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- W4320341769 abstract "We introduce a gradient-free data-driven framework for optimizing the power output of a wind farm based on a Bayesian approach and large-eddy simulations. In contrast with conventional wind farm layout optimization strategies, which make use of simple wake models, the proposed framework accounts for complex flow phenomena such as wake meandering, local speed-ups and the interaction of the wind turbines with the atmospheric flow. The capabilities of the framework are demonstrated for the case of a small wind farm consisting of five wind turbines. It is shown that it can find optimal designs within a few iterations, while leveraging the above phenomena to deliver increased wind farm performance." @default.
- W4320341769 created "2023-02-13" @default.
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- W4320341769 date "2023-02-02" @default.
- W4320341769 modified "2023-09-23" @default.
- W4320341769 title "Bayesian optimization of the layout of wind farms with a high-fidelity surrogate model" @default.
- W4320341769 doi "https://doi.org/10.48550/arxiv.2302.01071" @default.
- W4320341769 hasPublicationYear "2023" @default.
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