Surface Ice Effects on the Extreme and Fatigue Loading of Bottom Fixed Offshore Wind Turbines
Bibliographic record
Abstract
As interest and investment in offshore wind projects increases worldwide, some turbines will be installed in locations where ice of significant thickness forms on the water’s surface. This ice moves under the driving forces of wind, water, and thermal effects and may result in substantial forces on bottom-fixed structures. The North and Baltic Seas in Europe have begun to see significant wind project development and the Great Lakes of the United States and Canada, all regions that experience significant floating ice, will likely see wind projects in the near future. Because the forces imparted by surface ice are dynamic in nature, design of the support structures for these projects will require the calculation of the simultaneous effects of turbine operational, wind, and ice forces. The IEC standard for offshore wind turbine design and the ISO standard for offshore structures provide requirements and algorithms for the calculation of ice-induced forces; however, currently none of the widely used wind turbine dynamic simulation codes provide the ability to calculate and apply dynamic ice loads. A new suite of subroutines has been developed by the authors, collectively called IceFloe, which meets the requirements of these standards for design of support structures in ice prone waters, and has been coupled and tested with four wind turbine simulation codes. The IceFloe routines have been linked and tested with FAST, a tool developed under the management of the National Renewable Energy Laboratory. This integrated tool has been run with a 5 MW example turbine and ice conditions from selected areas of the Great Lakes with a range of ice thickness and velocity. Extreme and fatigue load calculations have been made and compared with and without the effects of ice loading. Example results from these calculations are presented. Results indicate that surface ice loading can impact the design of offshore support structures.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".