Ignition and Oxidation of 50/50 Butane Isomer Blends
Bibliographic record
Abstract
One of the alkanes found within gaseous fuel blends of interest to gas turbine applications is butane. There are two structural isomers of butane, normal butane and isobutane, and the combustion characteristics of either isomer are not well known. Of particular interest to this work are mixtures of n-butane and isobutane. A shock-tube experiment was performed to produce important ignition-delay-time data for these binary butane isomer mixtures, which are not currently well studied, with emphasis on 50-50 blends of the two isomers. These data represent the most extensive shock-tube results to date for mixtures of n-butane and isobutane. Ignition within the shock tube was determined from the sharp pressure rise measured at the end wall, which is characteristic of such exothermic reactions. Both experimental and kinetics modeling results are presented for a wide range of stoichiometries (ϕ=0.3−2.0), temperatures (1056–1598 K), and pressures (1–21 atm). The results of this work serve as a validation for the current chemical kinetics model. Correlations in the form of Arrhenius-type expressions are presented, which agree well with both the experimental results and the kinetics modeling. The results of an ignition-delay-time sensitivity analysis are provided, and key reactions are identified. The data from this study are compared with the modeling results of 100% normal butane and 100% isobutane. The 50/50 mixture of n-butane and isobutane was shown to be more readily ignitable than 100% isobutane but reacts slower than 100% n-butane only for the richer mixtures. There was little difference in ignition time between the lean mixtures.
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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 | 0.000 | 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) | 0.001 | 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".