Selection of Material for Wind Turbine Blade by Analytic Hierarchy Process (AHP) Method
Bibliographic record
Abstract
Wind energy and solar energy are prominent renewable energy options in the view of growing energy demand. Reliable small wind power produced at compatible price is the need of hour. Cost of the energy generation depends on the cost of the materials used, operating and maintenance cost, cost of the fuel. The material cost directly relates to the cost of the energy. Blade design plays significant role in any wind turbine design. In order to have long expected life of blade material selection is a crucial stage in blade design. Wood, Glass fiber, carbon fiber, natural fiber, sandwich composite materials are different material available for small wind turbine blades. Strength, durability, density, cost, and availability are the important properties to be considered during material selection of blade. The selection of material for wind turbine blade is an important stage in blade design. This paper presents a simple Analytic Hierarchy Process for material selection for the small wind turbine blade. AHP is one of the simplest and cost effective decision making method. In this work AHP is successfully applied for material selection for small wind turbine blade.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".