Support of offshore wind parks with synthetic aperture radar
Bibliographic record
Abstract
In all European countries with shallow coastal waters and a strong mean wind speed offshore wind parks are planned and constructed. The fast development of wind energy production in Europe led to an installation of more than 18,000 MW by the end of 2001. Up to date offshore wind farms of about 100 MW have been installed. Several projects for offshore wind farms are being planed and have already been approved in the North and Baltic Sea. In total they will have an output of more than 5000 MW in the near future. The construction and maintenance of offshore wind parks has to face the tough environmental conditions of the open sea, which results in extensive maintenance and expenses. Therefore reliable knowledge and forecast of the regional wind and the ocean wave fields is essential. Space borne synthetic aperture radar (SAR) data, as acquired by the European satellites ERS and ENVISAT as well as the Canadian satellite RADARSAT, provide wind fields with a sub-kilometre resolution and a coverage of up to 500 km swath width. They are thus ideally suited to investigate the spatial fine structure of the wind- and wave fields, which is one of the major factors in the optimal siting of wind farms. Due to their high coverage and resolution SAR data can provide information on the impact of the single turbines on the wind field, e.g., especially the turbulence in the wakes generated by the turbines, as well as the effect of the entire wind park on the local climate due to the increase of turbulence in the marine boundary layer. This study shows the potential of two dimensional high-resolution wind fields measured with space borne SAR to support the construction and operation of wind farms. The data can be used to minimize fatigue loading due to wind gusts as well as to provide short-term power forecasts in order to optimise the power output. Several examples of wind fields around the already existing offshore wind parks Utgrunden (South of Sweden) and Horns Rev (West of Denmark) and in the area of the sites under construction in the German Bight of the North Sea will be presented showing potential of SAR wind and wave measurements.
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) | 0.001 | 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 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".