Effects of the Intake Air Humidity on the Gas Turbine Performance Monitoring
Bibliographic record
Abstract
Gas turbine engines (GTEs) are extensively used in locations with high humidity such as offshore platforms. However, in the dry regions, GTEs are often equipped with water spray inlet coolers for warm seasons. In both cases, the moisture affects the thermodynamic properties of the intake air and drifts the performance off the dry condition, especially during the warm days, when the moisture content of the air is high and the inlet air cooler is operational. In this paper, a detailed steady state model is proposed to simulate the GTE performance with the humid air, and it is linked with a thermodynamic model to quantify the total moisture content of the air after the cooler. The developed framework is used to analyze the operating data of a GTE during the three years of service. The results are then utilized for model-based performance monitoring of the GTE, using a recently introduced performance indicator. A comparative analysis is performed between the results received from the primary model overlooking the humidity effects, and the developed enhanced performance model with humidity effects. A better accuracy for the performance indicator was observed where the enhanced model is employed, suggesting the importance of considering the intake air humidity for model-based performance monitoring.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".