An Experimental and Numerical Study of the Laminar Flame Speed of Jet Fuel Surrogate Blends
Bibliographic record
Abstract
Kerosene-type fuels are the most common aviation fuel, and an understanding of their combustion properties is essential for achieving optimized gas turbine operation. Presently, however, there is lack of experimental flame speed data available by which to validate the chemical kinetic mechanisms necessary for effective computational studies. In this study, premixed jet fuel surrogate blends and commercial kerosene are studied using particle image velocimetry in a stagnation flame geometry. Numerical simulations of each experiment are obtained using the CHEMKIN-PRO software package and the JetSurF 2.0 mechanism. The neat hydrocarbon surrogates investigated include n-decane, methylcyclohexane, and toluene, which represent the alkane, cycloalkane, and aromatic components of conventional aviation fuel, respectively. Two blends are studied in this paper. The first is a binary blend formulated to reproduce the laminar flame speed of aviation fuel using a mixing rule based on the laminar flame speed and adiabatic flame temperature of the hydrocarbon components, weighted by their respective mixture mole fractions. The second blend is a tertiary blend formulated to emulate the hydrogen to carbon ratio of the kerosene studied. All of the considered fuels and blends are studied at three equivalence ratios, corresponding to lean, stoichiometric, and rich conditions, and at several stretch rates. The centreline axial velocity profiles from numerical simulations are directly compared to the measured velocity profiles to validate the mechanism at each condition. The difference between the experimental and simulated reference flame speed is used to infer the true unstretched laminar flame speed of the mixture. These results allow the effectiveness of the different blending methodologies to be assessed.
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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".