A Methodology and Case Study of Outboard Traverse Flame Detection on Aeroderivative Gas Turbines
Bibliographic record
Abstract
Abstract Outboard-traverse flame migration in an annular profile, combustion gas pathway of a Siemens aero-derivative turbine engine can be detected with a dual immersion thermocouple. This solution is applicable for Oil & Gas operators using gas turbines fueled by natural gas. The typical flame profile within the annular combustion gas flow path is disturbed by introducing poor quality fuel into the engine. The skewed or outward bias flame profile in turn causes severe overheating of the hot section components around the outer radius of the annular combustor exit wall covering a number of hot section components. This ultimately causes accelerated components deterioration and failure to meet its target design life. Consequently, resulting in rejection of these components and increasing the life cycle cost of their asset operations. The introduction of the dual immersion thermocouple allow us to detect outward bias flame pattern using the exhaust gas temperature profile during operation and warn the operator of this condition via software alarm and trip provision. By implementing a means of detecting outward bias flame patterns, the operator will be made aware of this condition and can then take means to resolve this matter by first, allowing to optimize hot section components boundary limits and saving overhaul costs and second, avoid unplanned maintenance outages due to hot section premature failures.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".