Dept. of Civil, Structural & Environmental Engineering
Probabilistic analysis of offshore wind turbine towers
Keywords: Offshore Wind Energy; Extreme environment; Probabilistic analysis.
Offshore wind energy experienced an exponential growth in installed power since the beginning of the current century. While this growing trend is expected to continue, further growth of the sector imposes more demanding engineering methodologies.
One of the ways seen to achieve new competitiveness in the offshore wind sector is to tackle the uncertainty naturally inherent to the physical systems through probabilistic considerations that evaluate the inherent variation of the physical quantitates used to evaluate engineering systems. As a result, the design processes get progressively less deterministic and the designer has a more focused perception of the risk associated to the different modes of failure of a system.
In parallel with the necessity to increase competiveness in the sector, with further developments, it is foreseen that the technologies will get gradually bigger and will be installed in more remote and challenging environments. Thus, as the hub heights increase, the size of the wind turbine units they support also continue to get larger, wind farms are located in more severe offshore environments and in active seismic zones, the necessity to employ advanced design techniques, such as probabilistic methods, to optimize structural design becomes apparent.
The main objective of the presented work is then to employ the principles of structural reliability theory and probabilistic analysis to optimize the design of offshore wind turbine towers considering possible combinations of extreme environmental loads such as wind and wave effects with natural hazards.
The current project is being developed under the European Project TRUSS (TRAINING IN REDUCING UNCERTAINTY IN STRUCTURAL SAFETY) promoted by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 642453.
Project Supervisor: Associate Prof. Alan O'Connor