Effects of heat transfer on characteristics of thermionic energy converter
Bibliographic record
Abstract
Photon enhanced thermionic emission (PETE) is a new concept in solar energy conversion, combining thermal and photovoltaic carrier excitations with thermionic emission. A solar-power-driven thermionic energy converter operates by illuminating the solar light condensed by a large-scale Fresnel lens to convert heat energy into electrical energy. By enhancing the efficiency of converting solar radiation into the emitter internal energy, the output power and efficiency of the thermionic energy converter can be greatly improved. In this study, using numerical simulations, the effects of emitter temperature and output characteristics on a thermionic energy converter were investigated. The results showed that the higher rate of the heating power represented the higher temperature of an emitter, as well as output current density, and efficiency. In addition, by reducing the diameter of a collector and thermal conductivity of insulation materials, or increasing the diameter of emitter, the temperature of emitter, output current density, and efficiency could be notably improved. It is also worth mentioning that the main factor that affected the emitter temperature in the process of heat transfer was heat conduction between solids. In conclusion, adequate illumination, reasonable size of collector and emitter, as well as appropriate insulation measurements could efficiently improve the output characteristics of thermionic energy converter.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".