Experimental Study of the Blade Geometry Effect of Two-Stage Gravitational Water Vortex Turbine
Bibliographic record
Abstract
This study investigates blades of varying geometries in the context of a two-stage Gravitational Water Vortex Turbine (GVWT).The objective was to identify the optimal blade shape and radius yielding the best rotational speed, mechanical power, and efficiency for a two-stage vortex turbine.Specifically, the study examined parameters such as the Savonius shape and curvature, utilizing different blade ratios on two separate shafts.The turbines were configured with a telescopic system positioned at a distance of 10 cm apart.Each variation was subjected to loads ranging from 0.5 kg to 2 kg.Various performance metrics-rotational speed, torque, and water height-were assessed following load adjustments.Turbine Stage 1, employing the Savonius blade, achieved an optimal mechanical power output of 12.4 W, while Turbine Stage 2, utilizing a curved blade, reached a maximum mechanical power of 11.1 W. The Savonius blade demonstrated higher torque, operating more efficiently under greater loads.Notably, the water vortex with a larger air core experienced distortion caused by the turbine, leading to unstable flow.In contrast, implementing curved blades with a ratio of 0.5 provided the water vortex ample space to flow, resulting in a more stable vortex formation.Thus, carefully considering the optimal contact area and blade geometry is essential to minimize water vortex distortion in each turbine.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".