Ignition Delay Correlation for Predicting Autoignition of a Toluene Reference Fuel Blend in Spark Ignition Engines
Bibliographic record
Abstract
An ignition delay correlation was developed for a toluene reference fuel (TRF) blend that is representative of automotive gasoline fuels exhibiting two-stage ignition. Ignition delay times for the autoignition of a TRF 91 blend with an antiknock index of 91 were predicted through extensive chemical kinetic modeling in CHEMKIN for a constant volume reactor. The development of the correlation involved determining nonlinear least squares curve fits for these ignition delay predictions corresponding to different inlet pressures and temperatures, a number of fuel-air equivalence ratios, and a range of exhaust gas recirculation (EGR) rates. In addition to NO control, EGR is increasingly being utilized for managing combustion phasing in spark ignition (SI) engines to mitigate knock. Therefore, along with other operating parameters, the effects of EGR on autoignition have been incorporated in the correlation to address the need for predicting ignition delay in SI engines operating with EGR. Unlike the ignition delay expressions available in literature for primary reference fuel blends, the correlation developed in the present study can predict ignition delay for a TRF blend, a more realistic gasoline surrogate.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".