Modeling Radiation Heat Transfer With Participating Media in Solid Oxide Fuel Cells
Bibliographic record
Abstract
Solid oxide fuel cell (SOFC) technology has been shown to be viable, but its profitability has not yet been seen. To achieve a high net efficiency at a low net cost, a detailed understanding of the transport processes both inside and outside of the SOFC stack is required. Of particular significance is an accurate determination of the temperature distribution because material properties, chemical kinetics, and transport properties depend heavily on the temperature. Effective utilization of the heat can lead to a substantial increase in overall system efficiency and decrease in operating cost. Despite the extreme importance in accurately predicting temperature, the SOFC modeling community appears to be uncertain about the importance of incorporating radiation into their models. Although some models have included it, the majority of models ignore radiative heat transfer. SOFCs operate at temperatures around or above 1200 K, where radiation effects can be significant. In order to correctly predict the radiation heat transfer, participating gases must also be included. Water vapor and carbon dioxide can absorb, emit, and scatter radiation, and are present at the anode in high concentrations. This paper presents a simple thermal transport model for analyzing heat transfer and improving thermal management within planar SOFCs. The model was implemented using a commercial computational fluid dynamic code and includes conduction, convection, and radiation in a participating media. It is clear from this study that radiation must be considered when modeling solid oxide fuel cells. The effect of participating media radiation was shown to be minimal in this geometry, but it is likely to be more important in tubular geometries.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".