Modeling tidal turbine farm with vertical axis tidal current turbines
Bibliographic record
Abstract
A tidal current turbine is a device for harnessing energy from marine currents, and functions in a manner similar to a wind turbine. A group of tidal current turbines distributed in a site in ocean is called a tidal turbine farm. Among all tidal current energy extraction schemes, turbine farm is regarded as among the most promising turbine configurations; in fact, of turbine farms are extensively employed in the wind power industry. Wind farm planning and modeling approaches cannot be fully transferred to tidal farms, however, because of the complexities involved in modeling the underwater tidal turbine. This study develops a framework for planning a tidal turbine farm system with vertical axis tidal current turbines. This framework is intended to be used by energy planners in the early design stage. An approach for selecting the optimal design among alternative tidal turbine farm designs is proposed whereby the attractiveness of the alternatives is evaluated based on cost effectiveness. Where possible, experience gained from analysis of existing offshore wind farms is applied. The state of the art of tidal turbine design and wind farm planning are reviewed, and a planning framework for selecting the optimal tidal farm design is provided by identifying the important mathematical modeling procedures and elements. Considering the particular design of the vertical axis tidal turbine, a simplified relationship of turbine distribution and turbine farm efficiency is developed. As a case study, numerical simulation results are presented for environmental conditions offshore of British Columbia, Canada.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".