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Record W4413391846 · doi:10.1115/omae2025-155486

A Numerical Investigation of the Impact of Waves on a Large Floating Wind Farm in Northwest Atlantic

2025· article· en· W4413391846 on OpenAlexaff
Jahrul Alam

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoastal and Marine Dynamics
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsMarine engineeringEnvironmental scienceGeologyMeteorologyEngineeringPhysics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.007
GPT teacher head0.220
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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