Investigations on soot and flow field characteristics of blended liquid and gaseous fuels in turbulent swirl-stabilized non-premixed flames
Bibliographic record
Abstract
Combustion of fossil/carbon-based fuels in both ground and aviation-based transportation devices produce emissions such as NOx, CO2 and soot. Among these, soot formation is a complex process and their characteristics have not been understood completely. Hence, it has been the subject of recent studies in an effort to reduce its footprint in atmospheric pollution. The current work explores the possibility of adding different fuel additives to existing carbon-based fuels in aviation-type gas turbine combustors to understand their effect on soot formation. This study involves understanding the various hydrodynamic and chemical effects on soot formation through controlled lab-scale experiments with well-defined boundary conditions in both liquid and gaseous swirl-stabilized turbulent flames. Particle image velocimetry, laser diffraction and laser induced incandescence are utilized to experimentally characterize the flow-field, spray and soot characteristics respectively within the combustor. Various biofuels and oxygenated compounds are blended in small amounts to base Jet A-1 fuel in liquid spray combustors, whereas hydrogen/helium is used as an additive to ethylene base fuel in gaseous combustor. The overall effectiveness of a certain fuel additive along with the effect of various blending ratios, flow rates and spray characteristics on soot in different combustor configurations are discussed in detail. The changes in spray characteristics was noticeable for lower alcohols, but diminished with higher alcohols as their physical properties do not deviate largely from neat Jet A-1. The measurements revealed that even though the hydrodynamic effects such as flow-field and turbulence can influence the soot formation regions, spatial soot distribution and soot intermittency, the global changes in the magnitudes of soot volume fractions were mostly affected by local fuel chemistry. Higher alcohols were found to be highly effective in reducing soot concentrations when added to neat Jet A-1, whereas addition of hydrogen in small concentrations to ethylene resulted in an increase in soot loading, opposite to that observed in atmospheric laminar flames. In addition to this, high speed planar measurements in gaseous flames revealed that apart from the effect of swirl, bulk flow unsteadiness and local flow fluctuations contributed immensely to the growth and transport of soot from formation to oxidation regions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".