Dynamics of soot surface growth and agglomeration by enclosed spray combustion of jet fuel
Bibliographic record
Abstract
Understanding the dynamics of soot formation and growth during combustion of jet fuel is essential for mitigation of aircraft engine emissions. Here, soot formation during enclosed spray combustion of jet fuel is investigated for its capacity to form soot with comparable characteristics to that from aircraft engines. For this, microscopy, scanning mobility particle, X-ray diffraction & Raman spectroscopy measurements and discrete element modeling (DEM) are employed along the flame centerline at various Effective eQuivalence Ratios (EQR). The DEM-derived mobility and primary particle size distribution dynamics are in excellent agreement with those measured at 5–63 cm height above the burner (HAB) for the measured temperature and soot volume fraction. At low EQR (1.46 and 1.59), soot surface growth stops at residence time, t = 4–7 ms, resulting in median soot primary particle diameters, d¯p, of ∼ 14 nm. At longer t (high HAB), agglomeration takes over increasing the median mobility diameter from 16 to 88 or 145 nm at EQR of 1.46 or 1.59, respectively, without altering d¯p and having the disorder over graphitic Raman band ratio, D/G = 0.9 ± 0.01 and a crystallite length, Lc = 1.24 ± 0.02 nm. In contrast, increasing EQR from 1.59 to 1.88, enhances soot surface growth, increases d¯p up to 23 nm and results in more graphitic soot having D/G = 0.8 ± 0.01 and Lc = 1.47 ± 0.01 nm. Furthermore, the D/G of soot is inversely proportional to its d¯p that is determined largely by surface growth.
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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".