Taguchi-based multi-factor analysis of self-starting behavior in vertical axis wind turbine farms
Bibliographic record
Abstract
A key aspect of system sustainability is the efficient use of energy. In this context, vertical axis wind turbines (VAWTs) require critical evaluation due to their relatively low electricity generation and poor self-starting performance. Accordingly, the present study extensively assesses the start-up performance of a VAWT farm comprising three H-Darrieus wind turbines (WT-1, WT-2 and WT-3), using Computational Fluid Dynamics (CFD) in combination with the Taguchi method. Initially, the self-starting behavior of individual turbines with solidity values of 0.50, 0.75, and 1.00 was examined. Subsequently, a two-stage Taguchi analysis was conducted to investigate the influence of turbine and farm design parameters, specifically, the solidity of the turbines and their spatial arrangement. In the first stage, four factors at three levels were considered. The results at this stage indicated that minimizing the spacing between turbines improves self-starting performance. Following this, a second-stage Taguchi analysis was performed, incorporating a larger number of parameters to provide a more detailed evaluation of start-up performance in a wind farm configuration. This stage included six factors, each evaluated at three levels. The results demonstrated that the overall self-starting performance was enhanced by employing wind turbines with a solidity of σ = 1 . 00 . In addition, turbine spacing of 1 . 1 D was suggested as optimal for achieving improved start-up capability. Furthermore, to gain additional insight into the start-up behavior in a four-turbine arrangement, a fourth turbine (WT-4) was added to the optimal three-turbine configuration (P1). The results indicated that the inclusion of WT-4 improved the self-starting performance of WT-3.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".