Optimization of active blade pitching of a vertical axis wind turbine using analytical and CFD based metamodel
Bibliographic record
Abstract
This study optimizes the active blade pitching (ABP) motion of a vertical axis wind turbine (VAWT) to increase its power output over a wide range of tip speed ratios (TSRs). The study begins by developing a simplified analytical model to compute the torque output of the VAWT as a function of its ABP. The analytical model is used to generate a preliminary optimal ABP at a single TSR. Next, computational fluid dynamics simulations are employed to develop metamodels of the response of the VAWT. These metamodels are used to determine the final optimal ABP. The optimization procedure is then repeated for different TSRs to determine an optimal ABP as a function of the TSR. The power outputs for the ABP-driven VAWT are compared with those of a fixed blade VAWT at different TSRs. The results indicate a 13% increase in the maximum power output of the VAWT compared to an optimal fixed pitch strategy. The torque outputs of the ABP-driven VAWT are then compared with those of a fixed blade VAWT at low TSRs. The results indicate that ABP would significantly improve the self-starting capability of 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) | 0.000 | 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".