Forecasting Effect of Blade Numbers to Cross-Flow Hydro-Type Turbine with Runner Angle 30° Using CFD and FDA Approach
Bibliographic record
Abstract
Hydro energy installations in Indonesia are 6% of the total potential resource of 75 TW. Hydro energy in Indonesia still has an excellent opportunity to be developed to reduce the gap between potential and installation. Research on cross-flow type hydro-turbines is one form of effort to increase the availability of electrical energy from hydro-energy. This research has been carried out on a cross-flow type hydro-turbine using a threedimensional computational fluid dynamic (CFD) method. The research used CFX Solver on ANSYS. This research aims to determine the influence of the blade's number on the Power Coefficient of the turbine. Research has been carried out with variations in blades 12, 24, 36, and 48. The runner uses an angle of 30 and operates at a 50-300 rpm rotational speed. The velocity of the water used is 3 m/s, and the simulation is in a steady state. The simulation zone is divided into the rotational zone and the stationary zone. The type of turbulence used in this study is SST, and the mesh method is tetrahedral. The research results that have been done were analyzed using factorial design analysis (two factors). The 36-blade runner variation produced the best Cpmax. The resulting Cpmax is 27%. The factorial design analysis shows a significant influence between the rotational speed factor and the number of blades on turbine performance. In addition, the results show that there is no interaction between the rotational speed factor and the number of blades.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.000 |
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 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".