Energy and Exergy Analysis of a Simple Gas Turbine Cycle with Wet Compression
Bibliographic record
Abstract
A thermodynamic model of the wet compressor in a simple gas turbine cycle was investigated in this paper. A suitable quantity of water was injected into the compressor-stages where it evaporated. Subsequently, the steam and air were heated in the combustion chamber and expanded in the turbine. The wet compressor (WC) has become a reliable way to reduce gas emissions and increase gas turbine efficiency. In this study, the operational data of the simple gas turbine and the maximum amount of water that can be injected into the compressor were assessed, as well as a comparison between the dry compression, the wet compression and the isentropic compression. The performance variation due to water spray in the compressor and the effect of varying ambient temperature on the performance of gas turbine (thermal efficiency, power) was investigated, and the results are compared to the results of the same cycle with a dry compressor. The analytical formula of exergy destruction and results show that exergy destruction increases with water injection. The programming of the performance model for the gas turbine was developed utilizing the software IPSEpro. The results of the gas turbine with a wet compressor demonstrates a 12% reduction in the compressor exit temperature up to isentropic temperature. The compressor work decreased by 11% when using a wet compressor, this lead to an improvement in power output and efficiency However, the wet compressor increases the specific fuel consumption and heat rate of the gas turbine. There are limitations in the amount of steam that can be injected, 0.4 kg/s of water was the optimum amount injected into the compressor.
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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 | 0.001 |
| 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) | 0.002 | 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".