Thermodynamics, Environmental and Sustainability Impacts of a Turbofan Engine Under Different Design Conditions Considering Variable Needs in the Aviation Industry
Bibliographic record
Abstract
Abstract In this study, thermodynamic analysis is implemented to the kerosene‐fuelled high by‐pass turbofan (HBP‐TF) engine to assess entropy, exergy, environmental, and sustainability metrics for different design variables such as pressure ratio of high‐pressure compressor (HPC‐PR) ranging from 7.5 to 8.5 and turbine inlet temperature (TIT) varying from 1400 to 1525 K considering variable needs in the aviation industry. As a novelty, entropic improvement potential (EIP) index for turbomachinery components and specific irreversibility production for the whole engine are calculated. Sustainability‐based parameters for different cases are compared with the baseline values of the HBP‐TF engine. The combustor has the highest entropy production of 44.4425 kW K−1 at the baseline. The higher TIT increases the entropy production of the combustor by 16.56%, whereas the higher HPC‐PR decreases it by 5.83%. The higher TIT and HPC‐PR favorably affect the sustainable efficiency factor of the engine, which is observed as 1.5482 at baseline and increases by 4.5% and 0.058% with the increment of TIT and HPC‐PR, respectively. The higher TIT and higher HPC‐PR results in lowering sustainability of the engine. The specific irreversibility production of the engine decreases by 3.78% and 0.1171% respectively, as TIT and HPC‐PR reach the highest point considered in the study.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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".