Thermodynamic Performance of a Gas Turbine Plant Combined With a Solid Oxide Fuel Cell
Bibliographic record
Abstract
This paper undertakes a thermodynamic analysis of a high-temperature solid oxide fuel cell, combined with a conventional recuperative gas turbine. In the analysis the balance equations for mass, energy and exergy for the system as a whole and its components are written, and both energy and exergy efficiencies are studied for comparison purposes. These results are also verified with data available in the literature for typical operating conditions, the predictive model of the system is validated. The energy efficiency of the integrated cycle is obtained to be as high as 60.55% at the optimum compression ratio. These model findings indicate the influence of different parameters on the performance of the cycle and irreversibilities therein, with respect to the exergy destruction rate and/or entropy generation rate. The results show that a higher ambient temperature would lead to lower energy and exergy efficiencies, and lower net specific power. Furthermore, the results indicate that increasing the turbine inlet temperature results in decreasing both the energy and exergy efficiencies of the cycle, whereas it improves the total specific power output. However, an increase in either the turbine inlet temperature or compression ratio leads to a higher rate of irreversibility within the plant. It is shown that the combustor and SOFC contribute predominantly to the total irreversibility of the system; about 60 percent of which takes place in these components at a typical operating condition, with 31.4% for the combustor and 27.9% for the SOFC.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".