Wind Energy Science
Wind Energy Science
WES
Articles | Volume 9, issue 10
Articles | Volume 9, issue 10
https://doi.org/10.5194/wes-9-1885-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-9-1885-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 9, issue 10
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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Cited articles

Abdallah, I., Lataniotis, C., and Sudret, B.: Parametric hierarchical kriging for multi-fidelity aero-servo-elastic simulators – Application to extreme loads on wind turbines, Probabilistic Eng. Mech., 55, 67–77, https://doi.org/10.1016/j.probengmech.2018.10.001, 2019. a
Avendaño-Valencia, L. D., Abdallah, I., and Chatzi, E.: Virtual fatigue diagnostics of wake-affected wind turbine via Gaussian process regression, Renew. Energy, 170, 539–561, https://doi.org/10.1016/j.renene.2021.02.003, 2021. a
Bishop, C. M.: Mixture density networks, Tech. rep., Aston University, ISBN NCRG/94/004, 1994. a, b, c
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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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