Design of a Lean Premixed Prevaporized Can Combustor
Bibliographic record
Abstract
Increasingly, more stringent emissions regulations have necessitated new gas turbine combustor designs with low pollutant emissions. Radically different modern designs have been developed to meet these requirements while maintaining high combustion efficiencies and good flame stability. While several published methodologies for conventional combustor design exist, none exist for modern ones. This paper describes the development of a new preliminary design algorithm for a modern lean premixed prevaporized (LPP) combustor. It also introduces a new LPP combustor concept. The approach used is multi-disciplinary in nature, applying empirical and semi-empirical models in the algorithm to capture complex processes such as droplet evaporation, chemical reaction, jet mixing, and heat transfer. The resulting set of procedures allows a designer to quickly define the detailed geometry of the combustor and provides an assessment of its performance. The preliminary design procedures were verified using the advanced numerical techniques of computational fluid dynamics (CFD). Reasonable agreement between predictions from the preliminary design and numerical analysis was achieved which indicated that the design procedures have been developed successfully.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".