurring, the traditional EVT-based methodologies are combined with adaptable risk-based design methodologies. The proposed dynamic, flexible, and small-scale (0.10×0.10 latitude/longitude grid) risk assessment methodology aids in offshore design decision-making for safer design and operation

Climate Adaptation Engineering and Risk-based Design and Management of Infrastructure

International audienceA changing climate may also result in more intense tropical cyclones and storms, more intense rain events and flooding, and other natural hazards. Moreover, increases in CO2 atmospheric concentrations, temperature and humidity will increase corrosion of concrete and steel structures. The chapter will describe how risk-based approaches are well suited to optimising climate adaptation strategies related to the design and maintenance of existing infrastructure. Climate adaptation strategies may include retrofitting or strengthening of existing structures, more frequent inspections, or enhanced designs. An important aspect is assessing at what point in time climate adaptation becomes economically viable. Stochastic methods are used to model infrastructure performance, effectiveness of adaptation strategies, exposure, and costs. These concepts will be illustrated with state-of-the-art research of risk-based assessment of climate adaptation strategies