Concentrating optical system optimization for 3- and 4-junction solar cells: impact of illumination profiles
Why this work is in the frame
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Bibliographic record
Abstract
Optical component designs for concentrating photovoltaic systems with three different multijunction solar cells (MJSCs) are optimized to yield maximum system efficiencies under standard test conditions, specifically uniform illumination. Optimization uses an integrated optoelectrical approach with ray tracing of the optical train to generate an irradiance profile for input to the cell’s distributed circuit model. These cells, a three-junction lattice-matched (3JLM) solar cell, a three-junction lattice-mismatched inverted metamorphic (3JIMM) solar cell, and a four-junction lattice-matched (4JLM) solar cell, were individually designed for maximum efficiency at 1000×. The optical train introduces losses, modifies the spectrum, and produces a spatially nonuniform profile across the cell. We decouple spectral modification from spatial nonuniformity to separately determine their individual impacts on system efficiencies, finding the optimal set of optical design parameters for each case. Spectral modification yields modest loss penalties (from 1.0% to 1.6%, relative to the MJSC), but the impact of nonuniformity is more significant and cell dependent, with relative loss penalties of 1.1%, 3.8%, and 2.3%, for 3JLM, 3JIMM, and 4JLM, respectively. While spectral modification does not significantly impact design parameters, spatial nonuniformity does, with absolute losses of 1% and 3.4% if 3JIMM and 4JLM cells are used in a 3JLM optimized system, respectively.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it