Parametric Study to Optimize Air/Fuel Mixing for Lean, Premix Combustion Systems
Bibliographic record
Abstract
New designs of gas turbine combustors for power generation applications have to meet ever-tightening emission standards (mainly NOx, CO and UHC) while operating at high combustor pressures. This requires a detailed understanding of the physical processes involved. The air-fuel mixture preparation is a critical step in most advanced gas turbine combustion strategies to achieve lower emissions. It has long been established that the level of unmixedness between the fuel and air is strongly tied with NOx levels. The present paper applies the statistical technique of Design Of Experiments (DOE) to a generic mixer set-up that includes an axial swirler, with fuel injected at discrete locations and transverse to the flow. The objective is to identify influential design and operating parameters that will provide rapid and enhanced mixing. The parameters tested include Swirl strength as measured by the Swirl number, Swirl type (Constant angle vs. Free vortex), number and momentum of fuel injection sites and gas temperature. Planar Laser Induced Fluorescence of acetone (PLIF) was used to quantify mixing at various planar locations in the mixing section. Commercial CFD software is used to model the flow field and predict the spatial mixing at selected conditions. Comparisons are made with experimental measurements with the aim to validate the CFD code and also on comparing the model results with the measurements.
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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 | 0.001 | 0.002 |
| 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) | 0.001 | 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".