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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]

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]

The estimation of fatigue lifetime for an offshore wind turbine support structure requires a large number of time-domain simulations. It is an important question whether it is possible to reduce the number of load cases while retaining a high level of accuracy of the results. We present a novel method for simplified fatigue load assessments based on statistical regression models that estimate fatigue damage during power production. The main idea is to predict the total fatigue damage only and not also the individual damage values for each load case. We demonstrate the method for a jacket-type support structure. Reducing the number of simulated load cases from 21 to 3, the total fatigue damage estimate exhibited a maximum error of about 6% compared with the complete assessment. As a consequence, a significant amount of simulation time can be saved, in the order of a factor of seven. This quick fatigue assessment is especially interesting in the application of structural optimization, with a large number of iterations. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: Performing a dynamic analysis of an offshore wind turbine with an aero-elastic solver relies on the inclusion of a model to represent the behavior of the piled foundations. The most commonly used solution is the p-y method, which was developed for offshore oil and gas applications and has been extended to the offshore wind energy industry. There are several shortcomings with this method, however, which can affect the accuracy of wind turbine simulations. These shortcomings are identified and explained in this work. More advanced pile foundations models which account for many nonlinear behaviors ignored by the traditional p-y method have been developed in the past for applications unrelated to offshore wind turbines. These models can nevertheless be applied to offshore wind turbine simulations and are discussed herein. A proposed method for incorporating these so called ‘dynamic p-y’ models into wind turbine simulations is laid out and discussed. [Copyright &y& Elsevier]

Abstract: An alternative support structure for offshore wind turbines is presented that removes the need for an expensive transition piece and allows for significant savings in steel weight. The concept is based on replacing both the traditional wind turbine tower and the usual support structure with a single lattice tower that extends from the sea bottom to the rotornacelle- assembly. An overview is given of studies performed and results obtained for this design so far, of its advantages and disadvantages, and of future plans for improved automatic optimization of the design. Although it is diffcult to show the economic viability of the full-height lattice tower concept, due to the general problem of reliably quantifying fabrication and installation costs, the major reduction in steel weight makes this an attractive concept that should be given further consideration, especially for future offshore projects in deep waters. [Copyright &y& Elsevier]

Abstract: Among several possible support structure types for offshore wind turbines, a full-height lattice tower is one design option. Advantages of this design are the smaller amount of steel used for the structure compared to other concepts, and the possibility to install the whole structure in one operation. Based on the complexity of dynamic loadings on the support structure by wind and wave as well as operational loads, an initial lattice tower design with constant member dimensions over the tower height shows a large optimization potential and can be optimized section by section. This paper presents basic considerations for an iterative optimization approach and identifies sensitivities for the optimization process of a full-height lattice tower. It was found that an analysis with constant member dimensions over the tower height gives an indication about the required dimensions for an optimized design. [Copyright &y& Elsevier]

Abstract: Up to one third of the total cost of energy from offshore wind generation is contributed by operation and maintenance (O&M). Compared to its onshore counterpart, this fraction is significantly higher. Costs are not only caused by spare-parts and repair actions, but also by production losses due to downtime. The accessibility of a turbine in case of a failure is one main aspect affecting downtime. Therefore, a tool has been developed and implemented in MATLAB to simulate the operating phase of a wind farm with special emphasis toward the modeling of failures and repair. As an example application, a site at the UK east coast was chosen, and a few distinct scenarios were considered. Results include how sensitive availability changes with respect to changes in maintenance fleet and maintenance scheduling strategy. A quantification of potential cost savings due to an increase in availability is also stated. [Copyright &y& Elsevier]

Optimization of the maintenance policies for offshore wind parks is an important step in lowering the costs of energy production from wind. The yield from wind energy production is expected to fall, which will increase the need to be cost efficient. In this article, the Markov decision process is presented and how it can be applied to evaluate different policies for corrective maintenance planning. In the case study, we show an alternative to the current state-of-the-art policy for corrective maintenance that will achieve a cost-reduction when energy production prices drop below the current levels. The presented method can be extended and applied to evaluate additional policies, with some examples provided. [ABSTRACT FROM AUTHOR]

