Bibliographic record
Abstract
This paper concerns the blades of multi-bladed water-pumping windmills when they have variable mass and centre of mass. The paper explores blade arrangement strategies that will minimize the eccentricity of the rotor centre of mass and hence any rotor-induced vibration. The number of blades in the rotor is assumed to equal the number made in each production batch, in contrast to the case where a batch of up to 22 blades was optimally matched to produce two- and three-bladed rotors, Hitz & Wood [1]. Using the measured mass and centre of mass of 24 blades for the rotor of a 26 ft Kijito windmill described by Harries [3], three strategies are considered. Random matching of the blades is shown to become increasingly effective as blade number increases. Pairing the blades by ordering in the product of mass and centre of mass, d, followed by random selection of pairs also produces rotors with low eccentricities. The numerical experiments show that the best strategy involving random selection is to pair by ordering, swapping the blades of every second pair, and then randomly arrange the resulting pairs. Finally, a heuristic based on blade pairing is shown to give eccentricities which are high compared to the minimum value determined exactly for 12 blades or less, but apparently low enough to be useful.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".