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The need for cost-effective support structure designs for offshore wind turbines has led to continued interest in the development of design optimization methods. So far, almost no studies have considered the effect of uncertainty, and hence probabilistic constraints, on the support structure design optimization problem. In this work, we present a general methodology that implements recent developments in gradient-based design optimization, in particular the use of analytical gradients, within the context of reliability-based design optimization methods. Gradient-based optimization is typically more efficient and has more well-defined convergence properties than gradient-free methods, making this the preferred paradigm for reliability-based optimization where possible. By an assumed factorization of the uncertain response into a design-independent, probabilistic part and a design-dependent but completely deterministic part, it is possible to computationally decouple the reliability analysis from the design optimization. Furthermore, this decoupling makes no further assumption about the functional nature of the stochastic response, meaning that high-fidelity surrogate modeling through Gaussian process regression of the probabilistic part can be performed while using analytical gradient-based methods for the design optimization. We apply this methodology to several different cases based around a uniform cantilever beam and the OC3 Monopile and different loading and constraint scenarios. The results demonstrate the viability of the approach in terms of obtaining reliable, optimal support structure designs and furthermore show that in practice only a limited amount of additional computational effort is required compared to deterministic design optimization. While there are some limitations in the applied cases, and some further refinement might be necessary for applications to high-fidelity design scenarios, the demonstrated capabilities of the proposed methodology show that efficient reliability-based optimization for offshore wind turbine support structures is feasible. [ABSTRACT FROM AUTHOR]

A complete fatigue assessment for operational conditions for offshore wind turbines involves simulating thousands of environmental states. For applications such as optimization, where this assessment needs to be repeated many times, that presents a significant computational problem. Here, we propose a novel way of reducing the number of simulated environmental states (load cases) while maintaining an acceptable accuracy. From one full fatigue analysis of a base design, the OC3 monopile (with the NREL 5MW turbine), the distribution of fatigue damage per load case can be used to estimate the lifetime fatigue damage of a range of modified designs. Using importance sampling and a specially adapted two‐stage filtering procedure, we obtain pseudo‐optimal sets of load cases from which the fatigue damage is estimated. This is applied to seven different designs that have been modified to emulate iterations of an optimization loop. For several of these designs, sampling less than 1% of all load cases can give damage estimates with median errors of less than 2%. Even for the most severe cases, using 3% of the environmental states yields a maximum error of 10%. While further refinement is possible, the method is considered viable for applications within design optimization and preliminary design. [ABSTRACT FROM AUTHOR]

This paper reviews the state of the art in offshore wind farm operations and maintenance with a focus on decision support models for the scheduling of maintenance. Factors influential to maintenance planning are collected from the literature and their inclusion in state-of-the-art models is discussed. Methods for modeling and optimization are presented. The methods currently used and possible alternatives are discussed. The existing models are already able to aid the decision-making process. They can be improved by applying more advanced mathematical methods, including uncertainties in the input, regarding more of the influential factors, and by collecting, analyzing, and subsequently using more accurate data. [ABSTRACT FROM AUTHOR]

During the bidding or very early design phases of jacket structures for offshore wind turbines, there may be very limited information available on meteorological conditions, soil conditions, turbine specifications, etc. However, it is still important to quickly produce near‐optimal designs with production costs similar to that of the final support structure. Numerical optimization methods can be used to this purpose. This paper investigates three gradient‐based optimization methods, where preliminary designs are produced by mass optimization. The mass is reduced by changing tube diameter and thickness of the structural members, and the optimization considers both frequency and fatigue constraints. The three methods are based on (1) damage equivalent loads, (2) quasi‐static analysis, and (3) dynamic analysis. The optimizations are conducted using in‐house software JADOP (jacket design optimization), and the optimized designs are evaluated using state‐of‐the‐art integrated time‐domain simulation software FEDEM Windpower. The findings show that each analysis can be applied with success. However, if excitations of structural frequencies contribute significantly to the overall damage, special care must be taken with quasi‐static and static modeling. It is observed that wave loading does not contribute considerably to the fatigue damage. Additionally, the aerodynamic loading does not change significantly with changes of tube geometry within the optimization ranges. The optimized designs are partly driven by reducing stress concentration factors, which can be achieved by reducing the chord diameter to thickness ratio. Thus, the optimized designs resemble each other to a certain extent. [ABSTRACT FROM AUTHOR]

