Vorticity and Its Relationship to Vortex Separation, Dynamic Stall, and Performance, in an H-Darrieus Vertical-Axis Wind Turbine Using CFD Simulations
Bibliographic record
Abstract
Vortices play a critical role in the operation of VAWTs (Vertical-Axis Wind Turbines). In spite of this, most studies have approached these matters via the qualitative analysis of vortex shedding, and torque-extraction data. These approaches rely only on the visual observation of vortices that can lead to subjective interpretations. In our work, a 3D framework is employed to address this issue. On this basis, the present study establishes a relationship between vorticity, dynamic stall and turbine performance, by examining various locations along the span and the chord of the airfoil. To conduct this analysis, a 3D-CFD (Computational Fluid Dynamics) simulation of an H-Darrieus with a symmetrical NACA 0018, powered by 8 m/s winds, is considered. The CFD simulations are validated based on the agreement of calculated power coefficients, with those obtained from experimental data, reported in the technical literature, with deviations being lower than 4%. The simulation results for various TSRs (Tip Speed Ratios) report new findings concerning the critical stages of VAWT operation. This shows that there is a link between the maximum vorticity, the imminent vortex-separation condition and the dynamic stall, with this being a function of the various axial positions in the VAWT.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".