Selection of Wind Turbine Blade Material Using Intuitionistic Fuzzy TOPSIS Method
Bibliographic record
Abstract
Utilizing turbines for wind energy is crucial for sustainable power. The effectiveness of turbines heavily relies on the blades, making material selection vital for their design. This study employs multi-criteria decision making (MCDM), particularly intuitionistic fuzzy (IF) in the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method, to facilitate material selection. The Intuitionistic Fuzzy Weighted Averaging (IFWA) operation aggregates the ideas of the decision-makers (D-Ms), prioritizing criteria and alternatives. The combined method, blending the Intuitionistic Fuzzy Set (IFS) and TOPSIS, proves highly efficient in determining the most suitable material among alternatives based on established criteria. The findings highlight carbon fiber as the premier choice, suggesting its adoption for enhanced wind turbine blades.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.012 | 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 teacher head, 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".