A Numerical Investigation of the Impact of Waves on a Large Floating Wind Farm in Northwest Atlantic
Bibliographic record
Abstract
Abstract This study investigated the combined effects of waves and atmospheric turbulence on the power output of a deep-water floating offshore wind farm in the Northwest Atlantic. Using a scale-adaptive large-eddy simulation (LES) framework, we modelled a floating wind farm comprising an array of 15 MW turbines with 240-meter rotor diameters, staggered with 5 MW turbines featuring 126-meter rotor diameters. The marine atmospheric boundary layer (MABL) was simulated using wave drag parameterization, Monin-Obukhov similarity theory, and stochastic turbulence forcing. The simulations incorporated rated wind speeds with varying wind directions and wave amplitudes ranging 0.2 to 2 meters, resolving turbulence structures using 107 million grid points and a vortex stretching-based subgrid model. Turbine wakes were modeled with a Gaussian actuator disk approach, including wave effects. Data analysis validated the LES framework against met-ocean environments and identified dominant frequency ranges influenced by waves using Proper Orthogonal Decomposition (POD) and wavelet transforms. Results highlight that atmospheric turbulence is the primary driver of power fluctuations, enhancing overall power output through vertical flux entrainment. Large-amplitude waves were found to modulate turbulence at higher frequency ranges, leading to improved turbine performance under certain conditions. Proper Orthogonal Decomposition (POD) and wavelet-based analysis revealed dominant flow structures influenced by wave-induced wind stress. These findings emphasize the critical role of pitch control strategies that account for wind-wave misalignment and Ekman spiral effects, particularly for spar-based floating turbine platforms. This research underscores the need for integrating wind-wave interactions into the design and operation of large floating wind farms to optimize energy production and structural resilience in complex marine environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".