On gradient‐based optimization of jacket structures for offshore wind turbines

Oest, Jacob; Sandal, Kasper; Schafhirt, Sebastian; Stieng, Lars Einar S.; Muskulus, Michael

doi:10.1002/we.2206

Cited by 20 publications

(10 citation statements)

References 28 publications

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“…In particular, some recent studies have demonstrated the viability and, in most cases, advantages of analytical sensitivities in gradient-based formulations. This has been shown for static ), quasi-static (Oest et al, 2017 and dynamic (Chew et al, 2016) loading conditions (see also Oest et al (2018) for a comparison of these three approaches). In general, these approaches make the design optimization problem more efficient and stable, 35 3 https://doi.…”

mentioning

confidence: 63%

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

Stieng¹,

Muskulus²

2019

Preprint

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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. 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 constraints 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.

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confidence: 63%

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

Stieng¹,

Muskulus²

2019

Preprint

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“…The boundaries are chosen conservatively by means of quite narrow design variable ranges (see Table 1), i.e., meaningful parameters that do not exhaust the possible range given by the structural code checks, in a realistic range around the values of the OC4 jacket (Popko et al, 2014). Only three-or four-legged structures with three, four, and five bays are regarded as valid solutions for this study.…”

Section: Boundaries Of Design Variables and Other Parametersmentioning

confidence: 99%

“…Oest et al (2018) presented a jacket optimization study, where different simulation codes were deployed to perform structural code checks. All mentioned works, except for the last one, represent tube sizing algorithms applied to the Offshore Code Comparison Collaboration Continuation (OC4) jacket substructure (Popko et al, 2014) for the National Renewable Energy Laboratory (NREL) 5 MW reference turbine (Jonkman et al, 2009), 3 where the initial structural topology is maintained even in the case of a strong tube diameter and wall thickness variations. Furthermore, it can be stated that all proposals share the entire mass of the jacket as an objective function to be minimized, which is meaningful in terms of tube sizing.…”

Section: Introductionmentioning

confidence: 99%

Wrong subject of previous reply (AC2)

Häfele¹

2018

Preprint

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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.

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“…Employing the genetic algorithm (GA), Ma et al [14] optimized a 100 m PC tower system for a 5 MW wind turbine and discussed the advantages of a PC wind turbine tower. Oest et al [15] explored three different state-of-the-art analytical gradient-based optimization approaches to minimize the mass of a jacket structure for wind turbines considering fatigue and frequency constraints and provided insight into critical structural and modeling parameters. Adopting GA, Chen et al [16] suggested that the optimal height of the concrete segment should be 80.5 m for one 120 m PCSH wind turbine tower.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Wind Turbine Towers Optimization with a Parallel Updated Particle Swarm Algorithm

Chen

et al. 2021

Applied Sciences

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The prestressed concrete–steel hybrid (PCSH) wind turbine tower, characterized by replacing the lower part of the traditional full-height steel tube wind turbine tower with a prestressed concrete (PC) segment, provides a potential alterative solution to transport difficulties and risks associated with traditional steel towers in mountainous areas. This paper proposes an optimization approach with a parallel updated particle swarm optimization (PUPSO) algorithm which aims at minimizing the objective function of the levelized cost of energy (LCOE) of the PCSH wind turbine towers in a life cycle perspective which represents the direct investments, labor costs, machinery costs, and the maintenance costs. Based on the constraints required by relevant specifications and industry standards, the geometry of a PCSH wind turbine tower for a 2 MW wind turbine is optimized using the proposed approach. The dimensions of the PCSH wind turbine tower are treated as optimization variables in the PUPSO algorithm. Results show that the optimized PCSH wind turbine tower can be an economic alternative for wind farms with lower LCOE requirements. In addition, compared with the traditional particle swarm optimization (PSO) algorithm and UPSO algorithm, the proposed PUPSO algorithm can enhance the optimization computation efficiency by about 60–110%.

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On gradient‐based optimization of jacket structures for offshore wind turbines

Cited by 20 publications

References 28 publications

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

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

Wrong subject of previous reply (AC2)

Hybrid Wind Turbine Towers Optimization with a Parallel Updated Particle Swarm Algorithm

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