Using the petiole of the miriti palm for the core of a small wind turbine blade
Bibliographic record
Abstract
In many small wind turbine blades, the interior space between laminate skins is filled by a material core. The mechanical properties of the core are much less important than its density, which must be low to reduce the moment of inertia as high inertia increases both the starting time of the turbine and the gyroscopic loads on the blades. In this paper, we use, for the first time, the petiole of the miriti palm (PMP) as the core of four small blades, in order to analyze its effect on turbine starting performance. PMP is abundant in the Amazon region and harvesting it does not destroy the palm because the petiole regrows; therefore, harvesting is fully sustainable and may well have a major role in increasing the sustainability on wind turbine manufacturing. We consider the benefits of using the easily worked petiole for the core in terms of manufacturing, as demonstrated by the construction of a 0.598 m blade. PMP is less dense on average than alternative materials, such as expanded polystyrene and balsa wood. The starting performance is an important issue for small wind turbines. It is evaluated using a quasi-steady model, in which blade element momentum theory is coupled to Newton's Second Law. The low density of the small blade made using petiole of the miriti reduces the starting time by 10% when compared with expanded polystyrene and 42% when compared to balsa wood.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".