Large‐eddy simulations of the evolution of imposed turbulence in forced boundary layers in a very long domain
Bibliographic record
Abstract
Abstract The technique of using imposed turbulence in combination with a forced boundary layer in order to model the atmospheric boundary layer is analyzed for a very long domain using large‐eddy simulations with different combinations of prescribed velocity profiles and pregenerated turbulence fields based on the Mann model. The ambient flow is first studied in the absence of wind turbines. The velocity profiles undergo a transition throughout the domain with a velocity increase of 10% to 15% close to the ground far downstream in the domain. The turbulence characteristics close to the turbulence plane are, as expected, similar to those of the added Mann turbulence. The turbulence will then undergo a transition throughout the domain to finally reach a balance with the shear profile at a certain downstream distance. This distance is found to depend on the turbulence level of the added Mann turbulence planes. A lower Mann turbulence level generally results in a shorter “balancing” distance. Secondly, a row of 10 turbines is imposed in the simulations at different distances from the plane of turbulence in order to determine how the distance affects wake conditions and power production levels. Our results show that a “balancing” distance is needed between the turbulence plane and the first turbine in the row in order to ensure nonchanging ambient conditions throughout the turbine row. This introduces an increase in the computational costs. The computational cost for the forced boundary technique is normally lower compared with using precursor simulations, for longer domains; however, this needs to be verified further.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".