Cavitation Hydrodynamic Performance of 3-D Printed Highly Skewed Stainless Steel Tidal Turbine Rotors
Bibliographic record
Abstract
Hydraulic turbines contribute to 60% of renewable energy in the world; however, they also entail some adverse effects on the aquatic ecology system. One such effect is their excessive noise and vibration. To minimize this effect, one of the most effective and feasible solutions is to modify the design of the turbine rotor blade by introducing a skew. In this study, two 0.3-meter tidal turbines with 0-degree (no-skewness) and positive 90-degree skewness made of stainless steel 316L were designed and printed using a 3-D printing powder bed fusion technique. These rotors were then tested at the Emerson Cavitation Tunnel (ECT) at Newcastle University, UK, and the variation in the skewness of the blades of the turbines as a function of the power coefficient on a given tip speed ratio (TSR) value was ascertained. Results showed that the highly skewed rotor had significantly lower drag and torque fluctuations, with a slight decrease in efficiency compared to the non-skewed one, which warrants further investigation on the effect of added skew to reduce vibration and noise. Numerical simulations were also performed for verification and validation of the experimental tests, using the H45 dynamometer at the ECT. A comprehensive software code for propellers and tidal turbines, ROTORYSICS, was used to examine the cavitation effect of the two rotors; a comparison was made for both, with and without cavitation. The results indicate that for a high immersion depth of tidal turbine rotors, cavitation rarely occurs, but for hydrokinetic turbines that are installed on dams in rivers and falls, cavitation could be a serious concern. It was concluded that the 0-degree skewed rotor is more hydrodynamically efficient than the 90-degree skewed rotor.
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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) | 0.001 | 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".