Aerodynamic and fuel dilution effects on non-premixed \ngas jet flames \n
Bibliographic record
Abstract
This study examines the changes in the flame structure and emissions from \nlaboratory-scale flares over a wide range of test conditions. \nIn the initial study, the experimental measurements examined the effect of \nvarying the fuel jet velocity on the flame temperature, flame structure, and the inflame \nand post-flame composition of species in methane flames. The test conditions \ninvolved laboratory-scale flares in the attached and lifted regimes under laminar, \ntransitional and turbulent conditions. The results show that while an increase in the \njet velocity leads to an increase in EINOx, this also leads to a decrease in EICO, and \nsimilarly, EICO decreases with decreasing flame luminosity and sooting propensity. \nThe second study examined the effect of CO2 dilution on methane jet flames \nwhere CO2, which was used as a diluent, was injected into the fuel-jet stream. The \ndilution-induced extinction was achieved by fixing the fuel flow rate, while varying \nthe diluent mole fraction. The effect of the changes in the flame length, lift-off \nheight, and in the emissions due to this dilution was studied. Amongst other \nfindings, this study shows that CO2 is effective in reducing the EINOx in the postflame \nregion of methane jet flames at Reynolds number ranging from 1584 to \n14254, and that soot formation is suppressed at higher diluent concentrations in the \njet flame. \nThe final study involved the characterisation and the comparison of the inflame \ncomposition of major species and the post-flame soot and pollutant emissions \ngenerated from the combustion of methane and propane flames. The results show \nthat the dilution of the fuel stream with CO2 reduces the size of soot aggregates in \npropane flames and that the soot emission factor decreases at increased diluent \nconcentrations. In addition, for the same test conditions utilised in this study, the \nEICO and EINOx are higher in methane flames than in propane flames.
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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) | 0.000 | 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".