Numerical Research on the Vortex Center on the Forward-Swept 3-D Wind Turbine Blades at Low Rotational Speed
Bibliographic record
Abstract
This paper studies the CFD simulation of forward three-dimensional (3-D) horizontal axis wind turbine (HAWT) blades. Using logarithmic grid and Q-criterion to learn the vortex dynamics around the blades at low rotational speed. The computational fluid dynamics (CFD) simulation uses Q-criterion to probe vortices and logarithmic grid to emphasize the micro-gridding effect of the turbulent boundary layer. The visualization & measurement of the simulation results give the coefficient of pressure (Cp). For forward 3-D wind turbine blade, at low rotational speed, the strongly accelerated laminar region surrounds the lower blade, and the decelerated tip blade region coalesce each other give rise to a reverse limiting streamline, eroding the laminar region further until a little is left on the tip of the blade. The "reverse limiting streamline" grows inward radially, the area is narrowing closing to the leading edge of the blade tip. The second side of the rolled-up vortex appears the velocity ratio (Uc/Ulocal) of the second vortices are higher than the main vortex cores. For radius R=1.547 m, U=12 m/s, at 210 RPM, CL and CD values reach a maximum with fully laminar tip conditions. While at 120 RPM, the CL and CD values reach a minimum in the absence of laminar tips. The results show the detailed vortex dynamic pattern surround the blades, give more understanding to design laminar 3-D blade toward a noiseless wind turbine system.
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 | 0.001 |
| Scholarly communication | 0.000 | 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".