Relations between the number of spray droplets and chemiluminescence for Jet A-1 flames stabilized in a gas turbine model combustor
Bibliographic record
Abstract
The spatial and temporal relations between the flame chemiluminescence and the number of droplets for spray flames stabilized in a gas turbine model combustor are investigated experimentally. Pressure measurements, Interferometric Laser Imaging for Droplet Sizing, shadowgraphy , as well as flame chemiluminescence and Mie scattering imaging are performed. Test conditions corresponding to premixed methane and air, Jet A-1 spray, and Jet A-1 spray in premixed methane and air flames are examined. For all test conditions, the fuel-air equivalence ratio is 0.6, and the fuels and air flow rates are adjusted to generate a fixed nominal power of 10 kW. It is shown that, compared to the Jet A-1 spray flames, the dual-fuel flames feature smaller plenum pressure fluctuations. The droplet sizing suggests that, compared to Jet A-1, the dual-fuel spray is poorly atomized, leading to the generation of large droplets. Spectral analysis shows that, although the tested flames are not thermoacoustically excited, an injector-induced instability exists. The spatial relations between the flame chemiluminescence and the spray number density shows the coupling between these parameters is driven by the spray dynamics for the Jet A-1 flames; however, this coupling is driven by methane combustion for the dual-fuel test condition. A time-lag-based linear-regression model is developed and used to investigate the temporal relations between the flame chemiluminescence and the spray number of droplets for Jet A-1 spray flames. It is obtained that the oscillations of the number of spray droplets lead those of the flame chemiluminescence. The findings suggest that, in addition to the several spray characteristics reported in the literature, the droplets number density plays an important role in elaborating the coupling between the spray and flame chemiluminescence.
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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 | 0.000 | 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".