Design of a Composite Encapsulation for Concentrated Photovoltaic Systems With Improved Performance
Bibliographic record
Abstract
Abstract Most of the currently used encapsulants are inefficient for cooled concentrated photovoltaic (CPV) systems. The encapsulant of cells for CPV systems, must have an optimum combination of thermal conductivity, coefficient of thermal expansion and long term shear modulus. In this work an improved backside composite encapsulation is designed and developed that can provide increased power output and longer life by enhancing the effectiveness of cooling and reducing thermal stresses. The best combination of material properties is identified through parametric studies on finite element model of CPV laminate using ethylene vinyl acetate as datum line. It is found that increasing thermal conductivity from 0.311 to 0.75 W/mK can improve the cooling and hence the power production by 2%. While long term shear modulus and coefficient of thermal expansion needs to be reduced for a longer service life. Using in-house built material design codes, optimum combinations of matrix and filler were identified that could provide the set range of properties. In line with material design code, a total of only four samples using thermoplastic polyurethane as matrix and Al2O3 or AlN as fillers were synthesized to validate the design experimentally. The material properties were measured and used in the parent finite element model to evaluate the performance of the experimentally developed material and to validate the parametric studies. A good agreement is found between the experimental and computational results and hence the overall methodology is found effective for application focused design and development of composite materials. It is expected that this material design and development approach will provide a useful guideline to the CPV manufacturing industries.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".