Technological advancements for anti-icing and de-icing offshore wind turbine blades
Bibliographic record
Abstract
Due to the abundance of wind resources in marine environments, offshore wind turbines (OWTs) have gained significant attention in recent years. However, their blades are prone to ice accretion when operating in cold climates. Ice accretion on OWT blades induces surface roughness thereby reducing the aerodynamic performance of the turbine. Although various ice mitigation techniques have been explored, tested, and applied to onshore wind turbines, their feasibility for offshore application remains uncertain. Therefore, this review conducts a comprehensive feasibility study, examining each ice mitigation technique, its fundamental principles, advantages, disadvantages, and the potential for successful integration on OWT blades. The study also highlights the challenges of implementing these techniques in harsh offshore environments, providing critical insights for future research in this field. • This article reviews techniques for preventing ice accretion on wind turbine blades, focusing on OWT. • It explores various passive and active anti-icing methods, analysing their principles, advantages, challenges, and impacts. • The review concludes that a hybrid approach of SHC and thermal heating offers promising potential for energy efficiency.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".