Global Gust Climate Evaluation and Its Influence on Wind Turbines
Bibliographic record
Abstract
Strong gusts negatively affect wind turbines in many ways. They (1) harm their structural safety; (2) reduce their wind energy output; and (3) lead to a shorter wind turbine rotor blade fatigue life. Therefore, the goal of this study was to provide a global assessment of the gust climate, considering its influence on wind turbines. The gust characteristics analyzed were: (1) the gust speed return values for 30, 50 and 100 years; (2) the share of gust speed exceedances of cut-out speed; and (3) the gust factor. In order to consider the seasonal variation of gust speed, gust characteristics were evaluated on a monthly basis. The global monthly wind power density was simulated and geographical restrictions were applied to highlight gust characteristics in areas that are generally suitable for wind turbine installation. Gust characteristics were computed based on ERA-interim data on a 1° × 1° spatial resolution grid. After comprehensive goodness-of-fit evaluation of 12 theoretical distributions, Wakeby distribution was used to compute gust speed return values. Finally, the gust characteristics were integrated into the newly developed wind turbine gust index. It was found that the Northeastern United States and Southeast Canada, Newfoundland, the southern tip of South America, and Northwestern Europe are most negatively affected by the impacts of gusts. In regions where trade winds dominate, such as eastern Brazil, the Sahara, southern parts of Somalia, and southeastern parts of the Arabian Peninsula, the gust climate is well suitable for wind turbine installation.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".