A large-scale rotor test platform for atmospheric icing simulation and passive ice protection evaluation
Bibliographic record
Abstract
• Large-scale bench replicates atmospheric icing on wind turbine blades • Realistic freezing rain simulated with controlled LWC, MVD, and wind speed • New method quantifies ice adhesion via centrifugal shedding on full blades • Complete analysis of results uses cohesive and adhesive strength of ice Ice accumulation on the leading edges of wind turbines significantly reduces energy output, aerodynamic efficiency, and structural integrity. In recent years, research into ice protection systems has intensified, driven by the growing deployment of turbines and rotorcraft in cold climates. However, the development and validation of these systems are hindered by the rarity and unpredictability of natural icing events, making field-testing impractical. To address this, there is a pressing need for large-scale laboratory platforms that can reliably replicate atmospheric icing conditions. This study presents the design and implementation of a horizontal axis rotating-blade test stand installed within a cold room, enabling controlled simulation of representative icing scenarios. Unlike most existing setups, the platform reproduces realistic icing through a complete accumulation-pause-shedding sequence, allowing precise characterization of ice morphology prior to detachment. The procedure leverages centrifugal ice adhesion measurements and is validated using a metallic substrate to ensure repeatability and comparability with prior studies and small-scale tests for two specific ice types problematic for wind turbines. The configuration captures both adhesive and cohesive failure mechanisms at a meaningful scale, reflecting fracture behaviors typically observed on full-sized turbine blades. The validation tests show results in accordance with other similar test benches with ice adhesion strength measured at 0,25MPa at -12°C and 0,15MPa at -5°C. The cohesive strength of the ice, required for the evaluation of ice adhesion strength in partial detachment cases, is measured specifically for this test bench as well using a dedicated apparatus. The setup is particularly suited for evaluating passive ice protection strategies, such as icephobic surface coatings, under realistic and reproducible conditions. Consequently, it provides a robust foundation for future studies by bridging the gap between small-scale laboratory methods and full-scale field behavior.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".