#### The EUROS Workpackage *Uncertainty quantification in wind and waves* in a nutshell

The loads on OWTs are currently computed by brute-force computations for different combinations of wind and waves. The probability distribution in wind loads is investigated in WP 1.1 and 1.2. Also for waves, models for the probability distribution are available. Current methods can determine which combinations to use, but only when the variables are not correlated. However, wind and waves are correlated. Therefore, these models are not suitable and the combinations have to be chosen more carefully in order to represent reality as good as possible. Through proper choices, the number of calculations can be reduced drastically. This can also give new insights about which other conditions might be related.

However, it might be that some inputs are more important than others in the computations of the loads on OWTs. Therefore, we want to combine the developed method with techniques for sensitivity analysis. By doing this, we can identify the inputs which have most effect on the loads on OWTs. Again, several methods are available, but cannot be used in practice due to either correlations in the data or due to a limited number of calculations available. We are now developing a new method which is based on the correlated data and which works with only a few calculations.

## 2019 |

Uncertainty quantification with dependent input data - including applications to offshore wind farms PhD Thesis University of Amsterdam, 2019, ISBN: 978-94-6323-848-9. |

Quantifying Data Dependencies with Rényi Mutual Information and Minimum Spanning Trees Journal Article Entropy, 21 (2), pp. 100, 2019, ISSN: 1099-4300. |

Efficient estimation of divergence-based sensitivity indices with Gaussian process surrogates Miscellaneous 2019, (submitted). |

## 2018 |

Uncertainty quantification with dependent inputs: wind and waves Inproceedings 6th European Conference on Computational Mechanics (ECCM 6) – 7th European Conference on Computational Fluid Dynamics (ECFD 7), ECCOMAS 2018. |

Clustering-based collocation for uncertainty propagation with multivariate dependent inputs Journal Article International Journal for Uncertainty Quantification, 8 (1), pp. 43–59, 2018. |

Quantifying dependencies for sensitivity analysis with multivariate input sample data Unpublished 2018. |