Study the Effect of Blade Angle on Hydrokinetic Turbine Performance
Bibliographic record
Abstract
Harnessing renewable energy from water currents, such as rivers and tidal streams, without extensive infrastructure, positions hydrokinetic turbines as a highly promising technology.This research details the design and optimization of hydrokinetic turbine blade profiles to significantly improve their efficiency and overall performance.A comprehensive analysis, utilizing Computational Fluid Dynamics (CFD) simulations, was conducted to investigate the influence of varying angles on blade hydrodynamic performance.Our evaluation encompassed diverse blade profiles, seeking to optimize efficiency under various flow conditions, with dual objectives of maximizing power output and mitigating cavitation and structural stress.The findings conclusively demonstrate that the optimal selection of the blade angle can substantially enhance turbine efficiency, thus bolstering its potential for large-scale energy production.Furthermore, a specific angle of 67.5 degrees exhibited an unexpectedly superior power output compared to angles of 15 and 45 degrees.This work advances hydrokinetic technology and provides a robust framework for the continued optimization of renewable energy systems.
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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 | 0.001 |
| 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) | 0.002 | 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".