A Discrete Vortex Method for Simulating a Stand-Alone Tidal-Current Turbine: Modeling and Validation
Bibliographic record
Abstract
This paper advanced our recent effort (Li and Çalışal, 2007, “Preliminary Result of a Discrete Vortex Method for Individual Marine Current Turbine,” The 26th ASME International Conference on Offshore Mechanics and Arctic Engineering, Jun. 10–15, San Diego, CA) to study the behavior of tidal-current turbines. We propose a discrete-vortex method with free-wake structure (DVM-UBC) to describe the behavior of a stand-alone tidal-current turbine and its surrounding unsteady flow and develop a numerical model to predict the performance and wake structure of the turbine based on DVM-UBC. To validate this method, we conducted a series of towing tank tests. DVM-UBC is then validated with several kinematic and dynamic results. When we compared the results obtained with DVM-UBC with our towing tank test results, published results, and the results obtained with other numerical methods, we achieved good agreements. Our comparisons also suggested that DVM-UBC can predict the performance of a turbine 50% more accurately than the traditional discrete-vortex method (traditional DVM) with comparable computational effort and will produce results comparable to the Reynolds averaged Navier–Stokes equation with much less computational effort.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".