Optical simulation and fabrication of periodic triangular gratings for the enhancement of photovoltaic solar panels
Bibliographic record
Abstract
The solar energy industry strives to produce more and more efficient and yet cost effective photovoltaic (PV) panels. Integration of specific micro/nano optical structures on the top surface of the PV panels is one of the efficient ways to increase their PV performance through enhancing light trapping and in-coupling. In this study, periodic triangular gratings (PTGs) in polymethyl methacrylate (PMMA) were numerically simulated and optimized. The goal of this study is to enhance the ability of solar panels to convert maximum obtainable amount of solar energy by improving the optical in-coupling of light to PV material. Initial optical simulation results shown that a flat PV panel (without any enhancing micro-optical structures) exhibits an average incident light power of 0.327 W over a range of the incident light angles between 15º and 90º. Introduction of the PTG allows capturing the incoming sunlight and reflecting it back onto the PV material for a second or more chances for absorption and conversion into electricity. The light trapping and redirection is achieved through the total internal reflection (TIR) phenomenon. Geometry of the PTG was initially optimized with respect to an incident sunlight orientation of 15º, 30º, 45º, 60º, 75º, and 90º. Optical performance of the particular optimized PTGs was analyzed over daylight conditions and several optical parameters, such as average incident power and intensity, were calculated when sunlight orientation angle was changing from 15º to 90º. By adding the PTG optimized for 15º incidents light, an average incident power of 0.342 W was achieved (4.6% improvement of optical performance). Functional PTG prototypes were fabricated with optical surface quality (below 10 nm Ra). The simulation results allow understanding how the overall daytime photovoltaic performance of solar panels can be improved.
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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 | 0.001 | 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".