Flame propagation of jet A-1 fuel and its surrogates
Bibliographic record
Abstract
The laminar flame speed is an essential flame parameter for both chemical kinetic mechanism validation and turbulent combustion studies. Kerosene-derived jet fuel flame speeds, however, are largely unknown and due to complex composition of the fuels themselves they cannot be modelled numerically. To overcome this limitation surrogate fuels and blends are used to reproduce the same flame speed of amore complex mixture. To accomplish this for aviation fuel, a database is created of four potential jet fuel surrogate components for laminar flame speed. The neat hydrocarbon surrogates investigated are n-dodecane and n-decane, methylcyclohex-ane, and toluene, which represent the alkane, cycloalkane, and aromatic components of conventional aviation fuel, respectively. Several blends of these surrogate fuels are tested experimentally and numerically to validate the effect of blend composition on flame speed. The database is then used to develop a blend to match the flame speeds of a commercial aviation fuel, Jet A-1. Unlike previous investigations of flame propagation, in this study, numerical simulations are directly compared to velocity profile measurements in laminar stagnation flames to extrapolate to a condition of zero flame stretch. Numerical simulations of each experiment are obtained using the CHEMKIN-PRO software package and the JetSurF 2.0 mechanism with accurate specification of all necessary boundary conditions from experimental measurements. The advantage of this technique is that the extrapolation to the unstretched condition is independent of the how well the mechanism predicts reactivity. Therefore, JetSurF 2.0 was simultaneously validated for each of the surrogate fuels and blends in a previously unused manner. The mechanism showed relatively good agreement for the n-alkane and cycloalkane fuels for which it was optimized for, while consistently under predicted the reactivity of toluene. The compiled database of jet fuel surrogate components allowed for five different potential surrogate mixtures to be developed. Experimental results of these blends suggest that although jet fuel is a very complex mixture a simple surrogate mixture consisting of 73% n-decane and 27% toluene byvolume appropriately matches the flame speed of Jet A-1. Numerical results using JetSurF 2.0 suggest that a 63% n-decane and 37% toluene by volume blend is the most appropriate surrogate and this was used to extrapolate the experimental JetA-1 results and determine its laminar flame speed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".