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Reliability-Based Design Optimization of a Spar-Type Floating Wind Turbine Support Structure

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Reliability-Based Optimization of Floating Wind Turbine Support Structures

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Abstract

Integrating reliability analyses into design optimization procedures of floating offshore wind turbine systems is not only extremely relevant in light of prevailing uncertainties but also benefits economic efficiency. However, design optimization is already complicated when the reliability element is taken into account, and it becomes even more difficult when, at the same time, a floating offshore wind turbine system, which is inherently complex, is considered. Thus, in this chapter, reliability-based design optimization of floating wind turbine systems is realized by means of an integrated framework, which necessitates a fair computing effort and time investment but still combines optimization approaches with reliability-based design and sophisticated modeling. In pre-processing, the reliability-based design optimization problem—comprising uncertainties, limit states, and environmental conditions as well as design variables, objectives, constraints, and reliability criteria—is specified. The realization of the reliability-based optimization process happens through quadratic regression, the response surface method, and Monte Carlo simulation. Prior to the execution of the optimization algorithm, several response surfaces for some distinct system geometries out of the entire optimization design space are generated, which finally feed into an interpolation approach for the reliability calculation during the iterative design optimization. The developed methodology proves that the coupling of reliability assessment and floating wind turbine design optimization is feasible in an efficient manner.

Note: This chapter is based on the publication by Leimeister & Kolios [33].

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Notes

  1. 1.

    Mathematical expressions for the two-parameter Weibull distribution can be found in the Appendix (cf. Sect. 6.5.1) in Eqs. 6.30–6.34.

  2. 2.

    Mathematical expressions for the three-parameter Weibull distribution can be found in the Appendix (cf. Sect. 6.5.2) in Eqs. 6.36–6.39.

  3. 3.

    The further mathematical expressions for the three-parameter Weibull distribution parameters for the SSS extreme event can be found in the Appendix (cf. Sect. 6.5.2) in Eqs. 6.40–6.42.

  4. 4.

    The mathematical derivation for determining the value that is associated with a specific percentile is presented in the Appendix (cf. Sect. 6.5.1) in Eq. 6.35.

  5. 5.

    The mathematical derivation for determining the value that is associated with a specific percentile, considering an extreme event, is presented in the Appendix (cf. Sect. 6.5.2) in Eq. 6.43.

  6. 6.

    As the interpolation approach is utilized within the iterative optimization algorithm (cf. Sect. 6.2.3.2) performed by means of the MoWiT-Dymola®-Python framework, the equations are presented in Python coding style and the Python function floor from the math module is utilized for rounding a value to the closest integer that is less than or equal to the input value.

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    Mareike Leimeister

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Leimeister, M. (2022). Reliability-Based Design Optimization of a Spar-Type Floating Wind Turbine Support Structure. In: Reliability-Based Optimization of Floating Wind Turbine Support Structures. Springer Theses. Springer, Cham. https://doi.org/10.1007/978-3-030-96889-2_6

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