Modeling of Heating and Evaporation of FACE I Gasoline Fuel and its Surrogates
Bibliographic record
Abstract
The US Department of Energy has formulated different gasoline fuels called ''Fuels for Advanced Combustion Engines (FACE)'' to standardize their compositions. FACE I is a low octane number gasoline fuel with research octane number (RON) of approximately 70. The detailed hydrocarbon analysis (DHA) of FACE I shows that it contains 33 components. This large number of components cannot be handled in fuel spray simulation where thousands of droplets are directly injected in combustion chamber. These droplets are to be heated, broken-up, collided and evaporated simultaneously. Heating and evaporation of single droplet FACE I fuel was investigated. The heating and evaporation model accounts for the effects of finite thermal conductivity, finite liquid diffusivity and recirculation inside the droplet, referred to as the effective thermal conductivity/effective diffusivity (ETC/ED) model. The temporal variations of the liquid mass fractions of the droplet components were used to characterize the evaporation process. Components with similar evaporation characteristics were merged together. A representative component was initially chosen based on the highest initial mass fraction. Three 6 components surrogates, Surrogate 1-3, that match evaporation characteristics of FACE I have been formulated without keeping same mass fractions of different hydrocarbon types. Another two surrogates (Surrogate 4 and 5) were considered keeping same hydrocarbon type concentrations. A distillation based surrogate that matches measured distillation profile was proposed. The calculated molar mass, hydrogen-to-carbon (H/C) ratio and RON of Surrogate 4 and distillation based one are close to those of FACE I.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".