To maintain the increasing interest and development in offshore wind energy, novel training tools for engineers and researchers are needed. Concurrently, educational outreach activities are in demand to inform the public about the importance of offshore wind energy. In this paper, the development of a serious game about the design and management of offshore wind farms is presented to address such demands. Such a serious game may enable a new audience to explore the field of offshore wind as well as provide researchers entering the field a better understanding of the intricacies of the industry. This requires a simulation that is realistic but also effective in teaching information and engaging outreach. Ultimately, increased public support and expanded training tools are desired to improve decision-making and to provide opportunities to test and integrate innovative solutions. The work presented here includes the game design and implementation of a prototype game. The game design involves building a game framework and developing a simplified simulation. This simulation addresses weather prediction, offshore wind farm design, operation and maintenance, energy demand, climate change, and finance. Playtesting of the prototype demonstrated immersion and informed decision-making of the players and surveys revealed that knowledge had increased while playing the game. Recommendations for future versions of the game are listed. [ABSTRACT FROM AUTHOR]

Under certain harsh environmental conditions, jacket structures supporting offshore wind turbines might be exposed to plunging breaking waves, causing slamming forces that affect the structural integrity and fatigue life. The slamming forces should thus be properly considered during the design, but a suitable force model specifically for jacket structures is currently in absence. In this study, a five-parameter force model is developed for estimating global slamming forces due to plunging breaking waves on jacket structures, based on statistical analyses of experimental data from the WaveSlam project. The force model is developed by considering a total of 176 individual breaking waves, under six wave conditions. For each individual breaking wave, the time history of the slamming force is calculated based on hammer test data in addition to wave test data, and the wave parameters are acquired from a wave elevation measurement. The acquired time histories and wave parameters are then used to determine the parameters involved in the force model, including two exponential parameters (i.e. α 1 and α 2 ) and three dimensionless coefficients for the expressions of wave-dependent parameters (i.e. duration coefficient ζ 1 , rising time coefficient ζ 2 , and peak force coefficient ζ 3 ). It is found that α 1 , α 2 , ζ 1 and ζ 2 are approximately constant, and ζ 3 follows a lognormal distribution. The quantile that determines ζ 3 should be carefully selected so as to provide a conservative prediction. A quantile of 95% is suggested in this paper, and it is found to be conservative based on the verification of the developed force model. Therefore, for a given sea state, this force model can give a deterministic and conservative prediction of the slamming force time history, regardless of the randomness of slamming forces. Challenges for the application of the force model are also addressed. [ABSTRACT FROM AUTHOR]

In large offshore wind farms fatigue loads on support structures can vary significantly due to differences and uncertainties in site conditions, making it necessary to optimize design clustering. An efficient probabilistic fatigue load estimation method for monopile foundations was implemented using Monte-Carlo simulations. Verification of frequency domain analysis for wave loads and scaling approaches for wind loads with time domain aero-elastic simulations lead to 95% accuracy on equivalent bending moments at mudline and tower bottom. The computational speed is in the order of 100 times faster than typical time domain tools. The model is applied to calculate location specific fatigue loads that can be used in deterministic and probabilistic design clustering. Results for an example wind farm with 150 turbines in 30–40 m water depth show a maximum load difference of 25%. Smart clustering using discrete optimization algorithms leads to a design load reduction of up to 13% compared to designs based on only the highest loaded turbine position. The proposed tool improves industry-standard clustering and provides a basis for design optimization and uncertainty analysis in large wind farms. [ABSTRACT FROM AUTHOR]

Design optimization of offshore wind turbine support structures is an expensive task due to the highly-constrained, non-convex and non-linear nature of the design problem. A good depth of detail in the problem formulation can give useful insights in the practical design process, but may also compromise the efficiency. This paper presents an analytical gradient-based method to solve the problem in an effective and efficient way. The design sensitivities of the objective and constraint functions are evaluated analytically, while the optimization procedure is performed in the time domain, subjected to sizing, eigenfrequency, extreme load and fatigue load constraints. A case study on the OC4 and UpWind jacket substructures show that the method was reliable and consistent in delivering superior efficiency and accuracy in the optimization study, as compared with the conventional finite difference approach. The global optimum was probably achieved in the design optimization process, where the large number of design constraints implemented can possibly be the blessing in disguise, as they seem to enable the optimizer to find the global optimum. Both the buckling and fatigue load constraints had significant influence over the design of tubular members and joints, while each component is oriented to maximize the utilization against the prescribed limit state functions. [ABSTRACT FROM AUTHOR]