The Blade's Angle Affects Banki-Turbine Performance as an Alternative Design for Clean Energy Generation
Bibliographic record
Abstract
Hydropower is a renewable energy source with a lot of potential in Southeast Asian countries, with a total energy potential of 152,257 MW in Southeast Asia.The development of a hydro-turbine design is required due to the enormous hydropower potential.The turbine's runner is critical for converting fluid internal energy into kinetic energy.A cross-flow turbine that has gained popularity recently is the banki-turbine.The research that has been done is three-dimensional modeling of the banki-turbine with the CFD method.This study aims to determine the effect of the blade's angle on turbine performance.This research's steps are design, mesh independence, validation, simulation, and analysis.Modeling research was conducted with variations of blade angles 10˚, 15˚, and 20˚.Schematic modeling using a steady state condition, the turbulent type Shear Stress transport (SST), and the tetrahedral mesh method.The modeling consists of a rotating zone and a stationary zone.The water inlet velocity is 3 m/s, and the outlet pressure equals the room pressure (1 atm).Simulation of bankiturbine operated in 50 RPM until 350 RPM of angular velocity.One of the analyzes used is Factorial Design.The best performance Cpmax is obtained from the variation of the blade's angle of 15˚ on 0.28.
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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 | 0.001 | 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".