Investigation of soot suppression by ammonia addition to laminar ethylene flames at varying pressure
Bibliographic record
Abstract
Blending ammonia with hydrocarbon fuels is a promising pathway to expedite its use in different industries, as it might offer a good compromise between energy output and minimizing the emissions of both NOx and carbonaceous combustion products , including soot. This work describes the effects of pressure on soot formation in ethylene/ammonia laminar co-flow diffusion flames . A high-pressure chamber was used to study the flames at pressures between 1 to 7 atm, and ammonia addition mole fractions between 0 and 50% (38% in mass fraction). Soot volume fraction and soot temperature measurements using a Spectral Soot Emissions technique show that adding a 50% mole fraction of ammonia at atmospheric pressure reduces soot concentration below the detection limit of the technique and between 70% and 80% for all the remaining studied pressures; that is, the ammonia soot suppression characteristics are demonstrated when increasing pressure. Maximum soot yield and total volumetrically integrated soot volume fraction results are investigated to reveal the contrasting effects of pressure and ammonia addition on the flame structure and the soot formation mechanisms. The results showcase the potential of NH 3 in high-pressure applications when mixed with hydrocarbon fuels to reduce carbon emissions .
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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".