Bibliographic record
Abstract
This chapter provides details about computational approaches used for modeling flows over VAWTs and quantifying their aerodynamic, power-producing, and self-starting performances. The sliding mesh technique is potentially the best available method for this purpose. The other better techniques, including immersed-boundary and overset mesh methods, presently suffer from requiring a large grid size to sufficiently resolve the boundary layers around the blades. Modeling turbulent flows around the turbines is also a challenge, since different turbulence models are found to perform better in different kinematic and flow conditions. Hybrid RANS-LES could be a more plausible choice, but the requirements for setting up the grid could be more strict for accurate results. Despite all these complications, computational simulations provide us with more insights about the instantaneous 3D nature and dynamics of the vertical flows. They also provide effective ways to link the vortex dynamics with the instantaneous production of unsteady forces, for which we also present a case study for self-starting phenomenon and performance of single- and dual-rotor VAWTs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".