Flame Speed and Vapor Pressure of Biojet Fuel Blends
Bibliographic record
Abstract
An understanding of the fundamental combustion properties of alternative fuels is essential for their adoption as replacements for non-renewable sources. In this study, three different biojet fuel mixtures are directly compared to conventional Jet A-1 on the basis of laminar flame speed and vapor pressure. The biofuel is derived from camelina oil and hydrotreated to ensure consistent elemental composition with conventional aviation fuel, yielding a bioderived synthetic paraffinic kerosene (Bio-SPK). Two considered blends are biofuel and Jet A-1 mixtures, while the third is a 90% Bio-SPK mixture with 10% aromatic additives. Premixed flame speed measurements are conducted at an unburned temperature of 400K and atmospheric pressure using a jet-wall stagnation flame apparatus. Since the laminar flame speed cannot be studied experimentally, a strained (or reference) flame speed is used as the basis for the initial comparison. Only by using an appropriate surrogate fuel were the reference flame speed measurements extrapolated to zero flame strain, accomplished using a direct comparison of simulations to experiments. This method has been previously shown to yield results consistent with non-linear extrapolations. Vapor pressure measurements of the biojet blends are also made from 25 to 200°C using an isoteniscope. Finally, the biojet blends are compared to the Jet A-1 benchmark on the basis of laminar flame speed at different equivalence ratios, as well as on the basis of vapor pressure over a wide temperature range, and the suitability of a binary laminar flame speed surrogate for these biojet fuels is discussed.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".