Optimization of Offshore Steel Jackets: Review and Proposal of a New Formulation for Time-Dependent Constraints

Couceiro, Iván; París, José; Navarrina, F.; Guizan, Raquel; Colominas, Ignasi

doi:10.1007/s11831-019-09342-y

Cited by 6 publications

(4 citation statements)

References 28 publications

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“…Taking this to be a 98 % confidence interval and, for lack of other information, assuming that the uncertainty in global stiffness follows a normal distribution, we obtain that θ stiff ∼ N (1, 0.052), i.e., a coefficient of variation (CoV) of 0.052. As an independent confirmation, this is close to, if a bit higher, than what would be obtained from Andersen et al (2012) and Damgaard et al (2015) (each giving a coefficient of variation of about 0.04).…”

Section: Global Stiffnesssupporting

confidence: 82%

“…Summing these contributions and adding also a constant (deterministic) structural damping of 1.0, the final result is θ damp ∼ N (7.735, 0.89), a CoV of 0.115. The soil uncertainty obtained here is about the same as would be derived from Damgaard et al (2015). The aerodynamic damping is harder to verify with additional sources, and in principle the level of uncertainty is expected to be wind speed dependent.…”

Section: Global Dampingmentioning

confidence: 57%

“…However, it was seen that, at least with the particular cost formulations used, the solution was more or less the same as when a simpler mass-based objective function was used. Another recent study regarding deterministic support structure optimization was done in Couceiro et al (2019). Like the previous study, completely analytical sensitivities were not used.…”

Section: Introductionmentioning

confidence: 99%

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

Stieng

Muskulus

2020

Wind Energ. Sci.

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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. 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.

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Section: Global Stiffnesssupporting

confidence: 82%

Section: Global Dampingmentioning

confidence: 57%

Section: Introductionmentioning

confidence: 99%

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

Stieng

Muskulus

2020

Wind Energ. Sci.

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“…The above expression approaches S ip,max from above as r approaches infinity. For computations, r is typically taken to be 50-200 depending on desired accuracy (see Couceiro et al (2019) for a discussion of this for OWT applications). Note that, 25 algebraically speaking, S ip,max cancels out 1 .…”

Section: Constraintsmentioning

confidence: 99%

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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Surrogate model based on ANN for the evaluation of the fundamental frequency of offshore wind turbines supported on jackets

Quevedo-Reina¹,

Álamo²,

Padrón³

et al. 2023

Computers & Structures

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Optimization of Offshore Steel Jackets: Review and Proposal of a New Formulation for Time-Dependent Constraints

Cited by 6 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

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

Surrogate model based on ANN for the evaluation of the fundamental frequency of offshore wind turbines supported on jackets

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