The project

The design of offshore renewable energy (ORE) structures requires estimates of the joint extreme values of metocean variables. For example, the design of fixed or floating offshore wind turbines requires estimates of joint (concurrent) extremes of wave heights and wind speeds. Similarly, the design of tidal turbines requires estimates of the joint extremes of wave heights and current speeds, whilst for wave energy converters the joint extremes of wave heights and periods are important.

The aim of the proposed research is to address the several research challenges in this area by (i) extending existing multivariate statistical models to create a single coherent and straightforward framework in which to estimate multivariate extremes, and (ii) developing open-source software for estimating multivariate metocean extremes, based on the methodologies developed in (i). The objectives of the proposed research are:

  1. Extend the existing composite model approach to higher dimensions;
  2. Develop a novel single-model approach for multivariate extremes;
  3. Integrate the models into open-source software for estimation of multivariate extremes;
  4. Demonstrate the application of models to extreme loading of ORE structures.

Dr Ed Mackay - Principal Investigator

Dr Ed Mackay is Research Fellow in the Offshore Renewable Energy Group at the University of Exeter, working on the design and modelling of floating offshore wind turbines. Prior to joining the University of Exeter, Ed worked in the wave and tidal energy industry for 12 years, most recently for Wavepower Technologies, where he led the work on physical testing and numerical modelling of wave energy converters (WECs). Ed spent 6 years working in the wave and tidal energy group at DNV GL (formerly Garrad Hassan), where he worked on the development of the WaveDyn and WaveFarmer software tools for modelling WECs and WEC arrays. During this time he worked on the numerical modelling and physical testing of over 20 WEC concepts.

Read more about Dr Ed Mackay

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