A Physics-Based Performance Indicator for Gas Turbine Engines Under Variable Operating Conditions
Bibliographic record
Abstract
Gas turbines are often used under variable ambient conditions and power demands, which also may be off their design points. Such operating scenarios affect the typical performance parameters, such as thermal efficiency, mass flow and power. As a result, such parameters may fail to accurately indicate the structural degradation of a gas turbine. The objective of this study is to develop a robust physics-based performance indicator for a gas turbine to demonstrate the short term recoverable as well as long term non-recoverable degradation level of the engine, independent of the operating conditions. A comprehensive physics-based thermodynamic model for the gas path of a single shaft gas turbine is developed to accurately predict the cycle parameters based on limited actual operating data. Consequently, for the given ambient condition, demanded power and shaft speed, the model predicts the cycle parameters for the gas turbine in a healthy condition as the baseline. In reality, the measured parameters gradually deviate from the model, which reflects the performance deterioration of the engine caused by degradation mechanisms. In order to capture this performance deterioration, the ratio of the excess exhaust heat power with respect to the design point power, called Excess Heat Ratio (EH) is being proposed as an effective indicator. The effectiveness of the Excess Heat Ratio is examined by using 38-month operating data of an industrial gas turbine between two major overhauls. The trends of EH clearly shows its capability to capture the short term recoverable degradations and subsequent retrievals, arising from compressor fouling and subsequent wash. In addition, EH is also able to capture the trend of the long term non-recoverable degradations. The proposed indicator has the following advantages: 1) only limited data from the operating system of a gas turbine is required without the need of additional instrumentation; 2) the both short and long term degradations of the gas turbine can be quantified by a single indicator that is independent from the operating conditions; and 3) it is practically applicable for real-time monitoring and maintenance planning.
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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 | 0.001 |
| 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".