Probabilistic assessment of exergy analysis of a wind turbine for optimum performance
Bibliographic record
Abstract
The performance of a wind turbine depends on its first law efficiency and second law efficiency. This study focuses on the second law of efficiency, that is, the exergy efficiency of a wind turbine. This study introduces a novel technique for determining the optimum performance conditions of a wind turbine. Jhimpir City, Pakistan, was selected for the case study. The wind speed distribution in the selected area was analyzed using different probability density functions. The three-parameter Weibull distribution is the best probability density function for fitting wind speed variation. The probability distribution of the total wind exergy is performed, and a one-year variation of the wind exergy is plotted, showing the maximum exergy around the middle of the year. The exergy efficiency of the turbine using a power curve and wind exergy was determined at different wind speeds. The probabilities of the various exergy efficiencies were also determined. The results show that a high exergy efficiency has a high probability but so does low exergy efficiency owing to seasonal variations. The proposed method can be extended to any wind farm to determine the geographical and meteorological parameters of the site.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".