Auto-ignition characteristics of diesel fuel in an O<sub>2</sub>–CO<sub>2</sub> mixture
Bibliographic record
Abstract
To study diesel fuel auto-ignition in an O2–CO2 mixture, a TZ (temperature zone) model is proposed. The effect of O2 and CO2 on reaction rate is considered. The relationship between temperature and ignition delay time is obtained. Different reduced mechanisms based on steady-state assumptions are applied in three temperature zones (T ≤ 800 K, 800 K < T ≤ 1100 K, T > 1100 K). The TZ model is coupled to KIVA-3V code for simulation calculations. To support the simulations, a constant-volume combustion bomb test bench is set up to visualize diesel fuel auto-ignition in air (21%O2–79%N2), a 53%O2–47%CO2 mixture, and a 61%O2–39%CO2 mixture. Ignition delay time and the flame image in these three conditions are compared and analyzed. Then the flame temperature contour and the flame lift-off length in a 53%O2–47%CO2 mixture and a 61%O2–39%CO2 mixture are analyzed. The results show that diesel fuel auto-ignition can be achieved in the tested O2–CO2 mixture. The TZ model can predict the auto-ignition characteristics of diesel fuel in a 53%O2–47%CO2 mixture and a 61%O2–39%CO2, with errors of 12% and 10%, respectively. In these two conditions, the ignition delay time and flame lift-off length are shorter than they are in air.
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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".