Combustion characteristics of sustainable aviation fuels in a scaled-down afterburner test rig
Bibliographic record
Abstract
Abstract This study investigates the combustion characteristics (lean blowoff (LBO), flashback, and combustion instability) of sustainable aviation fuels (SAFs) using a two-staged combustor arranged in tandem. The combustor consists of a first (main) burner with an axial swirler and a second burner (afterburner) with a V-gutter bluff-body. The main burner utilises a constant flow rate of CH4/Air as reactants, while the afterburner operates on the combustion product from the first burner, additional dilution air, and one of the six liquid test fuels (Jet-A, A-2, C-1, C-5, C-9, and JP-8/HRJ) as reactants. When test fuels with higher derived cetane number (DCN) are used in the afterburner, it is observed that LBO occurs at lower afterburner global equivalence ratios ( ${\phi _{af}}$ ). This is due to the smaller chemical time scale of those test fuels, which enables the flame to be sustained under higher flow speeds. This is consistent with the results of previous studies. Additionally, this study shows that flashback also occurs at lower equivalence ratio for test fuels with higher DCN. This is a new finding and can be explained in a similar way to LBO. As the operating conditions approach the LBO or exceed flashback limits, the reactivity of shear layer also decreases, creating favourable conditions for them to occur. Moreover, for test fuels with low DCN (C-1 and C-5), blowoff occurs instead of flashback as ${\phi _{af}}$ increases. This can be attributed to the stronger pressure perturbation resulting from combustion instability in these flames compared to others. It demonstrates a stronger correlation between the pressure perturbation and the heat release rate fluctuation for C-1 and C-5 than that of other test fuels at the same equivalence ratio. In conclusion, it was found that test fuels with higher DCN have advantages in terms of LBO and stability, however, they are more prone to flashback.
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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 | 0.000 | 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) | 0.001 | 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".