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. The need for cost-effective support structure designs for offshore wind turbines has led to continued interest in thedevelopment of design optimization methods. So far, almost no studies have considered the effect of uncertainty, andhence probabilistic constraints, on the support structure design optimization problem. In this work, we present ageneral methodology that implements recent developments in gradient-based design optimization, in particular the useof analytical gradients, within the context of reliability-based design optimization methods. Gradient-basedoptimization 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 ofthe uncertain response into a design-independent, probabilistic part and a design-dependent but completelydeterministic part, it is possible to computationally decouple the reliability analysis from the designoptimization. Furthermore, this decoupling makes no further assumption about the functional nature of the stochasticresponse, meaning that high-fidelity surrogate modeling through Gaussian process regression of the probabilistic partcan be performed while using analytical gradient-based methods for the design optimization. We apply this methodologyto several different cases based around a uniform cantilever beam and the OC3 Monopile and different loading andconstraint scenarios. The results demonstrate the viability of the approach in terms of obtaining reliable, optimalsupport structure designs and furthermore show that in practice only a limited amount of additional computationaleffort is required compared to deterministic design optimization. While there are some limitations in the appliedcases, and some further refinement might be necessary for applications to high-fidelity design scenarios, thedemonstrated capabilities of the proposed methodology show that efficient reliability-based optimization for offshorewind turbine support structures is feasible.

Reliability-based design optimization of offshore wind turbine support structures using analytical sensitivities and factorized uncertainty modeling