A Four Component Skeletal Model for the Analysis of Jet Fuel Surrogate Combustion
Bibliographic record
Abstract
A skeletal chemical kinetic model for jet fuel combustion, comprising four representative fuel components, is presented. The sub model for the three components, toluene, methyl cyclohexane (MCH) and n-dodecane, is deduced from a detailed model for jet fuel surrogate proposed by Wang et al. [Wang et al., 2010]. The reduction is based on a species sensitivity approach, herein referred to as Alternate Species Elimination (ASE). The sub model for the fourth component, iso-octane, is established through semi-detailed kinetic modeling, considering existing reactions and species of the smaller hydrocarbon systems as well as species and reactions pertinent to the n-dodecane system. The performance of the resulting model is assessed by comparing predictions of ignition delay times and laminar burning velocities with those of the detailed model. It is shown that the skeletal model retains the predictive ability of the detailed model with respect to the three components, n-dodecane, MCH and toluene. The complementary iso-octane sub model is also found to reasonably predict high-temperature ignition delay times and laminar burning velocities. The four component skeletal model is tested against shock tube ignition data and laminar burning velocities of jet fuel surrogates. It is observed that high-temperature ignition is fairly well predicted while low-temperature ignition delay times are longer than experimentally observed. While the predictions of laminar burning velocities of atmospheric flames of jet fuels at 400 K are reasonable, slower flames are predicted at higher temperatures. The proposed skeletal model has 192 species and 1291 reactions, compared to the detailed multi-component model, with 348 species and 2163 elementary reactions, albeit without iso-octane. This results in improvement in the associated computational costs for combustion analysis. Further development of the skeletal model is needed to improve its prediction ability over a wider range of combustion properties and thermodynamic conditions.
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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) | 0.000 | 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) | 0.000 | 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".