Energy and exergy analyses of compressor inlet air-cooled gas turbines using the Joule—Brayton refrigeration cycle
Bibliographic record
Abstract
In this article, a novel method of inlet air cooling is proposed to enhance the performance of a gas turbine operating in hot climates. The intake air at the compressor bell-mouth is cooled by an air Brayton refrigerator driven by the gas turbine, and the refrigerator uses air as the working fluid. Introducing the air refrigeration cycle provides the advantage of quite low temperatures close to 0 °C and even lower. This is not possible with other methods of intake air cooling, namely evaporative cooling or use of waste heat-driven absorption machines. A thermodynamic analysis through energy and exergy is employed, and a comprehensive parametric study is performed to investigate the effects of extraction pressure ratio, extracted mass rate, turbine inlet temperature (TIT), and ambient relative humidity (RH) on increase in net work output, first law efficiency, and second law efficiency of a compressor inlet air-cooled gas turbine cycle using a Joule—Brayton refrigerator. The analysis of the results indicates that the maximum exergy destroyed in the combustion chamber, which represents>80 per cent of the total exergy destruction in the overall system, is significantly affected by the extraction pressure ratio and TIT and is slightly affected by ambient RH. The increase in the net work output, the first law efficiency, and the second law efficiency of the cycle significantly varies with a change in the extraction pressure ratio, extraction mass rate, TIT, and ambient RH. Results clearly show that performance evaluation based on first law analysis alone is not adequate, and hence more meaningful evaluation must include second law analysis. Decision-makers should find the methodology presented in this article useful in comparison and selection of various methods for inlet air cooling in gas turbines.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".