CFD Simulation of Savonius Cross-flow Hydrokinetic Turbine Using Various URANS Turbulence Models
Bibliographic record
Abstract
The utilization of hydrokinetic turbine technology is an innovative and sustainable method of generating electricity through the power of flowing water. The distinct advantage of this technology over traditional hydropower plants is its ability to operate without the need for the construction of dams or large water reservoirs, which can pose significant risks to local ecosystems and communities. Instead, hydrokinetic turbines can be directly installed in waterways, allowing for a more efficient and eco-friendly use of natural resources. This technology can provide clean, reliable electricity to millions of people globally, while also reducing greenhouse gas emissions and mitigating the effects of climate change. The present study presents two-dimensional computational fluid dynamics simulation of a cross-flow hydrokinetic turbine model. The simulation is performed by various Unsteady Reynolds-Averaged-NavierStokes models and then the results are compared to previous experimental and mathematical models. The flow field patterns and performance parameters of different models are presented and compared to compare the validity of different turbulence models available. © 2024, World Congress on Civil, Structural, and Environmental Engineering. All rights reserved.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".