Stress-cycle fatigue design with Kriging applied to offshore wind turbines
Source abstract
Abstract
Abstract The present paper discusses a new methodology to assess stress-cycle fatigue design using meta-modelling. Research on its application is presented for offshore wind turbine towers. Kriging models are used to surrogate the complex time-domain stress-cycle fatigue assessment that demands multiple evaluations in the design phase. The presented development highlights the importance of having a notion of improvement for the problem of meta-modelling. Literature shows that, when meta-modelling complex engineering problems, the idea of improvement is not always considered. To tackle the problem of assessing fatigue in the design phase, a learning criterion is introduced. It has the particularity of relating to the physical description of the stress-cycle fatigue. Results of the proposed criterion are compared with meta-modelling using a Latin hypercube sampling, and with the standard design approach that bins environmental conditions for fatigue design calculations. A full 1 year validation sample is used to study convergence. As surrogate models for stress-cycle fatigue, Kriging models significantly decrease the efforts of the design procedure. Results showed that computational efforts can be reduced consistently by a factor of 5–8 without compromising accuracy. This may correspond to a reduction of up to approximately 85% of the effort needed to assess stress-cycle fatigue in the design phase. To conclude, it is important to highlight that the methodology presented has a universal character. It can be implemented to reduce computational time or assess the probabilistic response with only one requirement, the definition of a single representative indicator. In the case of fatigue, the short-term stress-cycle damage rate was considered.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Probabilistic and Robust Engineering Design · Wind and Air Flow Studies · Wind Energy Research and Development
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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