Comparison of Degradation of Two Different Gas Turbine Engines in Natural Gas Compressor Stations
Bibliographic record
Abstract
Gas Turbine (GT), like other prime movers, undergoes wear and tear over time which results in performance drop as far as available power and efficiency are concerned. In addition to routine wear and tear, the engine also undergoes corrosion, fouling etc. due to the impurities it breathes in. It is standard procedure to ‘wash’ the engine from time to time to revive it. However, it is important to establish a correct schedule for the wash to ensure optimal maintenance procedure. This calls for accurate prediction of the performance degradation of the engine over time. In this paper, an error-in-variables based methodology is applied to evaluate the performance degradation of two GT engines between soak washes. These engines are LM2500+ (single spool) and RB211-24G (twin spool). The engine-air-compressor isentropic efficiency and air inlet flow rate as well as the engine heat rate and specific work are analyzed for both engines. For both engines, the compressor isentropic efficiency is found to degrade over time, while the engine heat rate correspondingly increases. The compressor air inlet flow rate and engine specific work remain mostly constant. Through a comparison between the time-history of the engine health parameters, it is found that the LM2500+ degrades at a much faster rate than the RB211-24G. However, the degradation of the LM2500+ is found to be fully recoverable by offline washes, while the degradation of the RB211-24G is only slightly recovered by offline washes. The RB211-24G engine is found to be running near its maximum efficiency at all times, which is likely the cause for the observed non-recoverable degradation that the engine experiences. The engine’s site location is also found to contribute to the degradation that the engine experiences.
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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".