Experimental and numerical study of coflow laminar DME/air and methane/air diffusion flames
Bibliographic record
Abstract
This paper presents a numerical and experimental study on the structure and soot formation of atmospheric pressure coflow laminar DME/air and CH4/air diffusion flames. Detailed thermal and transport properties were used in the simulation. The combustion chemistry was modeled using a reduced mechanism for the DME flame and GRI Mech3.0 for the methane frame. A semi-empirical soot formation model was used. In the experiments, the flames were generated using the standard NRC coflow laminar flame burner. Un the condition of constant carbon flow rate, the visible flame height of the DME flame is only about two thirds of that of the CH4 flame. For the methane flame, the soot field (distribution, visible flame height, and peak volume fraction) predicted numerically is in reasonably good agreement with the present experimental observation and experimental data in the literature. However, the same simplified soot model fails miserably when applied to the DME flame. In this flame, the predicted soot volume fractions in the centerline region are about two orders of magnitude larger than the values measured using a laser-induced incandescence technique, suggesting that the soot volume fractions in flames fueled with DME cannot be reliably modeled using the simplified soot model developed for conventional hydrocarbon flames without modification.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".