Vapour Space Flammability Considerations for Gasoline Compression Ignition Vehicles Operating on “Dieseline” Blends.
Bibliographic record
Abstract
Gasoline Compression Ignition (GCI) has been identified as a technology which could give both high efficiency and relatively low engine-out emissions. The introduction of any new vehicle technology requires widespread availability of appropriate fuels. It would be ideal therefore if GCI vehicles were able to operate using the standard grade of gasoline that is available at the pump. However, in spite of recent progress, operation at idle and low loads still remains a formidable challenge, given the relatively low autoignition reactivity of conventional gasoline at these conditions. One conceivable solution would be to use both diesel and gasoline, either in separate tanks or blended as a single fuel (“dieseline”). However, with this latter option, a major concern for dieseline would be whether a flammable mixture could exist in the vapour space in the fuel tank. It is expected that in a practical scenario, the ambient temperature would generally exceed the Lower Flammability Limit (LFL) temperature of diesel/gasoline blends. A non-flammable vapour could still be assured, however, if the temperature in the fuel tank were above the Upper Flammability Limit (UFL) temperature. To investigate whether a flammable mixture could exist, the Upper Flammability Limit (UFL) temperatures of 12 distinct blends of gasoline, diesel, ethanol and ETBE were investigated in a special combustion chamber designed to mimic a vehicle tank, with a 5% fuel fill level to represent expected worst case conditions. The results should be considered as provisional, but suggest that under realistic assumptions of gasoline DVPE and ambient temperature, nonflammable blends can be achieved with mixtures containing a minimum of 25-40% gasoline. Derived Cetane Numbers up to 36-40 could be achieved, making the fuels suitable for Low Temperature Combustion.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".