A Viscous Three-Dimensional Differential/Actuator-Disk Method for the Aerodynamic Analysis of Wind Farms
Bibliographic record
Abstract
Computational Fluid Dynamics (CFD) is a promising tool for the analysis and optimization of wind turbine positioning inside wind parks (also known as wind farms) in order to maximize power production. In this paper, 3-D, time-averaged, steady-state, incompressible Navier-Stokes equations, in which wind turbines are represented by surficial forces, are solved using a Control-Volume Finite Element Method (CVFEM). The fundamentals of developing a practical 3-D method are discussed in this paper, with an emphasis on some of the challenges that arose during their implementation. For isolated turbines, results have indicated that the proposed 3-D method attains the same level of accuracy, in terms of performance predictions, as the previously developed 2-D axisymmetric method and the well-known momentum-strip theory. Furthermore, the capability of the proposed method to predict wind turbine wake characteristics is also illustrated. Satisfactory agreement with experimental measurements has been achieved. The analysis of a two-row periodic wind farm in neutral atmospheric boundary layers demonstrate the existence of positive interference effects (venturi effects) as well as the dominant influence of mutual interference on the performance of dense wind turbine clusters.
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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".