Simulating the effect of post-flame agglomeration on the structure of soot
Bibliographic record
Abstract
Soot generated by non-premixed combustion is comprised of primary particles of non-uniform size. Typically, the variation in primary particle size is small within individual aggregates but large between the aggregates, that is, primary particle size is “externally mixed.” However, one occasionally sees an agglomerate composed of several sub-aggregates having distinctly different primary particle sizes. These hybrid agglomerates could arise from a secondary growth stage beyond the flame after the main in-flame formation processes. Here, we explore this hypothesis using a two-stage Langevin dynamics (LD) model inspired by our observations from electron microscopy. Our two-stage algorithm focuses on how post-flame agglomeration redistributes the morphological properties of aggregates. We use experimental information to enforce the more complex in-flame processes. We find that post-flame agglomeration shifts the correlation between the aggregate size and primary particle size rightward. This is because agglomeration does not change the overall population of primary particles but does move the primary particles to larger aggregates. The range of variations in primary particle size within individual aggregates is meanwhile reduced until eventually the larger hybrid super-agglomerates contain similar distributions of primary particle sizes, i.e., primary particle size becomes internally mixed. The fractal dimension of hybrid agglomerates is essentially the same as for the conventional DLCA aggregates, but the area shielding factor is reduced, that is, more of the total surface area is visible in projected images. Based on a simplified model of mobility, the simulations suggest that post-flame agglomeration reduces the effective density and mass-mobility exponent. The agglomerates produced by the two-stage LD model qualitatively resemble real soot observed in the experimental images and may be useful for detailed calculations of transport and optical properties.Copyright © 2024 American Association for Aerosol Research
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".