Vapour Space Flammability Considerations for Gasoline Compression Ignition Vehicles Operating on “Dieseline” Blends.
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">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.</div></div>
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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 | 0.002 |
| 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".