Forecasting Effect of Blade Numbers to Cross-Flow Hydro-Type Turbine with Runner Angle 30° Using CFD and FDA Approach
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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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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)
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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 it