Finite element modeling and experimental validation of concentrator photovoltaic module based on surface Mount technology
Bibliographic record
Abstract
The development of renewable and clean energy such as concentrator photovoltaics (CPV) has been spurred by the scarcity of fossil fuels and their impact on global warming. However, CPV is expensive and complex to assemble, which has led to the creation of a new assembly method based on Surface Mount Technologies (SMT). In this study, we used Finite Element Model (FEM) to investigate and optimize thermal performance of such an assembly. We first fabricated and characterized a 4-solar cell CPV SMT module to enable comparison between experimental and FEM predicted temperatures. Following this validation, a parametric study was conducted. The model was extended to an infinite number of solar cells to guide the design of a large-scale SMT-based CPV module. The optimal dimensions were determined by identifying the module parameters that affect cell temperature, such as the area and thickness of the metal ribbon on the backside of the solar cell and metal coverage on the transparent glass Printed Circuit Board (PCB) on the frontside of the solar cell. Furthermore, the results of the parametric simulation have confirmed our previous findings, indicating that the module assembled using the simplified SMT method, with optimal dimensions of the metal ribbon, exhibits superior heat dissipation compared to the standard design based on wire bonding, due to the presence of metal on the glass printed circuit board. Further, this work demonstrates that by optimizing the SMT design with FEM, the temperature of the solar cells can be maintained below 80 °C.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.002 | 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".