Energetic and Exergetic Analysis Hybrid Solid Oxide Fuel Cell Systems and Gas Turbine (SOFC-GT)
Bibliographic record
Abstract
Today, the issue of energy is very important for this purpose utilizing energy losses is very important.One of these ways is the use of heat dissipation in thermal systems.many attempts are doing to develop cogeneration systems taking into account cost, safety and environmental issues that gas turbine system of such a system can be noted.One important application of gas turbine cycle because of cycle high temperatures is to use them for hybrid cycle.For this purpose, the solid oxide fuel cell can be used that is very suitable for boiler input due to the high air temperature.In this regard, two different scenarios have been considered.In the first scenario, only the gas turbines cycle is used where the first law efficiency, second law efficiency and exergy destruction of the total cycle are 42.02 %, 50.28 % and 38960 KW, respectively.In this case, the most exergy destruction is related to combustion chamber.In the second scenario, fuel cell gas turbine cycle will be used, where the first law efficiency, second law efficiency and exergy destruction of the total cycle are 46 %, 56.84 % and 810 740 KW, respectively.In this case, the most exergy destruction is related to combustion chamber.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".