Optimizing the Structural Lifetime of Monopile-based Offshore Wind Turbines with Genetic Algorithms: Is it worth planning for Lifetime Extension?

Optimizing the Structural Lifetime of Monopile-based Offshore Wind Turbines with Genetic Algorithms: Is it worth planning for Lifetime Extension?

Rhomberg, Matthieu (TU Delft Mechanical, Maritime and Materials Engineering)

Metrikine, A. (mentor)
Muskulus, Michael (mentor)
Lourens, E. (mentor)
Ziegler, Lisa (mentor)

Delft University of Technology
Norwegian University of Science and Technology (NTNU)

European Wind Energy Masters (EWEM)

2017-09-29

Optimization of structures in a domain with large uncertainties is rather difficult. This also applies for the offshore wind energy sector. For current offshore wind energy development locations with monopile-based support structures the fatigue limit state is the driving design criteria. These analyses are connected with long time domain evaluations to cover non-linearities. Model, statistical and data uncertainties lead to a combined fatigue damage prediction uncertainty. The former are either covered by a design fatigue factor or a material factor, which are stated in certification standards, e.g. DNVGL-ST-0126. The influence of mass changes regarding different lifetimes and the impact of this design fatigue factor has not been published yet.

Based on this, within this graduation project, the monopile support structure is optimized for different lifetimes in order to identify mass changes and influences of the design fatigue factor. Literature shows that automatized optimization using genetic algorithms in offshore wind energy is possible but limited, due to the algorithm methodology including a large number of design evaluations. This graduation project shows the applicability of Importance Sampling for load case reduction in a genetic algorithm optimization for offshore wind. Compared to previous approaches Importance Sampling assists to use a full certification procedure for fatigue limit state computations in a feasible amount of time with high fatigue life estimation accuracies. Subsequently, the fatigue limit state load case table is reduced by 93%. By optimizing the monopile with this reduced amount of load cases the algorithm is computationally feasible for the industry.

Rambøll simulation software for offshore wind turbine support structure design is used in combination with the genetic algorithm function in Matlab®. The combination of the software leads to the optimization of monopile based offshore wind support structures for different lifetimes. The algorithm runs with a reduced amount of load cases. Resulting critical fatigue damage values of converged designs are showing deviations from actual fatigue damage values using full fatigue limit state load case tables at maximum 6.6% and minimum 1.7%. This high accuracy leads to an optimization of monopile structures for desired lifetimes and consequently to the mass versus lifetime curve. A mass increase of approximately 22% is observed from 25 to 100 years lifetime. After reaching 75 years lifetime the curve shows a flattening behavior. Besides, parameter evolutions of optimized monopile designs are discussed in terms of different fatigue life. The design variables are embedment depth, cone angle, and corresponding wall thicknesses of monopile sections.

Summarized, this thesis proved the implementation of a full state of the art fatigue limit state computation in the genetic algorithm by Importance Sampling with reduced load cases and also visualized the impact of mass changes for different projected lifetimes. As a conclusive remark, the application of Importance Sampling for load case reduction in the design process opens new possibilities of optimization in the offshore wind energy sector.

Genetic Algorithm
Importance Sampling
Monopile optimization

http://resolver.tudelft.nl/uuid:9a9ea7c6-76f3-4e45-b920-7871d49b34ee

Student theses

master thesis

© 2017 Matthieu Rhomberg