Determinations and performance investigations of hybrid composite properties for hydrokinetic turbine blades
Bibliographic record
Abstract
In this experimental study, hybrid composite properties for hydrokinetic turbine blades were determined, and its performance was examined. The reinforcements included glass fiber, treated and untreated highland bamboo fiber, reinforced polyester-based composites and their hybrid composites, as well as row bamboo (bamboo culm). The row bamboo was prepared on the bamboo culm’s outer surface to get rid of any leftovers, before cutting in accordance with the specimen test standards. In order to make hybrid composite materials, polyester resin was mixed with fibers in the proportions of bamboo fiber/glass fiber (50%/50%), and fiber/matrix (30%/70%). The composite containing glass fiber has a flexural strength that is 12% and 21% higher than materials reinforced with alkali-treated and untreated highland bamboo fiber reinforced composites respectively. The alkali treatment of highland bamboo fiber improved its physical-mechanical properties, making it suitable for use different application. Alkaline treatment boosts the composite’s tensile and compressive strength by 37% and 3.4% for composite reinforced with untreated bamboo fiber, and 10.2% and 23.8% for composite reinforced with glass fiber, respectively. The fiber density of highland bamboo was increased by removing less dense non-cellulosic components (hemicellulose and lignin). However, the fiber’s moisture absorption is the main issue in using it in a composite that works submerged in water. This study investigated whether incorporating glass fiber in highland bamboo-glass fiber polyester hybrid composite reduces the composite material’s water uptake. A reduction was found, however, the density was increased. It is challenging to employ row bamboo for the construction of hydrokinetic turbine blades as water ingress of the composite must be avoided even in the presence of erosion caused by cavitation and impact with foreign bodies in the water.
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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 | 0.000 | 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) | 0.000 | 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 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".