Effects of Ice, Wind, Waves and Currents on Offshore Wind Turbines Destined for Atlantic Canada
Bibliographic record
Abstract
Abstract The Canadian provinces of Newfoundland and Labrador (NL) and Nova Scotia (NS) are conducting assessment studies to investigate the use of offshore wind turbines to generate electricity. This paper deals with numerical simulations of two 15 MW offshore monopiles wind turbines: One is located off the western coast of Newfoundland Island (in water depth of 45 m) and the other is located off the northern coast of Nova Scotia (in water depth of 60 m). The two monopiles are similar, each has a diameter of 10 m, a tower of 150 m high, and blades rotor diameter of 240m. The environmental conditions (ice, wind, waves and currents) are also fairly similar; however, the water depth is different. The objective of this work is to conduct numerical parametric analyses to investigate the effects of environmental conditions (ice, wind, waves, and currents) on the behaviour of the monopiles when operating in “production mode”. All simulations were carried out using an open-source code, called OpenFAST. The environmental parameters were obtained from a metocean investigation of the region. In this paper, including ice loads on the monopiles in offshore Atlantic Canada is a significant contribution to the literature. For the simulations, the ice thickness was varied between 0.5 m and 1.6 m, the wind speeds were varied from 10 m/s to 20 m/s, the wave heights were varied between 1.7 m to 4.9 m, and current speeds were varied from 0.05 m/s to 0.25 m/s. The results of the simulations are presented and discussed, and conclusions and recommendations are provided.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".