Wake modeling and simulation of a real scale wind turbine using large eddy simulation and dynamic adaptive mesh refinement
Bibliographic record
Abstract
Wind energy has gained visibility in terms of progress and potential worldwide. In this context, scientific research in wind energy has shown significant progress, particularly in the development of computational fluid dynamics approaches that resolve the real scale wind turbines. The present study aims to apply Large Eddy Simulation (LES) to provide crucial spatial and temporal information on the flowfield surrounding a full-scale NREL 5 MW wind turbine in order to investigate the following: (i) wind turbine-generated wakes and their effects, (ii) interactions between the wind and turbine in terms of power generation, and (iii) wake effects for back to back turbines related to energy production efficiency. The numerical framework used in the simulations performs LES under a block-structured mesh that is dynamically refined to increase accuracy and reduce computational costs. The simulated 5MW NREL presented lower recovery velocities around the hub-height centerline in the near wake compared to other selected numerical results, which could be attributed to the simplification of the blade resolving geometry applied in the previous studies. Despite that, most results presented differences lower than 10% among the profiles. In addition, the power generation is validated with NREL experimental data with a difference of around 3.5%.
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 | 0.001 | 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".