Numerical study of the effect of <scp> CO <sub>2</sub> </scp> / <scp> H <sub>2</sub> O </scp> dilution on the laminar burning velocity of methane/air flames under elevated initial temperature and pressure
Bibliographic record
Abstract
Abstract Diluents have an essential effect during combustion. Discovering the influence of CO 2 and H 2 O as diluents on laminar burning velocity (LBV) is helpful for combustion control and optimization. In this study, CH 4 /air/CO 2 /H 2 O mixtures were investigated and validated using the FFCM‐Mech 1.0 over extensive boundary conditions. The chemical effects of the diluents CO 2 and H 2 O were separated using a decoupling method. It was found that an increase in initial temperature promotes the chemical effects, while an increase in initial pressure does the opposite. In addition, the inhibiting effect of CO 2 on LBV is stronger than that of H 2 O. Sensitivity, mole fraction, and rate of production (ROP) analyses were used to reveal that the sum of the chemical effects of adding CO 2 and H 2 O separately was greater than the chemical effects of adding equal amounts of CO 2 and H 2 O simultaneously. This paper not only investigates the effect of CO 2 and H 2 O on the LBV under wide boundary conditions, but also offers a valuable guide for studying the operating conditions and intensity settings of exhaust gas recirculation (EGR) and theoretical guidance for further research on the combination of EGR and in‐cylinder water injection technology.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".