Abstract
Abstract. The structural optimization problem of jacket substructures for offshore wind turbines is commonly regarded as a pure tube dimensioning problem, minimizing the entire mass of the structure. However, this approach goes along with the assumption that the given topology is fixed in any case. The present work contributes to the improvement of the state of the art by utilizing more detailed models for geometry, costs, and structural design code checks. They are assembled in an optimization scheme, in order to consider the jacket optimization problem from a different point of view that is closer to practical applications. The conventional mass objective function is replaced by a sum of various terms related to the cost of the structure. To address the issue of high demand of numerical capacity, a machine learning approach based on Gaussian process regression is applied to reduce numerical expenses and enhance the number of considered design load cases. The proposed approach is meant to provide decision guidance in the first phase of wind farm planning. A numerical example for a National Renewable Energy Laboratory (NREL) 5 MW turbine under FINO3 environmental conditions is computed by two effective optimization methods (sequential quadratic programming and an interior-point method), allowing for the estimation of characteristic design variables of a jacket substructure. In order to resolve the mixed-integer problem formulation, multiple subproblems with fixed-integer design variables are solved. The results show that three-legged jackets may be preferable to four-legged ones under the boundaries of this study. In addition, it is shown that mass-dependent cost functions can be easily improved by just considering the number of jacket legs to yield more reliable results.
Highlights
The substructure contributes significantly to the total capital expenses of offshore wind turbines and to the levelized costs of offshore wind energy, which are still high compared to the onshore counterpart (Mone et al, 2017)
A basis to address these points was given by Häfele et al (2018a), where appropriate geometry, cost, and structural code check models for fatigue and ultimate limit states were developed
This paper presents a study on jacket substructures, based on optimization
Summary
The substructure contributes significantly to the total capital expenses of offshore wind turbines and to the levelized costs of offshore wind energy, which are still high compared to the onshore counterpart (Mone et al, 2017). An optimization scheme which addresses the early design phase is highly desirable to provide decision guidance for experienced designers Proposals tackling this kind of problem were given by Damiani (2016) and Häfele and Rolfes (2016), where technically oriented jacket models were proposed but lacking fatigue limit state checks in the first and detailed load assumptions in the second case. A basis to address these points was given by Häfele et al (2018a), where appropriate geometry, cost, and structural code check models for fatigue and ultimate limit states were developed In this study, these models are deployed within an optimization scheme to obtain optimal design solutions for jacket substructures. The work ends with a consideration of benefits and limitations (Sect. 6) and conclusions (Sect. 7)
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