Interpolation of confidence intervals for fatigue design using a surrogate model

Abstract

For complex systems, the applicability of surrogate models has shown the potential to enable accurate assessments using a reduced batch of data and to compile information about large datasets. These behave as black-box functions that replace a series of inputs/outputs. In the present work, a Kriging surrogate is used to predict confidence intervals in an offshore wind turbine tower fatigue design. Uncertainty in fatigue due to loading is highly connected to the mean. One year operational fatigue results is used to validate the results. The Kriging is applied to replicate the yearly states of operation, and successfully predicts intervals of confidence for the long-term fatigue design. Regarding the interest of data analysis, the approach implemented is characterized by its flexibility and capability of approaching any problem that can be characterized by a single variable. Being therefore an interesting tool in decision schemes where large datasets are available or prediction of unknown outputs is required.