Articles | Volume 9, issue 10
https://doi.org/10.5194/wes-9-1885-2024
https://doi.org/10.5194/wes-9-1885-2024
Research article
 | 
07 Oct 2024
Research article |  | 07 Oct 2024

Probabilistic surrogate modeling of damage equivalent loads on onshore and offshore wind turbines using mixture density networks

Deepali Singh, Richard Dwight, and Axelle Viré

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Latest update: 13 Dec 2024
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Short summary
The selection of a suitable site for the installation of a wind turbine plays an important role in ensuring a safe operating lifetime of the structure. In this study, we show that mixture density networks can accelerate this process by inferring functions from data that can accurately map the environmental conditions to the loads but also propagate the uncertainty from the inflow to the response.
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