Convective heat transfer and experimental icing aerodynamics of wind turbine blades
Bibliographic record
Abstract
The total worldwide base of installed wind energy peak capacity reached 94 GW by the end of 2007, including 1846 MW in Canada. Wind turbine systems are being installed throughout Canada and often in mountains and cold weather regions, due to their high wind energy potential. Harsh cold weather climates, involving turbulence, gusts, icing and lightning strikes in these regions, affect wind turbine performance. Ice accretion and irregular shedding during turbine operation lead to load imbalances, often causing the turbine to shut off. They create excessive turbine vibration and may change the natural frequency of blades as well as promote higher fatigue loads and increase the bending moment of blades. Icing also affects the tower structure by increasing stresses, due to increased loads from ice accretion. This can lead to structural failures, especially when coupled to strong wind loads. Icing also affects the reliability of anemometers, thereby leading to inaccurate wind speed measurements and resulting in resource estimation errors. Icing issues can directly impact personnel safety, due to falling and projected ice. It is therefore important to expand research on wind turbines operating in cold climate areas. This study presents an experimental investigation including three important fundamental aspects: 1) heat transfer characteristics of the airfoil with and without liquid water content (LWC) at varying angles of attack; 2) energy losses of wind energy while a wind turbine is operating under icing conditions; and 3) aerodynamic characteristics of an airfoil during a simulated icing event. A turbine scale model with curved 3-D blades and a DC generator is tested in a large refrigerated wind tunnel, where ice formation is simulated by spraying water droplets. A NACA 63421 airfoil is used to study the characteristics of aerodynamics and convective heat transfer. The current, voltage, rotation of the DC generator and temperature distribution along the airfoil, which are used to calculate heat transfer coefficients, are measured using a Data Acquisition (DAQ) system and recorded with LabVIEW software. The drag, lift and moment of the airfoil are measured by a force balance system to obtain the aerodynamics of an iced airfoil. This research also quantifies the power loss under various icing conditions. The data obtained can be used to valid numerical data method to predict heat transfer characteristics while wind turbine blades worked in cold climate regions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".