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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]

The ultimate capacity of support structures is checked with extreme loads. This is straightforward when the limit state equations depend on a single load component, and it has become common to report maxima for each load component. However, if more than one load component is influential, e.g., both axial force and bending moments, it is not straightforward how to define an extreme load. The combination of univariate maxima can be too conservative, and many different combinations of load components can result in the worst value of the limit state equations. The use of contemporaneous load vectors is typically non-conservative. Therefore, in practice, limit state checks are done for each possible load vector, from each time step of a simulation. This is not feasible when performing reliability assessments and structural optimization, where additional, time-consuming computations are involved for each load vector. We therefore propose to use Pareto-optimal loads, which are a small set of loads that together represent all possible worst case scenarios. Simulations with two reference wind turbines show that this approach can be very useful for jacket structures, whereas the design of monopiles is often governed by the bending moment only. Even in this case, the approach might be useful when approaching the structural limits during optimization. [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. This paper presents an analytical gradient-based method to solve this problem in an efficient and effective way. The design sensitivities of the objective and constraint functions are evaluated analytically while the optimization of the structure is performed, subject to sizing, eigenfrequency, extreme load and fatigue load constraints. A case study was carried out for the OC4 jacket substructure to evaluate the method. Results show that an optimal jacket design with 52 percent structural mass reduction was attained in 27 iterations, while satisfying all design constraints under the simplified load cases used. Besides, it is shown that the analytical sensitivity analysis was more accurate and efficient than the often used finite difference approximations. It could avoid numerical artifacts that typically occur in the analysis of extreme load constraint sensitivities. [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]