Modelling of the structural performance of arctic offshore wind turbines
Bibliographic record
Abstract
Offshore wind energy considered as the future of green energy, has known a consistent growth globally over the past decades with Europe being a major player followed by the US and Canada. The first large scale offshore wind farm of 160 MW (Horns Rev) was built at 14 m from the west coast of the Danish North sea site in 2002. This market of sustainable energy has also extended to other regions in the world most especially in Asia.[17] There is a large potential market for offshore wind turbines in northern regions with extreme cold climate such as Northern Asia, North America and Northern Europe. According to the European Wind Energy Association (EWEA), approximately 20% of the European offshore wind turbines producing in total 10 GW will be installed in the Baltic Sea by 2020. Offshore wind turbines in Northern regions with extreme cold conditions will experience additional ice loads apart from aerodynamic and hydrodynamic loads.[30] The main purpose of this research was to aid in the development of a tool, for structural design purposes of offshore wind turbines, that describes different environmental load conditions and site specific soil properties. This tool was to enable the user to easily select site specific environmental and soil properties, run a simulation and possibly generate an optimized wind turbine substructure. This thesis consisted of research on wind energy and finite element numerical simulations using the engineering software Abaqus. This study focused on the modelling of the structural response of monopile offshore wind turbines under aerodynamic, hydrodynamic and ice loads taking into consideration the soil-structure interaction. A specific site at the Gulf of Bothnia (Tahkoluoto) made of glacial till seabed type was used as a case study. To arrive at a conclusion, the lateral response and Eigenfrequency of a 5 MW baseline offshore wind turbine under dynamic ice, wind and wave loads was evaluated considering the soil-monopile interaction idealized using the coupled spring foundation model. The results were validated using similar results from a previous simulation done by VTT. The result of this experiment suggests that in the case of lack of in-situ site specific data, adequate selection and evaluation of the soil properties and soil models should be done as the soil-structure interaction can significantly affect the structural response.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".