Understanding the Influence of Turbine Geometry and Atmospheric Turbulence on Wind Turbine Wakes
Bibliographic record
Abstract
A wind turbine wake is divided into two regions, near wake and far wake. In the near wake region, the flow is highly turbulent and is strongly influenced by the rotor geometry. In the far wake region, the influence of rotor geometry becomes less important as atmospheric effects become dominant. However, how turbine geometry and atmospheric condition affect the two wake regions is not well studied. In this work, the influence of atmospheric turbulence and the blade aerodynamic forces on wake development is studied using computational fluid dynamics (CFD) models. The CFD simulation results are based on an actuator disk model and an k–ε turbulence model. The effects of blade geometry are captured by prescribing aerodynamics forces exerted by a LM8.2 blade on an actuator disk, and are compared with that of an equivalent uniform normalized force, under two atmospheric turbulence conditions. The finding shows that the length of the near wake region is strongly affected by atmospheric turbulence, with the wake becoming fully developed as far as 2.5 rotor diameters downstream of the rotor under low turbulence conditions. Furthermore, the velocity profile in the far wake region is independent of the blade profile. In other words, in the cases studied, an actuator disk with an equivalent uniform force will produce nearly identical velocity profiles in the far wake region as one with nonuniform aerodynamic force profiles. These findings have implications on existing wake models where the far wake is the region of interest. Specifically, the beginning of the far wake region should be properly defined for each scenario, and that it is not necessary to provide detailed rotor geometry for far wake simulations.
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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) | 0.000 | 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".