Numerical Investigation of Turbulent Flow around a Recent Horizontal Axis Wind Turbine using Low and High Reynolds Models
Bibliographic record
Abstract
The effects of different Reynolds Averaged Navier Stokes (RANS) turbulence models on two near-wall approaches using high and low Reynolds models on predicting performance of horizontal axis wind turbines (HAWTs) were studied for a range of wind conditions where flow over the rotor varied from fully attached to massively separated flow. This paper's main contribution is in establishing which RANS models can produce quantitatively reliable numerical predictions of turbulent flow around wind turbine rotors. The authors used measurements done by the new MEXICO (Model rotor EXperiments In COntrolled conditions) project in the German Dutch wind tunnels (DNW) in order to validate and test CFD (Computational Fluid Dynamic) codes. Four different RANS turbulence models were considered: Spalart-Allmaras; k-ε (RNG); k-ω SST; and the transition γ-Reθ model. At low wind speeds, it was found that all four models were good predictors of aerodynamic performance, and at high wind speeds, where the swirl effect was modeled using wall function corrections in both equations, the k-ε model was considered to be the best model: it was the most accurate within a reasonable computational time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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) | 0.001 | 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".