The Effect of Additional Air Deflector at Air Concentrator on Photovoltaic Performance
Bibliographic record
Abstract
The supply of fossil energy is decreasing along with the development of civilization and technological advances. Solar energy is one of the renewable energies that can reduce these problems. Solar cells can convert solar energy into electrical energy using the principle of the photovoltaic effect. The performance of photovoltaic panels is affected by the increase in temperature in the photovoltaic panels. This study aims to identify the effect of increasing the number of deflectors on the air concentrator which is implemented as an active cooling of solar cells. This research was carried out experimentally by integrating an air concentrator, deflector, and heatsink on a PV panel. The application of various without concentrators, concentrators without deflectors, concentrators with two deflectors, concentrators with four deflectors, and concentrators with six deflectors, the temperature values are 56.70℃, 54.66℃, 53.30℃, 51.63℃ and 53.70℃. From several variations of the addition of deflectors that have been carried out, it is found that through the application of a concentrator using four deflectors can obtain the most optimal results by producing a maximum power of 24.50 W with a total efficiency of 6.35%. In this configuration, a temperature drop of 5.07℃ is obtained.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".