Modeling of Turbulent Atmospheric Flow Around Tubular and Lattice Meteorological Masts
Bibliographic record
Abstract
This paper presents a numerical study of turbulent atmospheric flow around tubular and lattice meteorological masts and a wind speed and energy uncertainty calculation method based on the numerical results. The flow is described by the Reynolds averaged Navier–Stokes equations, complemented by the shear stress transport turbulence model, with modified constants and source terms added to maintain turbulence properties. ANSYS-CFX 11.0 is used to solve the computational model. The numerical results have been post-processed to account for the wind direction changes during the 10-min-measurement-period, and have been validated with mast data. From the numerical results, a wind speed and energy uncertainty calculation method that takes the wind rose into account is proposed. This technique provides a means to detect incorrectly mounted booms according to the local wind conditions. Most importantly, it produces uncertainty more conservatively than both the International Energy Agency (IEA) recommendations and the IEC-61400-121 (International Electrotechnical Commission) annex G norm. These differences stem from the use of a turbulence model in this paper, which predicts higher flow distortions due to the presence of the mast.
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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 | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".