Maintenance costs in offshore wind farms are one of the main reasons why it is still not competitive with its onshore counterpart. To lower the price of energy, the authors want to improve the modeling of offshore wind farms in order to help improving the applied maintenance strategies. In this paper, we investigate the repair of wind turbine components as an alternating renewal process. The time it takes to repair (renew) a component in a turbine, directly influences the downtime of this turbine, or even larger parts of the wind farm. This downtime leads to production losses, which in turn raise the cost of energy. Previous investigations by two of the present authors showed that a variation in repair times has a significant influence of the production losses. In Maintenance costs in offshore wind farms are one of the main reasons why it is still not competitive with its onshore counterpart. To lower the price of energy, the authors want to improve the modeling of offshore wind farms in order to help improving the applied maintenance strategies. In this paper, we investigate the repair of wind turbine components as an alternating renewal process. The time it takes to repair (renew) a component in a turbine, directly influences the downtime of this turbine, or even larger parts of the wind farm. This downtime leads to production losses, which in turn raise the cost of energy. Previous investigations by two of the present authors showed that a variation in repair times has a significant influence of the production losses. In this paper, an alternate renewal process is investigated as tool to calculate the influence of the repair time duration on the production losses. We show that if we assume an exponential distribution of repair times, the distribution of the downtimes can be calculated analytically. The results are demonstrated with a case study, based on available failure rates and repair times. For other distributions, the calculation has to be done numerically, after fitting the parameters of the distribution to the available data. Further work with other distributions and actual repair time distributions is planned. [ABSTRACT FROM AUTHOR]

The large amount of computational effort required for a full fatigue assessment of offshore wind turbine support structures under operational conditions can make these analyses prohibitive, especially for applications like design optimization, for which the analysis would have to be repeated for each iteration of the process. To combat this issue, we present a simple procedure for reducing the number of load cases required for an accurate fatigue assessment. After training on one full fatigue analysis of a base design, the method can be applied to establish a deterministic, reduced sampling set to be used for a family of related designs. The method is based on sorting the load cases by their severity, measured as the product of fatigue damage and probability of occurrence, and then calculating the relative error resulting from using only the most severe load cases to estimate the total fatigue damage. By assuming this error to be approximately constant, one can then estimate the fatigue damage of other designs using just these load cases. The method yields a maximum error of about 6% when using around 30 load cases (out of 3647) and, for most cases, errors of less than 1%-2% can be expected for sample sizes in the range 15-60. One of the main points in favor of the method is its simplicity when compared to more advanced sampling-based approaches. Though there are possibilities for further improvements, the presented version of the method can be used without further modifications and is especially useful for design optimization and preliminary design. We end the paper by noting some possibilities for future work that extend or improve upon the method. [ABSTRACT FROM AUTHOR]

Decoupled load simulations are a computationally efficient method to perform a dynamic analysis of an offshore wind turbine. Modelling the dynamic interactions between rotor and support structure, especially the damping caused by the rotating rotor, is of importance, since it influences the structural response significantly and has a major impact on estimating fatigue lifetime. Linear damping is usually used for this purpose, but experimentally and analytically derived formulas to calculate an aerodynamic damping ratio often show discrepancies to measurement and simulation data. In this study decoupled simulation methods with reduced and full rotor loads are compared to an integrated simulation. The accuracy of decoupled methods is evaluated and an optimization is performed to obtain aerodynamic damping ratios for different wind speeds that provide the best results with respect to variance and equivalent fatigue loads at distinct output locations. Results show that aerodynamic damping is not linear, but it is possible to match desired output using decoupled models. Moreover, damping ratios obtained from the empirical study suggest that aerodynamic damping increases for higher wind speeds. [ABSTRACT FROM AUTHOR]