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Statistical modeling of fully nonlinear hydrodynamic loads on offshore wind turbine foundations using wave episodes and targeted CFD simulations through active sampling
  • Stephen Guth,
  • Eirini Katsidoniotaki,
  • Themistoklis Sapsis
Stephen Guth
Massachusetts Institute of Technology

Corresponding Author:[email protected]

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Eirini Katsidoniotaki
Uppsala Universitet
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Themistoklis Sapsis
Massachusetts Institute of Technology
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For many design applications in offshore engineering, including offshore wind turbine foundations, engineers need accurate statistics for kinematic and dynamic quantities, such as hydrodynamic forces, whose statistics depend on the stochastic sea surface elevation. Nonlinear phenomena in the wave--structure interaction require high-fidelity simulations to be analyzed accurately. However, accurate quantification of statistics requires a massive number of simulations, and the computational cost is prohibitively expensive. To avoid that cost, this study presents a machine learning framework to develop a reliable surrogate model that minimizes the need for computationally expensive numerical simulations, which is implemented for the monopile foundation of an offshore wind turbine. This framework consists of two parts. The first focuses on dimensionality reduction of stochastic irregular wave episodes and the resulting hydrodynamic force time series. The second of the framework focuses on the development of a Gaussian process regression surrogate model which learns a mapping between the wave episode and the force-on-structure. This surrogate uses a Bayesian active learning method that sequentially samples the wave episodes likely to contribute to the accurate prediction of extreme hydrodynamic forces in order to design subsequent CFD numerical simulations. Additionally, the study implements a spectrum transfer technique to combine CFD results from quiescent and extreme waves. The principal advantage of this framework is that the trained surrogate model is orders of magnitude faster to evaluate than the classical modeling methods, while built-in uncertainty quantification capabilities allows for efficient sampling of the parameter using with the CFD tools traditionally employed.
06 Jul 20231st Revision Received
07 Jul 2023Submission Checks Completed
07 Jul 2023Assigned to Editor
07 Jul 2023Review(s) Completed, Editorial Evaluation Pending
20 Jul 2023Reviewer(s) Assigned
24 Sep 2023Editorial Decision: Revise Minor
05 Oct 20232nd Revision Received
06 Oct 2023Submission Checks Completed
06 Oct 2023Assigned to Editor
06 Oct 2023Review(s) Completed, Editorial Evaluation Pending
15 Oct 2023Editorial Decision: Accept