A Numerical Methodology to Predict the Maximum Power Output of Tidal Stream Arrays
Bibliographic record
Abstract
Due to its high level of consistency and predictability, tidal stream energy is a feasible and promising type of renewable energy for future development and investment. Numerical modeling of tidal farms is a challenging task. Many studies have shown the applicability of the Blade Element Momentum (BEM) method for modeling the interaction of turbines in tidal arrays. Apart from its well-known capabilities, there is a scarcity of research using BEM to model tidal stream energy farms. Therefore, the main aim of this numerical study is to simulate a full-scale array in a real geographical position. A fundamental linear relationship to estimate the power capture of full-scale turbines using available kinetic energy flux is being explored. For this purpose, a real site for developing a tidal farm on the southern coasts of Iran is selected. Then, a numerical methodology is validated and calibrated for the established farm by analyzing an array of turbines. A linear equation is proposed to calculate the tidal power of marine hydrokinetic turbines. The results indicate that the difference between the predicted value and the actual power does not exceed 6%.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".