Thermal optimization of a solar cell carrier for concentrator systems
Bibliographic record
Abstract
Solar cell efficiency decreases as its temperature increases. Therefore, it is necessary to design a thermally optimal solar cell carrier that will maintain a minimal solar cell temperature. To achieve this optimal solar cell carrier design, a finite-element analysis model of the solar cell on carrier was developed. This numerical model was experimentally calibrated against a known design, in which the average solar cell temperature was determined by examining the shift in the open circuit voltage. This allowed us to explore the relationship between the carrier geometry and the average solar cell temperature. That is, the solar cell carrier is characterized by two independent thermal resistances: the uniform flow thermal resistance, and the thermal spreading resistance. As the copper thickness was increased, the uniform flow resistance acted to raise the cell temperature while the spreading thermal resistance decreased the cell temperature. Therefore, when the carrier geometry minimized the thermal resistances, it was found that the minimum solar cell temperature was achieved at a copper thickness between 1.5 and 3 mm depending on the surface area of the carrier. This optimized carrier design reduced the average solar cell temperature by 16 °C, which corresponds to an increase of 0.8% in cell efficiency at 1666 suns as compared to the original design used to experimentally calibrate the numerical model.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".