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

Abstract

Abstract. 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.

Review of Recent Offshore Wind Turbine Research and Optimization Methodologies in Their Design

“…Lastly, this approximate model was used during the optimal iterative procedure with a global optimization algorithm to gain the final best design point considering uncertainties. Recently, a study by Stieng and Muskulus (2020) [117] presented a general methodology that implemented recent developments in gradient-based design optimization, particularly the use of analytical gradients, within the context of reliability-based design optimization methods. The study divided the offshore wind turbine's uncertain response into probabilistic and deterministic parts.…”

Quantifying and clustering the wake-induced perturbations within a wind farm for load analysis

“…Wake models allow us to simulate the wind conditions' distribution at each turbine, however, the RBD step is too costly to be performed for each turbine. For further details regarding the RBD, one may refer to the work of [7,8,9,10,11], proposing several approaches to reduce the computational cost of this step. In order to speed up the RBD at the scale of a wind farm, the present work aims at building clusters of WT similarly affected by the wake.…”

A two-step procedure for time-dependent reliability-based design optimization involving piece-wise stationary Gaussian processes

“…Other approaches combine metamodels with PMA, SORA (Zhang et al (2020b)) or SLA (Zhang et al (2020a)). The method proposed in Stieng and Muskulus (2020) that we will call the Stieng method can deal with general RBDO problems under the assumption that the performance function can be approximated by the product of two functions: one depending only on the design variables which is the performance function evaluated at the mean values of the uncertainties and the other one depending on the uncertain variables. A metamodel is fitted on the second function.…”