Industrial Trent Combustor—Combustion Noise Characteristics
Bibliographic record
Abstract
Thermoacoustic resonance is a difficult technical problem that is experienced by almost all lean-premixed combustors. The Industrial Trent combustor is a novel dry-low-emissions (DLE) combustor design, which incorporates three stages of lean premixed fuel injection in series. The three stages in series allow independent control of two stages—the third stage receives the balance of fuel to maintain the desired power level—at all power conditions. Thus, primary zone and secondary zone temperatures can be independently controlled. This paper examines how the flexibility offered by a 3-stage lean premixed combustion system permits the implementation of a successful combustion noise avoidance strategy at all power conditions and at all ambient conditions. This is because at a given engine condition (power level and day temperature) a characteristic “noise map” can be generated on the engine, independently of the engine running condition. The variable distribution of heat release along the length of the combustor provides an effective mechanism to control the amplitude of longitudinal resonance modes of the combustor. This approach has allowed the Industrial Trent combustion engineers to thoroughly “map out” all longitudinal combustor acoustic modes and design a fuel schedule that can navigate around regions of combustor thermoacoustic resonance. Noise mapping results are presented in detail, together with the development of noise prediction methods (frequency and amplitude) that have allowed the noise characteristics of the engine to be established over the entire operating envelope of the engine. [S0742-4795(00)00802-4]
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".