Flow Pattern Investigation of Savonius Cross-flow Hydrokinetic Turbine Using CFD URANS Turbulence Models
Bibliographic record
Abstract
The utilization of hydrokinetic turbine technology represents an innovative and sustainable approach for generating electricity by harnessing the power of flowing water.Unlike traditional hydropower plants, hydrokinetic turbines offer several advantages as they do not require the construction of dams or large water reservoirs.This characteristic eliminates potential risks to local ecosystems and communities.By directly installing these turbines in waterways, the utilization of natural resources becomes more efficient and environmentally friendly.Consequently, this technology has the potential to provide clean and reliable electricity globally while also mitigating greenhouse gas emissions and addressing climate change.This research project focuses on conducting a comprehensive investigation through two-dimensional computational fluid dynamics simulations of a cross-flow hydrokinetic turbine model.By utilizing various Unsteady Reynolds-Averaged-Navier-Stokes models, the obtained results will be compared with previous experimental and CFD models.The flow field patterns will be analysed and compared to effectively illustrate the discrepancies and similarities among different turbulence models used.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| 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".