Optimizing thermophotovoltaic (TPV) systems through photon fate analysis: An experimental case study based on ellipsoidal optical cavities
Bibliographic record
Abstract
This study presents a comprehensive photon fate framework for thermophotovoltaic (TPV) systems, integrating both numerical and experimental approaches to optimize performance through photon recycling, for different photovoltaic (PV) cells (Si, GaSb, and InGaAsSb). A quantitative analysis of optimal recycling factors reveals their strong dependence on power levels, view factor losses, and PV cell type. Results show lower emitter input power necessitates higher recycling factors for optimal performance. In an InGaAsSb-based TPV system with an emitter input power of 10 W/cm 2 , the maximum electrical output power is P elc = 2.4 W/cm 2 (for view factor loss F VF-loss = 1 %), 1.63 W/cm 2 ( F VF-loss = 5 %), and 0.87 W/cm 2 ( F VF-loss = 20 %), corresponding to optimal photon recycling factors of F rec = 92 %, 79 %, and 44 %, respectively. Experimental validation is achieved using a TPV system with a novel ellipsoidal optical cavity configuration featuring silver-coated annular rings with tunable width-to-diameter ratios ( ω/a = 0, 0.2, 0.4, 0.6, 0.8) to precisely control photon recycling factors from F rec = 0 to F rec = 0.735. The impact of temperature-dependent recycling effectiveness is analyzed, demonstrating enhancement factors up to ∼210 times at low-temperature operation and ∼80 times at higher temperatures. These results provide insights into operating temperature strategies and establish a practical design framework for optimizing TPV systems. Furthermore, the findings serve as a valuable tool for system-level trade-offs, offering guidance for maximizing efficiency based on system constraints. This work lays the foundation for next-generation TPV system designs, advancing the integration of photon management strategies for high-performance energy conversion. • Photon fate analysis used to maximize TPV system performance. • Emitter temperature increases of 300 K achieved in experiments. • Photon recycling increases TPV system output power by orders of magnitude.
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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 | 0.001 | 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 | 0.000 |
| 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".